From 60deff46f045dd2916b4cff3e53565e0a95b7c3c Mon Sep 17 00:00:00 2001 From: Polina Binder Date: Thu, 21 May 2026 00:41:33 +0000 Subject: [PATCH 01/12] evo2_megatron: load Savanna HF checkpoints with weights_only=False MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit torch 2.6 changed the default of `weights_only` to True. The Savanna checkpoint pickle includes numpy globals (`numpy.core.multiarray._reconstruct`), which the safer loader rejects. The converter then exits 0 with no output written and the error gets buried in stderr — silent failure. The Savanna repos under arcinstitute/* are trusted sources, so load with weights_only=False. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../src/bionemo/evo2/utils/checkpoint/savanna_to_mbridge.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bionemo-recipes/recipes/evo2_megatron/src/bionemo/evo2/utils/checkpoint/savanna_to_mbridge.py b/bionemo-recipes/recipes/evo2_megatron/src/bionemo/evo2/utils/checkpoint/savanna_to_mbridge.py index 811b07153e..156ce530b5 100644 --- a/bionemo-recipes/recipes/evo2_megatron/src/bionemo/evo2/utils/checkpoint/savanna_to_mbridge.py +++ b/bionemo-recipes/recipes/evo2_megatron/src/bionemo/evo2/utils/checkpoint/savanna_to_mbridge.py @@ -135,7 +135,7 @@ def load_savanna_state_dict(path: Path) -> dict[str, torch.Tensor]: Returns: Flat state dict with keys like 'sequential.{i}.xxx'. """ - raw = torch.load(str(path), map_location="cpu", weights_only=True, mmap=True) + raw = torch.load(str(path), map_location="cpu", weights_only=False, mmap=True) if "module" in raw: raw = raw["module"] From b640f66ab62600ccf5da05f8ced1bb7f2797534f Mon Sep 17 00:00:00 2001 From: Polina Binder Date: Thu, 21 May 2026 00:43:15 +0000 Subject: [PATCH 02/12] interpretability/sae: add Evo2 1B SAE recipe Mirrors the existing esm2 / codonfm SAE recipes. Pipeline: chunk -> convert (Savanna->MBridge) -> predict_evo2 -> pt_to_parquet -> train Differences from esm2/codonfm are forced by Evo2 specifics: - Hyena/Megatron-Core model, no HF AutoModel path => reuses the existing `predict_evo2` CLI for inference instead of writing a custom extract.py - `pt_to_parquet.py` shim bridges predict_evo2's .pt output to the universal `sae.activation_store` parquet contract - `chunk_fasta.py` preprocessor keeps inputs within the model's trained context length (8192 bp for 1B); Hyena fftconv OOMs on long sequences even at micro-batch=1 - `train.py` is the same as codonfm's, copied verbatim per bionemo-recipes' KISS-over-DRY convention Validated end-to-end on 100 organelle sequences (Evo2 1B layer 12): loss 0.67 -> 0.045, FVU 0.90 -> 0.10, var_exp 0.10 -> 0.90, 2m14s wall. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../recipes/evo2/README.md | 30 ++ .../recipes/evo2/pyproject.toml | 24 ++ .../recipes/evo2/scripts/1b.sh | 116 +++++++ .../recipes/evo2/scripts/chunk_fasta.py | 73 ++++ .../recipes/evo2/scripts/pt_to_parquet.py | 65 ++++ .../recipes/evo2/scripts/train.py | 321 ++++++++++++++++++ .../recipes/evo2/src/evo2_sae/__init__.py | 16 + 7 files changed, 645 insertions(+) create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/README.md create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/pyproject.toml create mode 100755 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/1b.sh create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/chunk_fasta.py create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/pt_to_parquet.py create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/train.py create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/src/evo2_sae/__init__.py diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/README.md b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/README.md new file mode 100644 index 0000000000..ad749dbedb --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/README.md @@ -0,0 +1,30 @@ +# Evo2 SAE Recipe + +Train a sparse autoencoder on Evo2 (DNA language model) residual-stream activations. + +Pipeline: + +``` +HF Savanna ckpt --convert--> MBridge ckpt + | + predict_evo2 --embedding-layer N (FASTA in, .pt out) + | + pt_to_parquet shim (.pt -> ActivationStore parquet shards) + | + train.py (TopK SAE) +``` + +The eval / dashboard stage from the esm2 recipe is intentionally not ported in v1. + +## Quick start (1B model, single GPU) + +```bash +bash scripts/1b.sh +``` + +This will: + +1. Convert `arcinstitute/savanna_evo2_1b_base` to MBridge format +2. Run `predict_evo2` on the OpenGenome2 organelle FASTA, extracting layer-12 embeddings +3. Convert the .pt outputs to parquet shards +4. Train a TopK SAE (expansion=8, k=32) diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/pyproject.toml b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/pyproject.toml new file mode 100644 index 0000000000..26eff6b55c --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/pyproject.toml @@ -0,0 +1,24 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "evo2-sae" +version = "0.1.0" +description = "Sparse Autoencoders for the Evo2 DNA language model" +readme = "README.md" +requires-python = ">=3.10" + +dependencies = [ + "sae", + "torch>=2.0", + "numpy>=1.20", + "tqdm>=4.60", + "pyarrow>=10.0", +] + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.uv.sources] +sae = { workspace = true } diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/1b.sh b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/1b.sh new file mode 100755 index 0000000000..d499b4f365 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/1b.sh @@ -0,0 +1,116 @@ +#!/bin/bash +# Evo2 1B SAE pipeline: convert -> predict_evo2 -> pt_to_parquet -> train. +# +# Assumes: +# - bionemo-recipes/recipes/evo2_megatron has been built (.ci_build.sh) and +# its .venv is active, providing predict_evo2 + evo2_convert_savanna_to_mbridge. +# - The sae workspace package is importable in that same venv. +# - HF_TOKEN is set if Savanna checkpoint repo is gated. +# +# Override any of these by exporting before invocation. + +set -euo pipefail + +EVO2_MEGATRON_DIR="${EVO2_MEGATRON_DIR:-/workspace/bionemo-framework/bionemo-recipes/recipes/evo2_megatron}" +RECIPE_DIR="$(cd "$(dirname "$0")/.." && pwd)" + +MODEL="${MODEL:-arcinstitute/savanna_evo2_1b_base}" +MODEL_SIZE="${MODEL_SIZE:-evo2_1b_base}" +LAYER="${LAYER:-12}" +# Trained context length. 1B = 8192. Bump for 7B/40B (context-extended). +CHUNK_BP="${CHUNK_BP:-8192}" + +FASTA="${FASTA:-/data/interp/evo2/OpenGenome2/fasta/organelles/organelle_sequences.fasta.gz}" +WORK_ROOT="${WORK_ROOT:-/data/interp/evo2}" + +CKPT_DIR="${WORK_ROOT}/checkpoints/${MODEL_SIZE}_mbridge" +PREDICT_DIR="${WORK_ROOT}/activations/${MODEL_SIZE}_layer${LAYER}_pt" +PARQUET_DIR="${WORK_ROOT}/activations/${MODEL_SIZE}_layer${LAYER}_parquet" +OUTPUT_DIR="${WORK_ROOT}/sae/${MODEL_SIZE}_layer${LAYER}" + +source "${EVO2_MEGATRON_DIR}/.venv/bin/activate" + +echo "============================================================" +echo "STEP 0: Chunk FASTA to <=${CHUNK_BP} bp (model trained context)" +echo "============================================================" +# chunk_fasta.py reads .gz directly and writes plain .fasta; no separate gunzip needed. +INPUT_STEM="$(basename "$FASTA")" +INPUT_STEM="${INPUT_STEM%.gz}" +INPUT_STEM="${INPUT_STEM%.fasta}" +CHUNKED_FASTA="${WORK_ROOT}/scratch/${INPUT_STEM}_chunked${CHUNK_BP}.fasta" +if [[ -f "$CHUNKED_FASTA" ]]; then + echo "Reusing existing chunked FASTA: $CHUNKED_FASTA" +else + python "${RECIPE_DIR}/scripts/chunk_fasta.py" \ + --input "$FASTA" \ + --output "$CHUNKED_FASTA" \ + --window "$CHUNK_BP" +fi +FASTA="$CHUNKED_FASTA" + +echo "============================================================" +echo "STEP 1: Convert Savanna -> MBridge" +echo "============================================================" +if [[ ! -f "${CKPT_DIR}/latest_checkpointed_iteration.txt" ]]; then + evo2_convert_savanna_to_mbridge \ + --savanna-ckpt-path "$MODEL" \ + --mbridge-ckpt-dir "$CKPT_DIR" \ + --model-size "$MODEL_SIZE" \ + --tokenizer-path "${EVO2_MEGATRON_DIR}/tokenizers/nucleotide_fast_tokenizer_512" +else + echo "Reusing existing checkpoint at $CKPT_DIR" +fi + +echo "============================================================" +echo "STEP 2: Extract layer-${LAYER} embeddings (predict_evo2)" +echo "============================================================" +mkdir -p "$PREDICT_DIR" +if compgen -G "${PREDICT_DIR}/predictions__*.pt" > /dev/null; then + echo "Reusing existing .pt files in $PREDICT_DIR" +else + predict_evo2 \ + --fasta "$FASTA" \ + --ckpt-dir "$CKPT_DIR" \ + --output-dir "$PREDICT_DIR" \ + --embedding-layer "$LAYER" \ + --micro-batch-size 1 \ + --devices 1 \ + --write-interval batch +fi + +echo "============================================================" +echo "STEP 3: Convert .pt -> parquet ActivationStore" +echo "============================================================" +if [[ -f "${PARQUET_DIR}/metadata.json" ]]; then + echo "Reusing existing parquet shards at $PARQUET_DIR" +else + python "${RECIPE_DIR}/scripts/pt_to_parquet.py" \ + --predict-dir "$PREDICT_DIR" \ + --output "$PARQUET_DIR" \ + --model-name "$MODEL" \ + --layer "$LAYER" +fi + +echo "============================================================" +echo "STEP 4: Train TopK SAE" +echo "============================================================" +python "${RECIPE_DIR}/scripts/train.py" \ + --cache-dir "$PARQUET_DIR" \ + --model-path "$MODEL" \ + --layer "$LAYER" \ + --model-type topk \ + --expansion-factor 8 --top-k 32 \ + --auxk 64 --auxk-coef 0.03125 \ + --init-pre-bias \ + --n-epochs 3 \ + --batch-size 4096 \ + --lr 3e-4 \ + --log-interval 50 \ + --no-wandb \ + --output-dir "$OUTPUT_DIR" \ + --checkpoint-dir "${OUTPUT_DIR}/checkpoints" \ + --checkpoint-steps 999999 + +echo "============================================================" +echo "DONE: SAE checkpoint at ${OUTPUT_DIR}/checkpoints/checkpoint_final.pt" +echo "============================================================" diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/chunk_fasta.py b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/chunk_fasta.py new file mode 100644 index 0000000000..55b26cad30 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/chunk_fasta.py @@ -0,0 +1,73 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-Apache2 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Chunk a FASTA into <=N-bp windows so predict_evo2 stays inside the model's trained context. + +Evo2 1B was trained with seq_length=8192; longer inputs OOM in the Hyena +fftconv path (intermediates scale super-linearly with L). For 7B/40B raise +--window to whatever those checkpoints were context-extended to. + +Non-overlapping windows by default. Each chunk gets a header of the form +">{orig_id}:{start}-{end}" so downstream parquet can be back-mapped. +""" + +import argparse +import gzip +from pathlib import Path + + +def parse_fasta(path: Path): + """Yield (seq_id, sequence) tuples from a FASTA file (transparently handles .gz).""" + opener = gzip.open if path.suffix == ".gz" else open + seq_id, parts = None, [] + with opener(path, "rt") as f: + for line in f: + line = line.rstrip() + if line.startswith(">"): + if seq_id is not None: + yield seq_id, "".join(parts) + seq_id = line[1:].split()[0] + parts = [] + else: + parts.append(line) + if seq_id is not None: + yield seq_id, "".join(parts) + + +def main(): + """Read input FASTA, write non-overlapping <=window-bp chunks to output FASTA.""" + p = argparse.ArgumentParser() + p.add_argument("--input", type=Path, required=True) + p.add_argument("--output", type=Path, required=True) + p.add_argument("--window", type=int, default=8192) + args = p.parse_args() + + n_in = n_out = bp_out = 0 + args.output.parent.mkdir(parents=True, exist_ok=True) + with open(args.output, "w") as out: + for seq_id, seq in parse_fasta(args.input): + n_in += 1 + for start in range(0, len(seq), args.window): + end = min(start + args.window, len(seq)) + chunk = seq[start:end] + out.write(f">{seq_id}:{start}-{end}\n{chunk}\n") + n_out += 1 + bp_out += len(chunk) + + print(f"Chunked {n_in} sequences -> {n_out} chunks ({bp_out:,} bp) at window={args.window}") + + +if __name__ == "__main__": + main() diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/pt_to_parquet.py b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/pt_to_parquet.py new file mode 100644 index 0000000000..6a182b575d --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/pt_to_parquet.py @@ -0,0 +1,65 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-Apache2 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Convert predict_evo2 .pt outputs to SAE ActivationStore parquet shards. + +predict_evo2 with --embedding-layer writes dicts of: + hidden_embeddings: [B, S, H] (bf16) + pad_mask: [B, S] (1 = valid token, 0 = padding) + seq_idx, tokens: metadata, ignored here + +We read each file, mask out padding, flatten to [N_tokens, H], and append +to an ActivationStore so train.py's load_activations() can consume it. +""" + +import argparse +import json +from pathlib import Path + +import torch +from sae.activation_store import ActivationStore, ActivationStoreConfig +from tqdm import tqdm + + +def main(): + """Walk predict_evo2 .pt files, mask padding, and write to an ActivationStore.""" + p = argparse.ArgumentParser() + p.add_argument("--predict-dir", type=Path, required=True, help="Dir containing predictions__*.pt") + p.add_argument("--output", type=Path, required=True, help="ActivationStore output dir") + p.add_argument("--model-name", type=str, required=True, help="Stamped into metadata.json") + p.add_argument("--layer", type=int, required=True, help="Stamped into metadata.json") + p.add_argument("--shard-size", type=int, default=100_000) + args = p.parse_args() + + pt_files = sorted(args.predict_dir.rglob("predictions__*.pt")) + if not pt_files: + raise FileNotFoundError(f"No predictions__*.pt under {args.predict_dir}") + + store = ActivationStore(args.output, ActivationStoreConfig(shard_size=args.shard_size)) + n_sequences = 0 + for pt in tqdm(pt_files, desc="pt->parquet"): + d = torch.load(pt, map_location="cpu", weights_only=False) + hidden = d["hidden_embeddings"] + mask = d["pad_mask"].bool() + flat = hidden[mask].float() + store.append(flat) + n_sequences += hidden.shape[0] + + store.finalize(metadata={"model_name": args.model_name, "layer": args.layer, "n_sequences": n_sequences}) + print(json.dumps(store.metadata, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/train.py b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/train.py new file mode 100644 index 0000000000..19355822ae --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/train.py @@ -0,0 +1,321 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-Apache2 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Step 2: Train SAE from cached CodonFM activations. + +Loads pre-extracted activations from an ActivationStore cache directory +and trains a Sparse Autoencoder. Requires extract.py to have been run first. + +Single-GPU: + python scripts/train.py \ + --cache-dir .cache/activations/encodon_1b_layer-2 \ + --model-path path/to/encodon_1b --layer -2 \ + --expansion-factor 8 --top-k 32 --batch-size 4096 --n-epochs 3 + +Multi-GPU DDP: + torchrun --nproc_per_node=4 scripts/train.py \ + --cache-dir .cache/activations/encodon_1b_layer-2 \ + --model-path path/to/encodon_1b --layer -2 \ + --expansion-factor 8 --top-k 32 --batch-size 4096 --n-epochs 3 \ + --dp-size 4 +""" + +import argparse +import os +from pathlib import Path + +import numpy as np +import torch +from sae.activation_store import load_activations +from sae.architectures import ReLUSAE, TopKSAE +from sae.perf_logger import PerfLogger +from sae.training import ParallelConfig, Trainer, TrainingConfig, WandbConfig +from sae.utils import get_device, set_seed + + +def parse_args(): # noqa: D103 + p = argparse.ArgumentParser( + description="Train SAE from cached CodonFM activations", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + + # Required + p.add_argument("--cache-dir", type=str, required=True, help="Path to activation cache (from extract.py)") + p.add_argument("--model-path", type=str, required=True, help="Encodon model path (for cache validation)") + p.add_argument("--layer", type=int, required=True, help="Layer index (for cache validation)") + + # SAE architecture + sae_group = p.add_argument_group("SAE model") + sae_group.add_argument("--model-type", type=str, default="topk", choices=["topk", "relu"]) + sae_group.add_argument("--expansion-factor", type=int, default=8) + sae_group.add_argument("--top-k", type=int, default=32) + sae_group.add_argument("--normalize-input", action=argparse.BooleanOptionalAction, default=False) + sae_group.add_argument("--auxk", type=int, default=None) + sae_group.add_argument("--auxk-coef", type=float, default=1 / 32) + sae_group.add_argument("--dead-tokens-threshold", type=int, default=10_000_000) + sae_group.add_argument("--init-pre-bias", action=argparse.BooleanOptionalAction, default=False) + sae_group.add_argument("--l1-coeff", type=float, default=1e-2, help="L1 coefficient (relu only)") + + # Training + train_group = p.add_argument_group("Training") + train_group.add_argument("--lr", type=float, default=3e-4) + train_group.add_argument("--n-epochs", type=int, default=3) + train_group.add_argument("--batch-size", type=int, default=4096) + train_group.add_argument("--log-interval", type=int, default=50) + train_group.add_argument("--shuffle", action=argparse.BooleanOptionalAction, default=True) + train_group.add_argument("--num-workers", type=int, default=0) + train_group.add_argument("--pin-memory", action=argparse.BooleanOptionalAction, default=False) + train_group.add_argument("--max-grad-norm", type=float, default=None) + train_group.add_argument("--lr-scale-with-latents", action=argparse.BooleanOptionalAction, default=False) + train_group.add_argument("--lr-reference-hidden-dim", type=int, default=2048) + train_group.add_argument("--warmup-steps", type=int, default=0, help="Linear LR warmup steps") + train_group.add_argument( + "--lr-schedule", + type=str, + default="constant", + choices=["constant", "cosine", "linear"], + help="LR schedule after warmup", + ) + train_group.add_argument("--lr-min", type=float, default=0.0, help="Minimum LR for decay schedules") + train_group.add_argument( + "--lr-decay-steps", + type=int, + default=None, + help="Total steps for LR decay (None = full training)", + ) + + # W&B + wb_group = p.add_argument_group("Weights & Biases") + wb_group.add_argument("--wandb", action=argparse.BooleanOptionalAction, default=False, dest="wandb_enabled") + wb_group.add_argument("--wandb-project", type=str, default="sae_codonfm_recipe") + wb_group.add_argument("--wandb-run-name", type=str, default=None) + wb_group.add_argument("--wandb-group", type=str, default=None) + wb_group.add_argument("--wandb-job-type", type=str, default=None) + + # Checkpointing + ckpt_group = p.add_argument_group("Checkpointing") + ckpt_group.add_argument("--checkpoint-dir", type=str, default=None) + ckpt_group.add_argument("--checkpoint-steps", type=int, default=None) + ckpt_group.add_argument("--resume-from", type=str, default=None) + + # Infrastructure + p.add_argument("--dp-size", type=int, default=1) + p.add_argument("--output-dir", type=str, default="./outputs") + p.add_argument("--seed", type=int, default=42) + p.add_argument("--device", type=str, default=None) + p.add_argument( + "--num-sequences", + type=int, + default=None, + help="Subset cached activations to this many sequences' worth of shards", + ) + + return p.parse_args() + + +def build_sae(args, input_dim: int) -> torch.nn.Module: # noqa: D103 + hidden_dim = input_dim * args.expansion_factor + + if args.model_type == "topk": + return TopKSAE( + input_dim=input_dim, + hidden_dim=hidden_dim, + top_k=args.top_k, + normalize_input=args.normalize_input, + auxk=args.auxk, + auxk_coef=args.auxk_coef, + dead_tokens_threshold=args.dead_tokens_threshold, + ) + elif args.model_type == "relu": + return ReLUSAE( + input_dim=input_dim, + hidden_dim=hidden_dim, + l1_coeff=args.l1_coeff, + ) + else: + raise ValueError(f"Unknown model type: {args.model_type}") + + +def build_training_config(args, device: str) -> TrainingConfig: # noqa: D103 + return TrainingConfig( + lr=args.lr, + n_epochs=args.n_epochs, + batch_size=args.batch_size, + device=device, + log_interval=args.log_interval, + shuffle=args.shuffle, + num_workers=args.num_workers, + pin_memory=args.pin_memory, + checkpoint_dir=args.checkpoint_dir, + checkpoint_steps=args.checkpoint_steps, + lr_scale_with_latents=args.lr_scale_with_latents, + lr_reference_hidden_dim=args.lr_reference_hidden_dim, + warmup_steps=args.warmup_steps, + max_grad_norm=args.max_grad_norm, + lr_schedule=args.lr_schedule, + lr_min=args.lr_min, + lr_decay_steps=args.lr_decay_steps, + ) + + +def build_wandb_config(args) -> WandbConfig: # noqa: D103 + return WandbConfig( + enabled=args.wandb_enabled, + project=args.wandb_project, + run_name=args.wandb_run_name, + group=args.wandb_group, + job_type=args.wandb_job_type, + config=vars(args), + ) + + +def build_parallel_config(args) -> ParallelConfig: # noqa: D103 + return ParallelConfig(dp_size=args.dp_size) + + +def main(): # noqa: D103 + args = parse_args() + + set_seed(args.seed) + device = args.device or get_device() + print(f"Using device: {device}") + print(f"Config: {vars(args)}") + + # Load cached activations + cache_path = Path(args.cache_dir) + if not (cache_path / "metadata.json").exists(): + raise FileNotFoundError(f"No cache found at {cache_path}. Run extract.py first.") + + store = load_activations(cache_path) + meta = store.metadata + + # Validate cache matches config + cached_model = meta.get("model_path", meta.get("model_name", "")) + if cached_model and cached_model != args.model_path: + print(f"WARNING: Cache model '{cached_model}' != '{args.model_path}'") + if meta.get("layer") != args.layer: + raise ValueError(f"Cache layer mismatch: {meta['layer']} vs {args.layer}") + + # Compute subsetting + cached_sequences = meta.get("n_sequences", None) + max_shards = None + if args.num_sequences and cached_sequences and args.num_sequences < cached_sequences: + keep_ratio = args.num_sequences / cached_sequences + max_shards = max(1, int(np.ceil(keep_ratio * meta["n_shards"]))) + print( + f"Subsetting: {args.num_sequences}/{cached_sequences} sequences " + f"-> using {max_shards}/{meta['n_shards']} shards (~{keep_ratio:.1%})" + ) + + # Estimate memory + n_shards_to_use = max_shards or meta["n_shards"] + shard_size = meta.get("shard_size", 100_000) + est_tokens = n_shards_to_use * shard_size + est_gb = est_tokens * meta["hidden_dim"] * 4 / (1024**3) + use_streaming = est_gb > 50 + + input_dim = meta["hidden_dim"] + sae = build_sae(args, input_dim) + print(f"SAE: {args.model_type}, input_dim={input_dim}, hidden_dim={sae.hidden_dim}") + + # Initialize pre_bias + if args.init_pre_bias and hasattr(sae, "init_pre_bias_from_data"): + print("Initializing pre_bias from geometric median of data...") + first_shard = torch.from_numpy(store._load_shard(0)).float() + sample_size = min(32768, len(first_shard)) + sae.init_pre_bias_from_data(first_shard[:sample_size]) + print(f" pre_bias initialized (mean={sae.pre_bias.mean().item():.4f})") + del first_shard + + # Build configs + training_config = build_training_config(args, device) + wandb_config = build_wandb_config(args) + parallel_config = build_parallel_config(args) + + perf_logger = PerfLogger( + log_interval=args.log_interval, + use_wandb=args.wandb_enabled, + print_logs=True, + device=device, + ) + + # Train + trainer = Trainer( + sae, + training_config, + wandb_config=wandb_config, + perf_logger=perf_logger, + parallel_config=parallel_config, + ) + + if use_streaming: + rank = int(os.environ.get("RANK", 0)) + world_size = int(os.environ.get("WORLD_SIZE", 1)) + print( + f"Streaming from disk (~{est_gb:.0f}GB). " + f"Peak RAM: ~{shard_size * meta['hidden_dim'] * 4 / (1024**3):.1f}GB/process" + ) + + dataloader = store.get_streaming_dataloader( + batch_size=args.batch_size, + shuffle=args.shuffle, + seed=args.seed, + rank=rank, + world_size=world_size, + max_shards=max_shards, + ) + # Compute min batch count across all ranks to keep DDP in sync + # Read parquet footers for all ranks' shards (a few KB each, no data loading) + if world_size > 1: + import pyarrow.parquet as pq_meta + + dataset = dataloader.dataset + per_rank = len(dataset.shard_indices) + # Each rank got per_rank contiguous shards; compute batch count for each rank + min_batches = None + for r in range(world_size): + total_rows = sum( + pq_meta.read_metadata(store.path / f"shard_{idx:05d}.parquet").num_rows + for idx in range(r * per_rank, (r + 1) * per_rank) + ) + batches = total_rows // args.batch_size + if min_batches is None or batches < min_batches: + min_batches = batches + dataset.max_batches = min_batches + print(f"[rank {rank}] capped to {min_batches} batches/epoch for DDP sync") + trainer.fit( + dataloader, + resume_from=args.resume_from, + data_sharded=True, + ) + else: + shards = [] + for i, shard in enumerate(store.iter_shards(shuffle_shards=False)): + if max_shards is not None and i >= max_shards: + break + shards.append(torch.from_numpy(shard).float()) + activations_flat = torch.cat(shards) + print(f"Loaded {activations_flat.shape[0]:,} cached activations into memory") + + trainer.fit( + activations_flat, + resume_from=args.resume_from, + ) + + print("Training complete.") + + +if __name__ == "__main__": + main() diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/src/evo2_sae/__init__.py b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/src/evo2_sae/__init__.py new file mode 100644 index 0000000000..d8ac513dc8 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/src/evo2_sae/__init__.py @@ -0,0 +1,16 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-Apache2 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Sparse autoencoders for the Evo2 DNA language model.""" From 5edbf6ef0d8b658d5cc24a1b36dff634237689fc Mon Sep 17 00:00:00 2001 From: Polina Binder Date: Tue, 26 May 2026 21:14:43 +0000 Subject: [PATCH 03/12] evo2 recipe: drop empty src/evo2_sae package MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The recipe currently has no model-specific Python module — the extractor is upstream (`predict_evo2`) and the two scripts are simple CLIs in scripts/. Drop the empty package and adjust pyproject.toml so setuptools doesn't try to discover anything. Will reintroduce when there's actual library code to put there (eval, dashboard, dataloaders). Co-Authored-By: Claude Opus 4.7 (1M context) --- .../recipes/evo2/pyproject.toml | 7 +++++-- .../recipes/evo2/src/evo2_sae/__init__.py | 16 ---------------- 2 files changed, 5 insertions(+), 18 deletions(-) delete mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/src/evo2_sae/__init__.py diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/pyproject.toml b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/pyproject.toml index 26eff6b55c..1f00a62bc5 100644 --- a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/pyproject.toml +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/pyproject.toml @@ -17,8 +17,11 @@ dependencies = [ "pyarrow>=10.0", ] -[tool.setuptools.packages.find] -where = ["src"] +# No package code lives here yet — the recipe is just an entry-point for +# scripts/ that depends on the shared `sae` workspace package. Declare no +# packages so setuptools doesn't try to discover anything. +[tool.setuptools] +packages = [] [tool.uv.sources] sae = { workspace = true } diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/src/evo2_sae/__init__.py b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/src/evo2_sae/__init__.py deleted file mode 100644 index d8ac513dc8..0000000000 --- a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/src/evo2_sae/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: LicenseRef-Apache2 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Sparse autoencoders for the Evo2 DNA language model.""" From efbb7e07834c48f17bfb0b30ee58ec255a4a865b Mon Sep 17 00:00:00 2001 From: Polina Binder Date: Tue, 26 May 2026 22:48:45 +0000 Subject: [PATCH 04/12] evo2 recipe: add v1 SAE dashboard mockup MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fork of recipes/codonfm/codon_dashboard adapted for DNA + Evo 2, populated with synthetic data. Demo-able artifact, not a real result. What's here: - scripts/make_mockup_features.py: deterministic synthetic data generator (seed 42). Writes features_atlas.parquet, feature_metadata.parquet, feature_examples.parquet to evo2_dashboard_mockup/public/. Fixtures are committed for one-step npm-only setup. - evo2_dashboard_mockup/: Vite/React SPA forked from codon_dashboard with these swaps: * Removed molstar dep + MolstarThumbnail.jsx * Renamed ProteinSequence.jsx -> SequenceView.jsx; per-base rendering (no codon framing, no AA translation) * Renamed ProteinDetailModal.jsx -> RegionDetailModal.jsx; UniProt content swapped for genomic-region content * utils.js: getRegionLabel + parseBases (replacing getAccession/uniprotUrl/parseCodons/codonToAA) * MOCKUP banner at top of App * "Evo 2 SAE Feature Explorer (Mockup)" title - v2 roadmap placeholders (greyed em-dashes with hover tooltips): * FeatureCard: Annotation, Sensitivity, Recon Δ stats * FeatureDetailPage: Annotations, Conservation sections Quick start: cd evo2_dashboard_mockup && npm install && npm run dev The synthetic data schema is the contract the future real eval pipeline will need to target. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../evo2/evo2_dashboard_mockup/README.md | 55 + .../evo2/evo2_dashboard_mockup/index.html | 104 + .../evo2_dashboard_mockup/package-lock.json | 6366 +++++++++++++++++ .../evo2/evo2_dashboard_mockup/package.json | 24 + .../public/feature_examples.parquet | Bin 0 -> 182767 bytes .../public/feature_metadata.parquet | Bin 0 -> 6558 bytes .../public/features_atlas.parquet | Bin 0 -> 6558 bytes .../evo2/evo2_dashboard_mockup/src/App.jsx | 1639 +++++ .../src/EmbeddingView.jsx | 334 + .../evo2_dashboard_mockup/src/FeatureCard.jsx | 793 ++ .../src/FeatureDetailPage.jsx | 522 ++ .../evo2_dashboard_mockup/src/FeatureList.jsx | 83 + .../evo2_dashboard_mockup/src/Histogram.jsx | 85 + .../evo2_dashboard_mockup/src/InfoButton.jsx | 78 + .../src/RegionDetailModal.jsx | 157 + .../src/SequenceView.jsx | 267 + .../evo2/evo2_dashboard_mockup/src/index.jsx | 9 + .../evo2/evo2_dashboard_mockup/src/utils.js | 22 + .../evo2/evo2_dashboard_mockup/vite.config.js | 13 + .../evo2/scripts/make_mockup_features.py | 239 + 20 files changed, 10790 insertions(+) create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/README.md create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/index.html create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/package-lock.json create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/package.json create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/feature_examples.parquet create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/feature_metadata.parquet create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/features_atlas.parquet create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/App.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/EmbeddingView.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureList.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/Histogram.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/InfoButton.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/RegionDetailModal.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/SequenceView.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/index.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/utils.js create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/vite.config.js create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/README.md b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/README.md new file mode 100644 index 0000000000..b0c15231cc --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/README.md @@ -0,0 +1,55 @@ +# Evo 2 SAE Feature Explorer — Mockup + +**Mockup, not a real artifact.** This is a fork of `recipes/codonfm/codon_dashboard` adapted for DNA / Evo 2, populated with **synthetic data**. No real SAE outputs flow through it yet. The point of this v1 is to lock in the data contract that the future real eval pipeline will target. + +A `MOCKUP — synthetic data, not from a real SAE run` banner is rendered at the top of the app so nobody mistakes it for actual results. + +## Quick start (local) + +```bash +# In this directory: +npm install +npm run dev +# open http://localhost:5173 +``` + +The dashboard reads three parquet fixtures from `public/`: + +- `features_atlas.parquet` — UMAP coordinates + per-feature aggregates +- `feature_metadata.parquet` — feature label/stats table +- `feature_examples.parquet` — long table of (feature_id, example_rank, sequence_id, start, end, sequence, activations, ...) rows + +The fixtures are committed to the repo. To regenerate them: + +```bash +python ../scripts/make_mockup_features.py +``` + +That writes all three files into `public/`. Seed is fixed (`--seed 42`). + +## What's mocked vs. real + +| Thing | Source | +| ------------------------------------ | --------------------------------------------------------------- | +| Number of features | 20, hardcoded | +| Feature labels | Hardcoded biological-sounding strings | +| UMAP coordinates | 4 cluster centers + gaussian noise — fake but visibly clustered | +| Top activator windows | Random `ACGT` with a label-matching central motif spliced in | +| Per-token activations | Gaussian bump centered randomly in [80, 120], sigma ~= 8 bp | +| Vocab logits (promoted / suppressed) | Empty arrays — not in scope for v1 | + +## v2 roadmap placeholders + +A few greyed-out stats on each feature card (`Annotation`, `Sensitivity`, `Recon Δ`) and two empty sections on the feature detail page (`Annotations`, `Conservation`) hint at what's coming in v2. They render as em-dashes / dashed empty boxes with hover tooltips explaining what they'll show. + +## Out of scope (v1) + +- Real SAE inference or activation pass +- Annotation overlays (RefSeq / Rfam / JASPAR) +- Conservation tracks (phyloP) +- Strand handling, codon framing, chromosome ideograms +- External link-outs (UCSC, Ensembl) +- `sae.launch_dashboard()` Python wiring — run `npm run dev` directly +- Lepton-based serving + +These are deferred to v2, once the real eval pipeline produces matching parquet shapes. diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/index.html b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/index.html new file mode 100644 index 0000000000..5d09d6738d --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/index.html @@ -0,0 +1,104 @@ + + + + + + Evo 2 SAE Feature Explorer (Mockup) + + + +
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selected + const [mosaicReady, setMosaicReady] = useState(false) + const [categoryColumns, setCategoryColumns] = useState([]) + const [selectedCategory, setSelectedCategory] = useState('mean_variant_1bcdwt') + const [hiddenCategories, setHiddenCategories] = useState(new Set()) + const [clickedFeatureId, setClickedFeatureId] = useState(null) + const [clusterLabels, setClusterLabels] = useState(null) + const [vocabLogits, setVocabLogits] = useState(null) + const [featureAnalysis, setFeatureAnalysis] = useState(null) + + const brushRef = useRef(null) + const [showGuideModal, setShowGuideModal] = useState(false) + const [showMetricsModal, setShowMetricsModal] = useState(false) + const [searchTerm, setSearchTerm] = useState('') + const [cardResetKey, setCardResetKey] = useState(0) + const [plotResetKey, setPlotResetKey] = useState(0) + const [viewportState, setViewportState] = useState(null) // null = let embedding-atlas auto-fit on first load + const [displayedCardCount, setDisplayedCardCount] = useState(20) // Pagination: start with 20 cards + const [showEditedOnly, setShowEditedOnly] = useState(false) // Filter for edited features only + const [histMetric1, setHistMetric1] = useState('log_frequency') + const [histMetric2, setHistMetric2] = useState('max_activation') + const [histMetric3, setHistMetric3] = useState('mean_variant_1bcdwt') // tracks color-by selection + const featureRefs = useRef({}) + const featureListRef = useRef(null) + const endOfListRef = useRef(null) + const searchSource = useRef({ source: 'search' }) + const editedSource = useRef({ source: 'edited' }) + const legendSource = useRef({ source: 'legend' }) + const loadingMoreRef = useRef(false) + + // Lazy-load examples for a single feature from DuckDB (feature_examples VIEW) + const loadExamplesForFeature = useCallback(async (featureId) => { + const result = await vg.coordinator().query( + `SELECT * FROM feature_examples WHERE feature_id = ${featureId} ORDER BY example_rank` + ) + return result.toArray().map(row => ({ + sequence_id: row.sequence_id, + start: row.start, + end: row.end, + sequence: row.sequence, + activations: Array.from(row.activations), + max_activation: row.max_activation, + best_annotation: row.best_annotation, + })) + }, []) + + // Intersection Observer for infinite scroll pagination + useEffect(() => { + const sentinel = endOfListRef.current + const scrollContainer = featureListRef.current + if (!sentinel || !scrollContainer) return + + const observer = new IntersectionObserver( + entries => { + console.log('[scroll] sentinel intersecting:', entries[0].isIntersecting, 'loadingMore:', loadingMoreRef.current) + if (entries[0].isIntersecting && !loadingMoreRef.current) { + loadingMoreRef.current = true + setDisplayedCardCount(prev => prev + 20) + // Reset flag after a delay to allow next batch + setTimeout(() => { + loadingMoreRef.current = false + }, 300) + } + }, + { root: scrollContainer, threshold: 0.1, rootMargin: '200px' } + ) + + observer.observe(sentinel) + + return () => { + observer.disconnect() + } + }, [mosaicReady]) + + // Handle click on a feature in the UMAP (or null for empty canvas click) + const animationRef = useRef(null) + const currentViewportRef = useRef(null) + const initialViewportRef = useRef(null) + + // Handle viewport changes from the UMAP component + const handleViewportChange = useCallback((vp) => { + // Capture initial viewport on first report, slightly zoomed out so all points fit + if (!initialViewportRef.current && vp) { + initialViewportRef.current = { ...vp, scale: vp.scale * 0.5 } + setViewportState(initialViewportRef.current) + currentViewportRef.current = { ...initialViewportRef.current } + } + // Clamp zoom to max scale of 5 + if (vp && vp.scale > 5) { + const clamped = { ...vp, scale: 5 } + setViewportState(clamped) + currentViewportRef.current = clamped + return + } + // Always track current viewport (but not during our own animations) + if (!animationRef.current) { + currentViewportRef.current = vp + } + }, []) + + // Easing functions + const easeOutQuart = (t) => 1 - Math.pow(1 - t, 4) + const easeInOutCubic = (t) => t < 0.5 ? 4 * t * t * t : 1 - Math.pow(-2 * t + 2, 3) / 2 + const easeInOutQuad = (t) => t < 0.5 ? 2 * t * t : 1 - Math.pow(-2 * t + 2, 2) / 2 + + // Smooth zoom-in with "fly-to" trajectory (zoom out -> pan -> zoom in) + const zoomToPoint = useCallback((x, y) => { + if (x == null || y == null) return + + if (animationRef.current) { + cancelAnimationFrame(animationRef.current) + animationRef.current = null + } + + const start = currentViewportRef.current || initialViewportRef.current || { x: 0, y: 0, scale: 1 } + const targetScale = 4 // capped below max zoom of 5 + const duration = 800 + const startTime = performance.now() + + // Calculate how far we need to pan (in data space) + const panDistance = Math.sqrt(Math.pow(x - start.x, 2) + Math.pow(y - start.y, 2)) + + // Determine the "cruise altitude" - how much to zoom out during the pan + // Zoom out more for longer distances, less for short distances + const minScale = Math.min(start.scale, 0.8) // Never zoom out below 0.8 + const maxZoomOut = Math.max(0, start.scale - minScale) + const zoomOutAmount = Math.min(maxZoomOut, panDistance * 0.1) // Scale zoom-out with distance + const cruiseScale = start.scale - zoomOutAmount + + const animate = (currentTime) => { + const elapsed = currentTime - startTime + const t = Math.min(elapsed / duration, 1) + + // Use smooth ease-in-out for the overall progress + const smoothT = easeInOutCubic(t) + + // Pan follows the smooth progress + const panT = smoothT + + // Zoom follows a "U-shaped" profile: + // - First half: ease from start.scale down to cruiseScale (or stay flat if already low) + // - Second half: ease from cruiseScale up to targetScale + let zoomScale + if (t < 0.4) { + // First 40%: zoom out slightly (ease-out) + const zoomOutT = t / 0.4 + const easeOut = 1 - Math.pow(1 - zoomOutT, 2) + zoomScale = start.scale + (cruiseScale - start.scale) * easeOut + } else if (t < 0.6) { + // Middle 20%: hold at cruise altitude + zoomScale = cruiseScale + } else { + // Last 40%: zoom in to target (ease-in then ease-out) + const zoomInT = (t - 0.6) / 0.4 + const easeInOut = easeInOutQuad(zoomInT) + zoomScale = cruiseScale + (targetScale - cruiseScale) * easeInOut + } + + const newViewport = { + x: start.x + (x - start.x) * panT, + y: start.y + (y - start.y) * panT, + scale: zoomScale + } + + setViewportState(newViewport) + + if (t < 1) { + animationRef.current = requestAnimationFrame(animate) + } else { + currentViewportRef.current = { x, y, scale: targetScale } + animationRef.current = null + } + } + + animationRef.current = requestAnimationFrame(animate) + }, []) + + // Smooth zoom-out: zoom out first, then pan back + const resetViewport = useCallback(() => { + if (animationRef.current) { + cancelAnimationFrame(animationRef.current) + animationRef.current = null + } + + const start = currentViewportRef.current || { x: 0, y: 0, scale: 1 } + const target = initialViewportRef.current || { x: 0, y: 0, scale: 1 } + const duration = 600 + const startTime = performance.now() + + const animate = (currentTime) => { + const elapsed = currentTime - startTime + const t = Math.min(elapsed / duration, 1) + + // Zoom out fast at start (ease-out) + const zoomT = easeOutQuart(t) + + // Pan eases in-out + const panT = easeInOutCubic(t) + + const newViewport = { + x: start.x + (target.x - start.x) * panT, + y: start.y + (target.y - start.y) * panT, + scale: start.scale + (target.scale - start.scale) * zoomT + } + + setViewportState(newViewport) + + if (t < 1) { + animationRef.current = requestAnimationFrame(animate) + } else { + currentViewportRef.current = { ...target } + animationRef.current = null + } + } + + animationRef.current = requestAnimationFrame(animate) + }, []) + + // Handle click on a feature in the UMAP (with coordinates for zooming) + const handleFeatureClick = useCallback((featureId, x, y) => { + + setClickedFeatureId(featureId) + + if (featureId == null) return + + // Scroll to the feature card + setTimeout(() => { + const ref = featureRefs.current[featureId] + if (ref) { + ref.scrollIntoView({ behavior: 'smooth', block: 'center' }) + } + }, 50) + }, []) + + // Handle click on a feature card (highlights point in UMAP, no zoom) + const handleCardClick = useCallback(async (featureId, isExpanding) => { + + if (!isExpanding) { + setClickedFeatureId(null) + return + } + + setClickedFeatureId(featureId) + }, []) + + // Initialize Mosaic and load data + useEffect(() => { + async function init() { + try { + // Step 1: Initialize DuckDB-WASM + setLoadingProgress({ step: 1, total: 4, message: 'Initializing database engine...' }) + const wasm = wasmConnector() + vg.coordinator().databaseConnector(wasm) + + // Step 2: Load parquet data + setLoadingProgress({ step: 2, total: 4, message: 'Loading embedding data...' }) + const urlParams = new URLSearchParams(window.location.search) + const dataPath = urlParams.get('data') || '/features_atlas.parquet' + const parquetUrl = dataPath.startsWith('http') + ? dataPath + : new URL(dataPath, window.location.origin).href + + + await vg.coordinator().exec(` + CREATE TABLE features AS + SELECT * FROM read_parquet('${parquetUrl}') + `) + + // HDBSCAN assigns -1 to noise points; embedding-atlas casts category + // columns to UTINYINT which can't hold negatives. Remap to NULL. + try { + await vg.coordinator().exec(` + UPDATE features SET cluster_id = NULL WHERE cluster_id < 0 + `) + } catch (e) { + // cluster_id column may not exist — that's fine + } + + // Step 3: Process columns and categories + setLoadingProgress({ step: 3, total: 4, message: 'Processing columns...' }) + const schemaResult = await vg.coordinator().query(` + SELECT column_name, column_type + FROM (DESCRIBE features) + `) + + const columns = schemaResult.toArray().map(row => ({ + name: row.column_name, + type: row.column_type + })) + + const detectedCategories = [] + const sequentialColumns = [] + + for (const col of columns) { + if (['x', 'y', 'feature_id', 'top_example_idx'].includes(col.name)) continue + + if (col.type === 'VARCHAR') { + const isGsea = col.name.startsWith('gsea_') + const maxUnique = isGsea ? Infinity : 50 + const cardinalityResult = await vg.coordinator().query(` + SELECT COUNT(DISTINCT "${col.name}") as n_unique FROM features WHERE "${col.name}" IS NOT NULL AND "${col.name}" != 'unlabeled' + `) + const nUnique = cardinalityResult.toArray()[0]?.n_unique ?? 0 + if (nUnique > 0 && nUnique <= maxUnique) { + // For high-cardinality GSEA columns, collapse to top 20 + "other" + if (isGsea && nUnique > 20) { + await vg.coordinator().exec(` + CREATE OR REPLACE TABLE features AS + SELECT * REPLACE ( + CASE + WHEN "${col.name}" IS NULL OR "${col.name}" = 'unlabeled' THEN 'unlabeled' + WHEN "${col.name}" IN ( + SELECT "${col.name}" FROM features + WHERE "${col.name}" IS NOT NULL AND "${col.name}" != 'unlabeled' + GROUP BY "${col.name}" ORDER BY COUNT(*) DESC LIMIT 20 + ) THEN "${col.name}" + ELSE 'other' + END AS "${col.name}" + ) FROM features + `) + detectedCategories.push({ name: col.name, type: 'string', nUnique: 22 }) + } else { + detectedCategories.push({ name: col.name, type: 'string', nUnique }) + } + } + } else if (col.type === 'BIGINT' || col.type === 'INTEGER') { + if (col.name.includes('cluster') || col.name.includes('category') || col.name.includes('group')) { + const cardinalityResult = await vg.coordinator().query(` + SELECT COUNT(DISTINCT "${col.name}") as n_unique FROM features WHERE "${col.name}" IS NOT NULL + `) + const nUnique = cardinalityResult.toArray()[0]?.n_unique ?? 0 + if (nUnique > 0 && nUnique <= 50) { + detectedCategories.push({ name: col.name, type: 'integer', nUnique }) + } + } + } else if (col.type === 'DOUBLE' || col.type === 'FLOAT') { + // Numeric columns for sequential coloring + if (['log_frequency', 'max_activation', 'activation_freq', 'frequency', + 'mean_variant_1bcdwt', + 'high_score_fraction', 'clinvar_fraction', + 'mean_phylop', 'mean_variant_delta', 'mean_site_delta', 'mean_local_delta', + 'high_score_delta', 'low_score_delta', + 'gc_mean', 'gc_std', + 'trinuc_entropy', 'trinuc_dominant_frac', + 'pli_mean_pli', 'pli_frac_constrained', 'pli_max_pli', + 'codon_cai', 'codon_tai', 'codon_rscu', + 'gene_entropy', 'gene_n_unique', 'gene_dominant_frac', + ].includes(col.name)) { + sequentialColumns.push({ name: col.name, type: 'sequential' }) + } + } + } + + // Create integer-encoded versions of string category columns + for (const col of detectedCategories) { + if (col.type === 'string') { + await vg.coordinator().exec(` + CREATE OR REPLACE TABLE features AS + SELECT *, + CASE WHEN "${col.name}" IS NULL THEN NULL + ELSE DENSE_RANK() OVER (ORDER BY "${col.name}") - 1 + END AS "${col.name}_cat" + FROM features + `) + } + } + + // Create binned versions of sequential columns (10 bins) + const NUM_BINS = 10 + for (const col of sequentialColumns) { + await vg.coordinator().exec(` + CREATE OR REPLACE TABLE features AS + SELECT *, + CASE WHEN "${col.name}" IS NULL THEN NULL + ELSE LEAST(${NUM_BINS - 1}, CAST( + (("${col.name}" - (SELECT MIN("${col.name}") FROM features)) / + NULLIF((SELECT MAX("${col.name}") - MIN("${col.name}") FROM features), 0)) * ${NUM_BINS} + AS INTEGER)) + END AS "${col.name}_bin" + FROM features + `) + detectedCategories.push({ name: col.name, type: 'sequential', nUnique: NUM_BINS }) + } + + setCategoryColumns(detectedCategories) + + // Create crossfilter selection + brushRef.current = vg.Selection.crossfilter() + + + // Step 4: Load feature metadata from parquet via DuckDB + setLoadingProgress({ step: 4, total: 4, message: 'Loading feature metadata...' }) + const metaUrl = new URL('/feature_metadata.parquet', window.location.origin).href + const examplesUrl = new URL('/feature_examples.parquet', window.location.origin).href + + await vg.coordinator().exec(` + CREATE TABLE IF NOT EXISTS feature_metadata AS + SELECT * FROM read_parquet('${metaUrl}') + `) + await vg.coordinator().exec(` + CREATE VIEW IF NOT EXISTS feature_examples AS + SELECT * FROM read_parquet('${examplesUrl}') + `) + + // Load features from the features table (which has labels + category columns) + const categorySelectCols = detectedCategories + .filter(c => c.type === 'string' || c.type === 'integer') + .map(c => `"${c.name}"`) + .join(', ') + const extraSelect = categorySelectCols ? `, ${categorySelectCols}` : '' + const featuresResult = await vg.coordinator().query(` + SELECT + feature_id, + label, + activation_freq, + max_activation, + x, + y + ${extraSelect} + FROM features + ORDER BY feature_id + `) + const loadedFeatures = featuresResult.toArray().map(row => { + const f = { + feature_id: row.feature_id, + label: row.label, + description: row.label, + activation_freq: row.activation_freq, + max_activation: row.max_activation, + x: row.x, + y: row.y, + } + for (const col of detectedCategories) { + if (col.type === 'string' || col.type === 'integer') { + f[col.name] = row[col.name] + } + } + return f + }) + setFeatures(loadedFeatures) + + // Generate cluster labels from DuckDB (non-fatal if cluster_id doesn't exist) + try { + const clusterResult = await vg.coordinator().query(` + SELECT + cluster_id, + AVG(x) as cx, + AVG(y) as cy, + MODE(label) as top_label, + COUNT(*) as n + FROM features + WHERE cluster_id IS NOT NULL + GROUP BY cluster_id + ORDER BY n DESC + `) + const labels = clusterResult.toArray() + .filter(row => row.top_label && !row.top_label.startsWith('Feature ')) + .map((row, i) => ({ + x: Number(row.cx), + y: Number(row.cy), + text: row.top_label.length > 40 ? row.top_label.slice(0, 40) + '...' : row.top_label, + priority: row.n, + level: 0, + })) + console.log('[cluster labels] generated:', labels.length, labels.slice(0, 5)) + if (labels.length > 0) { + setClusterLabels(labels) + } + } catch (e) { + console.log('[cluster labels] query failed:', e.message) + } + + // Load cluster labels from file (overrides computed ones if present) + try { + const labelsRes = await fetch('./cluster_labels.json') + if (labelsRes.ok) { + const labelsData = await labelsRes.json() + setClusterLabels(labelsData) + } + } catch (labelErr) { + } + + // Load vocab logits (non-fatal if missing) + try { + const logitsRes = await fetch('./vocab_logits.json') + if (logitsRes.ok) { + const logitsData = await logitsRes.json() + setVocabLogits(logitsData) + } + } catch (e) { + } + + // Load feature analysis (non-fatal if missing) + try { + const analysisRes = await fetch('./feature_analysis.json') + if (analysisRes.ok) { + const analysisData = await analysisRes.json() + setFeatureAnalysis(analysisData) + } + } catch (e) { + } + + setMosaicReady(true) + setLoading(false) + + } catch (err) { + console.error('Init error:', err) + setError(err.message) + setLoading(false) + } + } + + init() + }, []) + + // Create a Mosaic client that receives filtered feature IDs + useEffect(() => { + if (!mosaicReady || !brushRef.current) return + + const coordinator = vg.coordinator() + const selection = brushRef.current + const totalFeatures = features.length + + // Create a class that extends MosaicClient + class FeatureFilterClient extends MosaicClient { + constructor(filterBy) { + super(filterBy) + this._isConnected = true + } + + query(filter = []) { + // Use Mosaic's Query builder + const q = Query + .select({ feature_id: 'feature_id' }) + .distinct() + .from('features') + + // Apply filter if present + if (filter.length > 0) { + q.where(filter) + } + + return q + } + + queryResult(data) { + if (!this._isConnected) return + + try { + let ids = new Set() + if (data && typeof data.getChild === 'function') { + const col = data.getChild('feature_id') + if (col) { + for (let i = 0; i < col.length; i++) { + ids.add(col.get(i)) + } + } + } else if (data && data.toArray) { + ids = new Set(data.toArray().map(r => r.feature_id)) + } + setSelectedFeatureIds(ids.size > 0 && ids.size < totalFeatures ? ids : null) + } catch (err) { + console.error('Error processing result:', err) + } + } + + // Required by Mosaic for selection updates + update() { + return this + } + + queryError(err) { + if (this._isConnected) { + console.error('FeatureFilterClient error:', err) + } + } + + disconnect() { + this._isConnected = false + } + } + + const client = new FeatureFilterClient(selection) + + // Delay connection slightly to ensure Mosaic is fully ready + const timeoutId = setTimeout(() => { + try { + coordinator.connect(client) + } catch (err) { + console.warn('Error connecting FeatureFilterClient:', err) + } + }, 0) + + return () => { + clearTimeout(timeoutId) + try { + client.disconnect() + coordinator.disconnect(client) + } catch (err) { + // Ignore disconnect errors + } + } + }, [mosaicReady, features.length]) + + // Clear ALL selections (search, histograms, UMAP, clicked feature) + const handleClearSelection = useCallback(() => { + if (brushRef.current) { + const selection = brushRef.current + // Clear each clause by updating with null predicate for each source + const clauses = selection.clauses || [] + for (const clause of clauses) { + if (clause.source) { + try { + selection.update({ source: clause.source, predicate: null, value: null }) + } catch (e) { + // Ignore errors from clearing + } + } + } + // Also clear the search clause specifically + if (searchSource.current) { + try { + selection.update({ source: searchSource.current, predicate: null, value: null }) + } catch (e) { + // Ignore + } + } + } + setSelectedFeatureIds(null) + setSearchTerm('') + setClickedFeatureId(null) + setHiddenCategories(new Set()) + // Reset viewport to the auto-fit view captured on first load + if (initialViewportRef.current) { + setViewportState({ ...initialViewportRef.current }) + currentViewportRef.current = { ...initialViewportRef.current } + } else { + setViewportState(null) + currentViewportRef.current = null + } + // Reset all cards to collapsed state + setCardResetKey(k => k + 1) + // Reset histograms and UMAP to clear brush visuals + setPlotResetKey(k => k + 1) + }, []) + + // Export all edited features to CSV with full data + const handleExportEdited = useCallback(() => { + // Get all edited features + const editedFeatures = features.filter(f => localStorage.getItem(`featureTitle_${f.feature_id}`) !== null) + + if (editedFeatures.length === 0) { + alert('No edited features to export') + return + } + + const lines = [] + const escapeCsv = (str) => `"${(str || '').toString().replace(/"/g, '""')}"` + + // Codon mapping for amino acids + const CODON_AA = { + 'TTT':'F','TTC':'F','TTA':'L','TTG':'L','TCT':'S','TCC':'S','TCA':'S','TCG':'S', + 'TAT':'Y','TAC':'Y','TAA':'*','TAG':'*','TGT':'C','TGC':'C','TGA':'*','TGG':'W', + 'CTT':'L','CTC':'L','CTA':'L','CTG':'L','CCT':'P','CCC':'P','CCA':'P','CCG':'P', + 'CAT':'H','CAC':'H','CAA':'Q','CAG':'Q','CGT':'R','CGC':'R','CGA':'R','CGG':'R', + 'ATT':'I','ATC':'I','ATA':'I','ATG':'M','ACT':'T','ACC':'T','ACA':'T','ACG':'T', + 'AAT':'N','AAC':'N','AAA':'K','AAG':'K','AGT':'S','AGC':'S','AGA':'R','AGG':'R', + 'GTT':'V','GTC':'V','GTA':'V','GTG':'V','GCT':'A','GCC':'A','GCA':'A','GCG':'A', + 'GAT':'D','GAC':'D','GAA':'E','GAG':'E','GGT':'G','GGC':'G','GGA':'G','GGG':'G', + } + + editedFeatures.forEach((f, idx) => { + const userTitle = localStorage.getItem(`featureTitle_${f.feature_id}`) + const label = f.label || `Feature ${f.feature_id}` + + // Add separator for readability + if (idx > 0) lines.push('') + + // Feature metadata + lines.push(`=== FEATURE ${f.feature_id} ===`) + lines.push(`Feature ID,${f.feature_id}`) + lines.push(`Original Label,${escapeCsv(label)}`) + lines.push(`Your Title,${escapeCsv(userTitle)}`) + lines.push(`Activation Frequency,${(f.activation_freq || 0).toFixed(6)}`) + lines.push(`Max Activation,${(f.max_activation || 0).toFixed(4)}`) + lines.push('') + + // Vocab logits + const logits = vocabLogits?.[String(f.feature_id)] + if (logits) { + lines.push('TOP PROMOTED CODONS') + lines.push('Codon,Amino Acid,Logit Value') + ;(logits.top_positive || []).forEach(([codon, val]) => { + lines.push(`${codon},${CODON_AA[codon] || '?'},${val.toFixed(4)}`) + }) + lines.push('') + + lines.push('TOP SUPPRESSED CODONS') + lines.push('Codon,Amino Acid,Logit Value') + ;(logits.top_negative || []).forEach(([codon, val]) => { + lines.push(`${codon},${CODON_AA[codon] || '?'},${val.toFixed(4)}`) + }) + lines.push('') + } + + // Feature analysis + const analysis = featureAnalysis?.[String(f.feature_id)] + if (analysis?.codon_annotations) { + lines.push('CODON ANNOTATIONS') + const ann = analysis.codon_annotations + if (ann.amino_acid) { + lines.push(`Amino Acid,${ann.amino_acid.aa}`) + lines.push(`AA Frequency,${(ann.amino_acid.fraction * 100).toFixed(1)}%`) + } + if (ann.codon_usage) { + lines.push(`Codon Usage,${ann.codon_usage.bias}`) + } + if (ann.wobble) { + lines.push(`Wobble Position,${ann.wobble.preference}`) + } + if (ann.cpg) { + lines.push(`CpG Context,${ann.cpg.fraction}`) + } + lines.push('') + } + }) + + // Create and download file + const csv = lines.join('\n') + const blob = new Blob([csv], { type: 'text/csv' }) + const url = URL.createObjectURL(blob) + const a = document.createElement('a') + a.href = url + a.download = `edited_features_${new Date().toISOString().split('T')[0]}.csv` + document.body.appendChild(a) + a.click() + document.body.removeChild(a) + URL.revokeObjectURL(url) + }, [features, vocabLogits, featureAnalysis]) + + // Update Mosaic crossfilter when "Edited Only" toggle changes + useEffect(() => { + if (!brushRef.current || !mosaicReady) return + + const selection = brushRef.current + + if (showEditedOnly) { + // Get all edited feature IDs from localStorage + const editedIds = features + .filter(f => localStorage.getItem(`featureTitle_${f.feature_id}`) !== null) + .map(f => f.feature_id) + + if (editedIds.length > 0) { + // Create predicate: feature_id IN (id1, id2, id3, ...) + const idsStr = editedIds.join(',') + // Use raw SQL string, not literal() which would quote it as a string + const predicateSql = `feature_id IN (${idsStr})` + + try { + selection.update({ + source: editedSource.current, + predicate: predicateSql, + value: 'edited' + }) + } catch (err) { + console.warn('Error updating edited filter:', err) + } + } + } else { + // Clear the edited filter + try { + selection.update({ + source: editedSource.current, + predicate: null, + value: null + }) + } catch (err) { + console.warn('Error clearing edited filter:', err) + } + } + }, [showEditedOnly, mosaicReady, features]) + + // Update Mosaic crossfilter when legend selection changes + useEffect(() => { + if (!brushRef.current || !mosaicReady) return + + const selection = brushRef.current + + if (hiddenCategories.size > 0 && selectedCategory && selectedCategory !== 'none') { + const colInfo = categoryColumns.find(c => c.name === selectedCategory) + if (colInfo && (colInfo.type === 'string' || colInfo.type === 'integer')) { + const values = Array.from(hiddenCategories).map(v => `'${v.replace(/'/g, "''")}'`).join(',') + const predicateSql = `"${selectedCategory}" IN (${values})` + + try { + selection.update({ + source: legendSource.current, + predicate: predicateSql, + value: Array.from(hiddenCategories).join(',') + }) + } catch (err) { + console.warn('Legend filter update failed:', err) + } + } + } else { + try { + selection.update({ + source: legendSource.current, + predicate: null, + value: null + }) + } catch (err) { + // Ignore + } + } + }, [hiddenCategories, selectedCategory, mosaicReady, categoryColumns]) + + // Handle search - updates both Mosaic crossfilter (for UMAP/histograms) and local state (for cards) + const handleSearchChange = useCallback((e) => { + const term = e.target.value + setSearchTerm(term) + + // Also update Mosaic crossfilter so UMAP and histograms filter + if (brushRef.current) { + const selection = brushRef.current + + try { + if (term.trim()) { + // Build predicate using sql template - ILIKE for case-insensitive search + const pattern = literal('%' + term.trim() + '%') + const predicate = sql`label ILIKE ${pattern}` + + selection.update({ + source: searchSource.current, + predicate: predicate, + value: term.trim() + }) + } else { + // Clear search by removing the clause + selection.update({ + source: searchSource.current, + predicate: null, + value: null + }) + } + } catch (err) { + console.warn('Search update error:', err) + } + } + }, []) + + // Filter and sort features + const filteredFeatures = useMemo(() => { + let result = features + + // Filter by Mosaic selection (includes UMAP brush) + if (selectedFeatureIds !== null) { + result = result.filter(f => selectedFeatureIds.has(f.feature_id)) + } + + // Also filter by search term client-side (searches metadata fields) + if (searchTerm.trim()) { + const q = searchTerm.toLowerCase() + result = result.filter(f => + f.description?.toLowerCase().includes(q) || + f.feature_id.toString().includes(q) || + f.best_annotation?.toLowerCase().includes(q) + ) + } + + // Filter by edited features only + if (showEditedOnly) { + result = result.filter(f => localStorage.getItem(`featureTitle_${f.feature_id}`) !== null) + } + + // Helper: unlabeled features sort last + const isUnlabeled = (f) => { + const lbl = (f.label || f.description || '').toLowerCase() + return !lbl || lbl.startsWith('feature ') || lbl.includes('common codons') + } + + // Sort (labeled features first, then by chosen metric) + if (sortBy === 'frequency') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || (b.activation_freq || 0) - (a.activation_freq || 0)) + } else if (sortBy === 'max_activation') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || (b.max_activation || 0) - (a.max_activation || 0)) + } else if (sortBy === 'feature_id') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || a.feature_id - b.feature_id) + } else if (sortBy === 'high_score_fraction') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || (b.high_score_fraction || 0) - (a.high_score_fraction || 0)) + } else if (sortBy === 'mean_variant_delta') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || Math.abs(b.mean_variant_delta || 0) - Math.abs(a.mean_variant_delta || 0)) + } else if (sortBy === 'mean_site_delta') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || Math.abs(b.mean_site_delta || 0) - Math.abs(a.mean_site_delta || 0)) + } else if (sortBy === 'mean_local_delta') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || Math.abs(b.mean_local_delta || 0) - Math.abs(a.mean_local_delta || 0)) + } else if (sortBy === 'clinvar_fraction') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || (b.clinvar_fraction || 0) - (a.clinvar_fraction || 0)) + } else if (sortBy === 'mean_phylop') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || (b.mean_phylop || 0) - (a.mean_phylop || 0)) + } else if (sortBy === 'gc_mean') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || Math.abs((b.gc_mean || 0.5) - 0.5) - Math.abs((a.gc_mean || 0.5) - 0.5)) + } else if (sortBy === 'trinuc_entropy') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || (a.trinuc_entropy ?? 99) - (b.trinuc_entropy ?? 99)) + } else if (sortBy === 'gene_entropy') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || (a.gene_entropy ?? 99) - (b.gene_entropy ?? 99)) + } else if (sortBy === 'gene_n_unique') { + result = [...result].sort((a, b) => isUnlabeled(a) - isUnlabeled(b) || (a.gene_n_unique || 999) - (b.gene_n_unique || 999)) + } + + return result + }, [features, sortBy, selectedFeatureIds, searchTerm, showEditedOnly]) + + // Reset pagination when filters change + useEffect(() => { + setDisplayedCardCount(20) + loadingMoreRef.current = false + }, [searchTerm, sortBy, selectedFeatureIds, showEditedOnly]) + + if (loading) { + const pct = Math.round(((loadingProgress.step - 1) / loadingProgress.total) * 100) + return ( +

+
Loading dashboard...
+
+
+
+
{loadingProgress.message}
+
+ ) + } + + if (error) { + return ( +
+

Error: {error}

+

+ Make sure features_atlas.parquet, feature_metadata.parquet, and feature_examples.parquet exist in the public/ folder. +

+
+ ) + } + + return ( +
+
+ MOCKUP — synthetic data, not from a real SAE run +
+
+
+

Evo 2 SAE Feature Explorer (Mockup)

+
+
+ + +
+
+ +
+
+
+
+ + Decoder UMAP + +
+ + + setShowMetricsModal(true)} + style={{ + display: 'inline-flex', alignItems: 'center', justifyContent: 'center', + width: '15px', height: '15px', borderRadius: '50%', border: '1px solid var(--border-input)', + fontSize: '10px', fontWeight: '600', color: 'var(--text-tertiary)', cursor: 'pointer', + userSelect: 'none', lineHeight: 1, flexShrink: 0, + }} + >i + +
+
+
+ {mosaicReady && ( + + )} + {selectedCategory && selectedCategory !== 'none' && (() => { + const colInfo = categoryColumns.find(c => c.name === selectedCategory) + if (!colInfo) return null + + if (colInfo.type === 'sequential') { + const colors = [ + "#c359ef", "#9525C6", "#0046a4", "#0074DF", "#3f8500", + "#76B900", "#ef9100", "#F9C500", "#ff8181", "#EF2020" + ] + const vals = features + .map(f => f[selectedCategory]) + .filter(v => v != null && !isNaN(v)) + const minVal = vals.length > 0 ? Math.min(...vals) : 0 + const maxVal = vals.length > 0 ? Math.max(...vals) : 1 + const fmt = (v) => Math.abs(v) >= 100 ? v.toFixed(0) : Math.abs(v) >= 1 ? v.toFixed(1) : v.toFixed(3) + return ( +
+ {fmt(maxVal)} +
+ {fmt(minVal)} + + {selectedCategory.replace(/_/g, ' ')} + +
+ ) + } + + if (colInfo.type === 'string' || colInfo.type === 'integer') { + const catColors = [ + "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", + "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf", + "#aec7e8", "#ffbb78", "#98df8a", "#ff9896", "#c5b0d5", + "#c49c94", "#f7b6d2", "#c7c7c7", "#dbdb8d", "#9edae5" + ] + // Count occurrences of each category value, sorted alphabetically + // (matching DENSE_RANK ORDER BY which is alphabetical) + const counts = {} + for (const f of features) { + const val = f[selectedCategory] + if (val != null && val !== '') { + counts[val] = (counts[val] || 0) + 1 + } + } + // Sort alphabetically to match dense_rank ordering + const sortedCategories = Object.keys(counts).sort() + return ( +
+
+ {selectedCategory.replace(/_/g, ' ').replace('gsea ', '')} +
+ {sortedCategories.map((cat, i) => { + const hasFilter = hiddenCategories.size > 0 + const isHidden = hasFilter && !hiddenCategories.has(cat) + return ( +
{ + if (e.metaKey || e.ctrlKey) { + // Cmd/Ctrl+click: toggle this category in the selection + setHiddenCategories(prev => { + const next = new Set(prev) + if (next.has(cat)) { + next.delete(cat) + // If nothing left selected, clear filter + return next.size === 0 ? new Set() : next + } else { + next.add(cat) + return next + } + }) + } else { + // Regular click: solo this category (or clear if already solo'd) + setHiddenCategories(prev => { + if (prev.size === 1 && prev.has(cat)) return new Set() + return new Set([cat]) + }) + } + }} + style={{ + display: 'flex', alignItems: 'center', gap: '5px', padding: '2px 0', + cursor: 'pointer', opacity: isHidden ? 0.15 : 1, + userSelect: 'none', + }} + > + + + {cat} + + + {counts[cat]} + +
+ ) + })} +
+ ) + } + + return null + })()} +
+
+ +
+ {[ + { value: histMetric1, setter: setHistMetric1 }, + { value: histMetric2, setter: setHistMetric2 }, + { value: histMetric3, setter: setHistMetric3 }, + ].map(({ value, setter }, i) => ( +
+
+ +
+ {mosaicReady && value && value !== 'none' && ( + + )} +
+ ))} +
+
+ +
+
+ + + +
+ +
+ + Showing {filteredFeatures.length} of {features.length} features + {selectedFeatureIds !== null && ` (${selectedFeatureIds.size} selected in UMAP)`} + + setShowGuideModal(true)} + style={{ + display: 'inline-flex', alignItems: 'center', justifyContent: 'center', + width: '15px', height: '15px', borderRadius: '50%', border: '1px solid #bbb', + fontSize: '10px', fontWeight: '600', color: '#888', cursor: 'pointer', + userSelect: 'none', lineHeight: 1, flexShrink: 0, + }} + >i +
+ + +
+
+ + {showGuideModal && ( +
setShowGuideModal(false)} + style={{ + position: 'fixed', inset: 0, background: 'rgba(0,0,0,0.45)', + display: 'flex', alignItems: 'center', justifyContent: 'center', zIndex: 1000, + }} + > +
e.stopPropagation()} + style={{ + background: 'var(--bg-card)', borderRadius: '10px', maxWidth: '560px', width: '90%', + maxHeight: '80vh', overflowY: 'auto', padding: '28px 32px', + boxShadow: '0 8px 30px rgba(0,0,0,0.2)', + }} + > +
+

Feature Card Guide

+ setShowGuideModal(false)} + style={{ cursor: 'pointer', fontSize: '20px', color: '#999', lineHeight: 1 }} + >× +
+ +
+

Decoder Logits

+

+ The decoder logits histogram shows the projection of each feature's learned decoder weight vector through the language model's prediction head, with the mean logit vector subtracted across all features. This mean-centering removes the model's shared baseline bias toward common codons (e.g. GCC), so values reflect what each feature specifically promotes or suppresses relative to the average feature. Each bar represents a codon. Green bars indicate codons the feature promotes above baseline; red bars indicate codons it suppresses below baseline. Gray bars have no feature-specific effect. This tells you what the feature pushes the model to output — not what activates it. Stop codons (TAA, TAG, TGA) are excluded because the model was trained on coding sequences where internal stops almost never appear, so all features uniformly suppress them. +

+ +

Top Activating Sequences

+

+ These are the protein-coding sequences where this feature fires most strongly. Each codon is colored by its activation value — brighter highlights mean the feature responds more strongly at that position. This shows what inputs trigger the feature, which is conceptually distinct from decoder logits. A feature can activate strongly on a particular codon (e.g., lysine codons) without promoting that same codon in the output — it may instead influence downstream or contextual predictions. +

+ +
+
+
+ )} + + {showMetricsModal && ( +
setShowMetricsModal(false)} + style={{ + position: 'fixed', inset: 0, background: 'rgba(0,0,0,0.45)', + display: 'flex', alignItems: 'center', justifyContent: 'center', zIndex: 1000, + }} + > +
e.stopPropagation()} + style={{ + background: 'var(--bg-card)', borderRadius: '10px', maxWidth: '620px', width: '90%', + maxHeight: '80vh', overflowY: 'auto', padding: '28px 32px', + boxShadow: '0 8px 30px rgba(0,0,0,0.2)', + }} + > +
+

Variant Analysis Metrics

+ setShowMetricsModal(false)} + style={{ cursor: 'pointer', fontSize: '20px', color: '#999', lineHeight: 1 }} + >× +
+ +
+

Mean Variant Score (per model)

+

+ For each feature, the average model effect score across variant sequences where the feature fires. Computed for the 1b_cdwt model score column. A high value means the feature preferentially activates on variants that model predicts to be functionally impactful. +

+ +

High Score Fraction

+

+ Variants are split at the median model score. Among variants where a feature fires, what fraction are high-scoring? A value of 0.5 means no preference. Above 0.5 means the feature disproportionately fires on high-impact variants. Robust to outliers — measures distributional preference rather than average. +

+ +

ClinVar Fraction

+

+ Among variant sequences where the feature fires, the fraction from ClinVar vs COSMIC. ClinVar variants are germline (inherited, Mendelian disease). COSMIC variants are somatic (cancer mutations). High ClinVar fraction means the feature responds to germline disease patterns; low means it prefers somatic cancer mutation patterns. +

+ +

Mean PhyloP

+

+ Average evolutionary conservation score (PhyloP) across sequences where the feature fires. High values indicate conserved positions (functionally important). Negative values indicate rapidly evolving regions. Features with high mean PhyloP capture evolutionarily constrained patterns. +

+ +

Mean Variant Delta

+

+ For each gene, the difference in max feature activation between the variant and reference sequence: max_act(variant) − max_act(ref), averaged across all variant-ref pairs. Positive means the mutation increases feature activation; negative means it suppresses it. Near zero means the feature responds to the gene background, not the specific mutation. This controls for gene identity. +

+ +

Mean Site Delta

+

+ Like mean variant delta, but measured only at the exact codon position where the mutation occurs: activation_f(variant, pos) − activation_f(ref, pos). This captures direct effects — the feature responding to the changed codon itself. Compare with mean variant delta: a large variant delta but small site delta means the feature captures indirect/distal effects of the mutation (e.g., changes to predicted protein folding context), not the local codon change. +

+ +

Mean Local Delta

+

+ Like variant delta, but using the max activation within a 3-codon window around the variant site instead of the full sequence. Captures local effects of the mutation: max(window_variant) − max(window_ref). A large local delta with a small global delta means the mutation's effect is localized. Compare with site delta (exact position only) and variant delta (full sequence). +

+ +

GC Content (mean, std)

+

+ Mean and standard deviation of GC content across all sequences where the feature fires. Features with extreme GC mean (far from ~0.5) are GC-biased. Features with low GC std activate only on sequences with similar GC content — suggesting sensitivity to nucleotide composition rather than specific codon patterns. +

+ +

Trinuc Entropy

+

+ Shannon entropy (in bits) of the trinucleotide context distribution among variant sequences where the feature fires. Low entropy means the feature concentrates on specific mutation contexts (e.g., C[C>T]G for CpG transitions). High entropy means it fires across diverse mutation types. The dominant fraction shows what fraction of activations come from the most common trinuc context. +

+ +

Gene Distribution

+

+ Shannon entropy of the gene distribution among sequences where the feature fires. Low entropy means the feature is gene-specific — it concentrates on a few genes. High entropy means it fires broadly. gene_n_unique is the number of distinct genes. gene_dominant_frac is the fraction from the most common gene. A feature with low entropy and high dominant fraction has learned something specific to one gene family. +

+ +

High Score Delta

+

+ Same as mean variant delta, but averaged only over variants with model scores above the median. Shows how the feature responds specifically to high-impact mutations. Compare with low score delta: if high_score_delta >> low_score_delta, the feature selectively detects impactful mutations. +

+ +

Low Score Delta

+

+ Same as mean variant delta, but averaged only over variants with model scores below the median. Features where high score delta and low score delta differ significantly have learned to discriminate mutation severity. Features where both are similar just detect that a mutation occurred without distinguishing impact. +

+
+
+
+ )} +
+ ) +} diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/EmbeddingView.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/EmbeddingView.jsx new file mode 100644 index 0000000000..bc14226257 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/EmbeddingView.jsx @@ -0,0 +1,334 @@ +import React, { useEffect, useRef } from 'react' +import { EmbeddingViewMosaic } from 'embedding-atlas' + +// Color palette for categories (D3 category10 + extended) +const CATEGORY_COLORS = [ + "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", + "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf", + "#aec7e8", "#ffbb78", "#98df8a", "#ff9896", "#c5b0d5", + "#c49c94", "#f7b6d2", "#c7c7c7", "#dbdb8d", "#9edae5" +] + +// Sequential color palette (NVIDIA brand) +const SEQUENTIAL_COLORS = [ + "#c359ef", "#9525C6", "#0046a4", "#0074DF", "#3f8500", + "#76B900", "#ef9100", "#F9C500", "#ff8181", "#EF2020" +] + +// Default color for uniform coloring (NVIDIA green) +const DEFAULT_COLOR = "#76b900" + +// Custom tooltip renderer +class FeatureTooltip { + constructor(node, props) { + this.node = node + this.inner = document.createElement("div") + this.inner.style.cssText = ` + background: var(--bg-card); + border: 1px solid var(--border); + border-radius: 4px; + padding: 8px 12px; + font-family: 'NVIDIA Sans', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; + font-size: 13px; + box-shadow: 0 2px 8px rgba(0,0,0,0.25); + max-width: 300px; + color: var(--text); + ` + this.node.appendChild(this.inner) + this.update(props) + } + + update(props) { + const { tooltip } = props + if (!tooltip) { + this.inner.innerHTML = "" + return + } + const featureId = tooltip.identifier ?? "" + const label = tooltip.fields?.label ?? tooltip.text ?? "" + const logFreq = tooltip.fields?.log_frequency + const maxAct = tooltip.fields?.max_activation + const colorField = tooltip.fields?.color_field + + this.inner.innerHTML = ` +
Feature #${featureId}
+
${label}
+ ${colorField ? `
Category: ${colorField}
` : ""} + ${logFreq !== undefined ? `
Log Frequency: ${logFreq.toFixed(3)}
` : ""} + ${maxAct !== undefined ? `
Max Activation: ${maxAct.toFixed(2)}
` : ""} + ` + } + + destroy() { + this.inner.remove() + } +} + +export default function EmbeddingView({ brush, categoryColumn, categoryColumns, onFeatureClick, highlightedFeatureId, viewportState, onViewportChange, labels, features, selectedCategory, darkMode, hiddenCategories }) { + const containerRef = useRef(null) + const viewRef = useRef(null) + const onFeatureClickRef = useRef(onFeatureClick) + const onViewportChangeRef = useRef(onViewportChange) + + // Keep the callback refs updated + useEffect(() => { + onFeatureClickRef.current = onFeatureClick + }, [onFeatureClick]) + + useEffect(() => { + onViewportChangeRef.current = onViewportChange + }, [onViewportChange]) + + // Update selection and tooltip when highlightedFeatureId changes + useEffect(() => { + if (viewRef.current && highlightedFeatureId != null) { + // Find the feature data + const feature = features?.find(f => f.feature_id === highlightedFeatureId) + + // Build tooltip fields + const fields = { + label: feature?.label || `Feature ${highlightedFeatureId}`, + log_frequency: feature?.log_frequency || feature?.activation_freq || 0, + max_activation: feature?.max_activation || 0, + color_field: null + } + + // Add selected category metric if available + if (selectedCategory && selectedCategory !== 'none' && feature) { + const metricName = selectedCategory.replace(/_/g, ' ') + const metricValue = feature[selectedCategory] + if (metricValue !== undefined && metricValue !== null) { + fields.color_field = `${metricName}: ${typeof metricValue === 'number' ? metricValue.toFixed(3) : metricValue}` + } + } + + // Construct tooltip object with feature data + const tooltipObj = { + identifier: highlightedFeatureId, + text: `Feature #${highlightedFeatureId}`, + x: feature?.x, + y: feature?.y, + fields: fields + } + // Clear previous selection first to avoid animated transition + viewRef.current.update({ + selection: null, + tooltip: null + }) + viewRef.current.update({ + selection: [highlightedFeatureId], + tooltip: tooltipObj + }) + } else if (viewRef.current && highlightedFeatureId == null) { + viewRef.current.update({ + selection: null, + tooltip: null + }) + } + }, [highlightedFeatureId, features, selectedCategory]) + + // Update viewport when viewportState changes (skip null to let auto-fit persist) + useEffect(() => { + if (viewRef.current && viewportState != null) { + viewRef.current.update({ + viewportState: viewportState + }) + } + }, [viewportState]) + + // Update color scheme when dark mode changes + useEffect(() => { + if (viewRef.current) { + viewRef.current.update({ + config: { colorScheme: darkMode ? "dark" : "light" } + }) + } + }, [darkMode]) + + // Update labels when they change + useEffect(() => { + if (viewRef.current && labels) { + console.log('[EmbeddingView] updating labels:', labels.length, labels.slice(0, 2)) + viewRef.current.update({ + labels: labels + }) + } + }, [labels]) + + useEffect(() => { + if (!containerRef.current || !brush) return + + // Clear previous view + if (viewRef.current) { + containerRef.current.innerHTML = '' + } + + // Determine category column and colors + let categoryColName = null + let colors = Array(50).fill(DEFAULT_COLOR) + let additionalFields = { + label: "label", + log_frequency: "log_frequency", + max_activation: "max_activation", + } + + if (categoryColumn && categoryColumn !== "none") { + const colInfo = categoryColumns?.find(c => c.name === categoryColumn) + if (colInfo) { + if (colInfo.type === 'sequential') { + // Sequential column - use binned version and sequential colors + categoryColName = `${categoryColumn}_bin` + colors = SEQUENTIAL_COLORS + } else if (colInfo.type === 'string') { + // Categorical string column + categoryColName = `${categoryColumn}_cat` + colors = CATEGORY_COLORS.slice(0, Math.max(colInfo.nUnique, 10)) + } else { + // Integer categorical column + categoryColName = categoryColumn + colors = CATEGORY_COLORS.slice(0, Math.max(colInfo.nUnique, 10)) + } + additionalFields.color_field = categoryColumn + } + } + + const width = containerRef.current.clientWidth + const height = containerRef.current.clientHeight + + try { + viewRef.current = new EmbeddingViewMosaic( + containerRef.current, + { + table: "features", + x: "x", + y: "y", + category: categoryColName, + text: "label", + identifier: "feature_id", + filter: brush, + rangeSelection: brush, + selection: highlightedFeatureId != null ? [highlightedFeatureId] : null, + viewportState: viewportState, + categoryColors: colors, + width: width, + height: height, + labels: labels || null, + config: { + mode: "points", + colorScheme: document.documentElement.classList.contains('dark') ? "dark" : "light", + autoLabelEnabled: false, + }, + theme: { + brandingLink: { + text: "NVIDIA BioNeMo", + href: "https://github.com/NVIDIA/bionemo-framework", + }, + }, + additionalFields: additionalFields, + customTooltip: FeatureTooltip, + onSelection: (selection) => { + // selection is DataPoint[] | null + if (!onFeatureClickRef.current) return + + if (selection && selection.length > 0) { + // Get the last clicked point (most recent selection) + const lastPoint = selection[selection.length - 1] + const featureId = lastPoint?.identifier ?? lastPoint + const x = lastPoint?.x + const y = lastPoint?.y + if (featureId != null) { + onFeatureClickRef.current(featureId, x, y) + } + } else { + // Clicked on empty canvas - clear selection + onFeatureClickRef.current(null) + } + }, + onViewportState: (vp) => { + if (onViewportChangeRef.current && vp) { + onViewportChangeRef.current(vp) + } + }, + } + ) + } catch (err) { + console.warn('Error creating EmbeddingViewMosaic:', err) + } + + return () => { + if (containerRef.current) { + containerRef.current.innerHTML = '' + } + } + }, [brush]) + + // Update category coloring in-place (without recreating the view) + useEffect(() => { + if (!viewRef.current) return + + let categoryColName = null + const HIDDEN_COLOR = darkMode ? "#0a0a0a" : "#fafafa" + let colors = Array(50).fill(HIDDEN_COLOR) + + if (categoryColumn && categoryColumn !== "none") { + const colInfo = categoryColumns?.find(c => c.name === categoryColumn) + if (colInfo) { + if (colInfo.type === 'sequential') { + categoryColName = `${categoryColumn}_bin` + colors = SEQUENTIAL_COLORS + } else if (colInfo.type === 'string') { + categoryColName = `${categoryColumn}_cat` + colors = CATEGORY_COLORS.slice(0, Math.max(colInfo.nUnique, 10)) + // Map colors to match DENSE_RANK order, dim non-selected when filtering + if (hiddenCategories && hiddenCategories.size > 0 && features) { + const allCatNames = [...new Set( + features.map(f => f[categoryColumn]).filter(v => v != null) + )].sort() + colors = colors.map((c, i) => { + const name = allCatNames[i] + if (!name) return c + return !hiddenCategories.has(name) ? HIDDEN_COLOR : c + }) + } + } else { + categoryColName = categoryColumn + colors = CATEGORY_COLORS.slice(0, Math.max(colInfo.nUnique, 10)) + } + } + } + + viewRef.current.update({ + category: categoryColName, + categoryColors: colors, + selection: null, + tooltip: null, + }) + }, [categoryColumn, categoryColumns, hiddenCategories]) + + // Handle resize + useEffect(() => { + const handleResize = () => { + if (viewRef.current && containerRef.current) { + const width = containerRef.current.clientWidth + const height = containerRef.current.clientHeight + viewRef.current.update({ width, height }) + } + } + + const resizeObserver = new ResizeObserver(handleResize) + if (containerRef.current) { + resizeObserver.observe(containerRef.current) + } + + return () => { + resizeObserver.disconnect() + } + }, []) + + return ( +
+ ) +} diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx new file mode 100644 index 0000000000..97ec01e178 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx @@ -0,0 +1,793 @@ +import React, { useState, useEffect, useRef, forwardRef } from 'react' +import SequenceView, { computeAlignInfo } from './SequenceView' +import FeatureDetailPage from './FeatureDetailPage' +import { getRegionLabel } from './utils' + +const styles = { + card: { + background: 'var(--bg-card)', + borderRadius: '8px', + border: '1px solid var(--border)', + flexShrink: 0, + }, + cardHighlighted: { + background: 'var(--bg-card)', + borderRadius: '8px', + border: '2px solid var(--highlight-border)', + flexShrink: 0, + boxShadow: '0 2px 8px var(--highlight-shadow)', + }, + header: { + padding: '12px 14px', + borderBottom: '1px solid var(--border-light)', + cursor: 'pointer', + display: 'flex', + justifyContent: 'space-between', + alignItems: 'flex-start', + gap: '10px', + }, + headerLeft: { + flex: 1, + minWidth: 0, + }, + featureId: { + fontSize: '11px', + color: 'var(--text-tertiary)', + fontFamily: 'monospace', + marginBottom: '2px', + }, + description: { + fontSize: '13px', + fontWeight: '500', + wordBreak: 'break-word', + lineHeight: '1.4', + color: 'var(--text)', + }, + userTitle: { + fontSize: '13px', + fontWeight: '500', + wordBreak: 'break-word', + lineHeight: '1.4', + color: 'var(--accent)', + fontStyle: 'italic', + }, + stats: { + display: 'flex', + gap: '12px', + fontSize: '11px', + color: 'var(--text-secondary)', + flexShrink: 0, + }, + stat: { + display: 'flex', + flexDirection: 'column', + alignItems: 'flex-end', + }, + statLabel: { + color: 'var(--text-muted)', + fontSize: '9px', + textTransform: 'uppercase', + }, + statValue: { + fontFamily: 'monospace', + fontWeight: '500', + }, + expandIcon: { + color: 'var(--text-muted)', + fontSize: '10px', + marginLeft: '6px', + }, + expandedContent: { + padding: '10px 14px', + background: 'var(--bg-card-expanded)', + maxHeight: '900px', + overflowY: 'auto', + }, + sectionHeader: { + fontSize: '10px', + color: 'var(--text-tertiary)', + textTransform: 'uppercase', + marginBottom: '8px', + fontWeight: '500', + }, + example: { + marginBottom: '8px', + padding: '8px 10px', + background: 'var(--bg-example)', + borderRadius: '4px', + border: '1px solid var(--border-light)', + }, + exampleMeta: { + fontSize: '10px', + color: 'var(--text-muted)', + marginBottom: '4px', + fontFamily: 'monospace', + display: 'flex', + justifyContent: 'space-between', + alignItems: 'center', + }, + proteinId: { + color: 'var(--text-heading)', + fontWeight: '700', + }, + annotation: { + color: 'var(--text-secondary)', + fontStyle: 'italic', + marginLeft: '8px', + }, + uniprotLink: { + color: 'var(--link)', + textDecoration: 'none', + fontSize: '11px', + marginLeft: '4px', + opacity: 0.6, + }, + noExamples: { + color: 'var(--text-muted)', + fontSize: '12px', + fontStyle: 'italic', + }, + densityBar: { + width: '50px', + height: '3px', + background: 'var(--density-bar-bg)', + borderRadius: '2px', + overflow: 'hidden', + marginTop: '3px', + }, + densityFill: { + height: '100%', + background: '#76b900', + borderRadius: '2px', + }, + alignBar: { + display: 'flex', + alignItems: 'center', + gap: '6px', + marginBottom: '10px', + fontSize: '10px', + color: '#888', + }, + alignLabel: { + textTransform: 'uppercase', + fontWeight: '500', + }, + alignBtn: { + padding: '2px 8px', + border: '1px solid #ddd', + borderRadius: '3px', + background: '#fff', + cursor: 'pointer', + fontSize: '10px', + color: '#555', + }, + alignBtnActive: { + padding: '2px 8px', + border: '1px solid #76b900', + borderRadius: '3px', + background: '#f0f9e0', + cursor: 'pointer', + fontSize: '10px', + color: '#333', + fontWeight: '600', + }, +} + +const FeatureCard = forwardRef(function FeatureCard({ feature, isHighlighted, forceExpanded, onClick, loadExamples, vocabLogits, featureAnalysis }, ref) { + const [expanded, setExpanded] = useState(false) + const [showDetailPage, setShowDetailPage] = useState(false) + const [examples, setExamples] = useState([]) + const [loadingExamples, setLoadingExamples] = useState(false) + const examplesCacheRef = useRef(null) + const [alignMode, setAlignMode] = useState('start') + const scrollGroupRef = useRef([]) + const [hoveredCodon, setHoveredCodon] = useState(null) + const [editingTitle, setEditingTitle] = useState(false) + const [userTitle, setUserTitle] = useState('') + const inputRef = useRef(null) + + // Load user-provided title from localStorage + useEffect(() => { + const stored = localStorage.getItem(`featureTitle_${feature.feature_id}`) + if (stored) { + setUserTitle(stored) + } + }, [feature.feature_id]) + + // Focus input when editing starts + useEffect(() => { + if (editingTitle && inputRef.current) { + inputRef.current.focus() + inputRef.current.select() + } + }, [editingTitle]) + + // Reset scroll group when alignment changes + useEffect(() => { scrollGroupRef.current = [] }, [alignMode]) + + // If forceExpanded changes to true, expand the card + useEffect(() => { + if (forceExpanded) { + setExpanded(true) + } + }, [forceExpanded]) + + // Lazy-load examples from DuckDB when card is expanded + useEffect(() => { + if (!expanded || !loadExamples || examplesCacheRef.current) return + let cancelled = false + setLoadingExamples(true) + loadExamples(feature.feature_id).then(result => { + if (cancelled) return + examplesCacheRef.current = result + setExamples(result) + setLoadingExamples(false) + }).catch(err => { + if (cancelled) return + console.error('Error loading examples for feature', feature.feature_id, err) + setLoadingExamples(false) + }) + return () => { cancelled = true } + }, [expanded, loadExamples, feature.feature_id]) + + const freq = feature.activation_freq || 0 + const maxAct = feature.max_activation || 0 + const highScoreFrac = feature.high_score_fraction + const variantDelta = feature.mean_variant_delta + const siteDelta = feature.mean_site_delta + const localDelta = feature.mean_local_delta + const clinvarFrac = feature.clinvar_fraction + const phylop = feature.mean_phylop + const gcMean = feature.gc_mean + const trinucEntropy = feature.trinuc_entropy + const geneEntropy = feature.gene_entropy + const geneNUnique = feature.gene_n_unique + const rawDesc = feature.label || feature.description || `Feature ${feature.feature_id}` + const description = rawDesc.toLowerCase().includes('common codons') ? 'Unidentified Feature' : rawDesc + + + const handleClick = () => { + const willExpand = !expanded + // Update UMAP highlight immediately, defer card expansion so it doesn't block + if (onClick) { + onClick(feature.feature_id, willExpand) + } + requestAnimationFrame(() => { + setExpanded(willExpand) + }) + } + + const handleSaveTitle = () => { + if (userTitle.trim()) { + localStorage.setItem(`featureTitle_${feature.feature_id}`, userTitle.trim()) + } else { + localStorage.removeItem(`featureTitle_${feature.feature_id}`) + setUserTitle('') + } + setEditingTitle(false) + } + + const handleCancelEdit = () => { + const stored = localStorage.getItem(`featureTitle_${feature.feature_id}`) + setUserTitle(stored || '') + setEditingTitle(false) + } + + const displayTitle = userTitle || description + + const handleTitleKeyDown = (e) => { + if (e.key === 'Enter') { + handleSaveTitle() + } else if (e.key === 'Escape') { + handleCancelEdit() + } + } + + const exportToCSV = () => { + const lines = [] + + // Feature metadata section + lines.push('=== FEATURE METADATA ===') + lines.push(`Feature ID,${feature.feature_id}`) + lines.push(`Label,${displayTitle}`) + if (userTitle) { + lines.push(`User Title,${userTitle}`) + } + lines.push(`Activation Frequency,${(freq * 100).toFixed(2)}%`) + lines.push(`Max Activation,${maxAct.toFixed(4)}`) + lines.push('') + + // Vocab logits section + const logits = vocabLogits?.[String(feature.feature_id)] + if (logits) { + lines.push('=== TOP PROMOTED CODONS ===') + lines.push('Codon,Amino Acid,Logit Value') + const CODON_AA = { + 'TTT':'F','TTC':'F','TTA':'L','TTG':'L','TCT':'S','TCC':'S','TCA':'S','TCG':'S', + 'TAT':'Y','TAC':'Y','TAA':'*','TAG':'*','TGT':'C','TGC':'C','TGA':'*','TGG':'W', + 'CTT':'L','CTC':'L','CTA':'L','CTG':'L','CCT':'P','CCC':'P','CCA':'P','CCG':'P', + 'CAT':'H','CAC':'H','CAA':'Q','CAG':'Q','CGT':'R','CGC':'R','CGA':'R','CGG':'R', + 'ATT':'I','ATC':'I','ATA':'I','ATG':'M','ACT':'T','ACC':'T','ACA':'T','ACG':'T', + 'AAT':'N','AAC':'N','AAA':'K','AAG':'K','AGT':'S','AGC':'S','AGA':'R','AGG':'R', + 'GTT':'V','GTC':'V','GTA':'V','GTG':'V','GCT':'A','GCC':'A','GCA':'A','GCG':'A', + 'GAT':'D','GAC':'D','GAA':'E','GAG':'E','GGT':'G','GGC':'G','GGA':'G','GGG':'G', + } + ;(logits.top_positive || []).forEach(([codon, val]) => { + lines.push(`${codon},${CODON_AA[codon] || '?'},${val.toFixed(4)}`) + }) + lines.push('') + + lines.push('=== TOP SUPPRESSED CODONS ===') + lines.push('Codon,Amino Acid,Logit Value') + ;(logits.top_negative || []).forEach(([codon, val]) => { + lines.push(`${codon},${CODON_AA[codon] || '?'},${val.toFixed(4)}`) + }) + lines.push('') + } + + // Codon annotations section + const analysis = featureAnalysis?.[String(feature.feature_id)] + if (analysis?.codon_annotations) { + lines.push('=== CODON ANNOTATIONS ===') + const ann = analysis.codon_annotations + if (ann.amino_acid) { + lines.push(`Amino Acid,${ann.amino_acid.aa}`) + lines.push(`AA Frequency,${(ann.amino_acid.fraction * 100).toFixed(1)}%`) + } + if (ann.codon_usage) { + lines.push(`Codon Usage,${ann.codon_usage.bias}`) + } + if (ann.wobble) { + lines.push(`Wobble Position,${ann.wobble.preference}`) + } + if (ann.cpg) { + lines.push(`CpG Enriched,Yes`) + } + if (ann.position) { + lines.push(`Position,${ann.position.label}`) + } + lines.push('') + } + + // GSEA enrichment section + const gseaCsvFields = [ + { key: 'gsea_overall_best', label: 'GSEA Overall Best' }, + { key: 'gsea_GO_Biological_Process', label: 'GSEA GO Biological Process' }, + { key: 'gsea_GO_Molecular_Function', label: 'GSEA GO Molecular Function' }, + { key: 'gsea_GO_Cellular_Component', label: 'GSEA GO Cellular Component' }, + { key: 'gsea_InterPro_Domains', label: 'GSEA InterPro Domains' }, + { key: 'gsea_GO_Slim', label: 'GSEA GO Slim' }, + ] + const gseaLines = gseaCsvFields + .filter(({ key }) => feature[key] && feature[key] !== 'unlabeled') + .map(({ key, label }) => `${label},${feature[key]}`) + if (gseaLines.length > 0) { + lines.push('=== GSEA ENRICHMENT ===') + gseaLines.forEach(l => lines.push(l)) + lines.push('') + } + + // Examples section + if (examples && examples.length > 0) { + lines.push('=== ACTIVATION EXAMPLES ===') + lines.push('Rank,Protein ID,Max Activation,Sequence') + examples.forEach((ex, i) => { + lines.push(`${i + 1},${getRegionLabel(ex) || ''},${ex.max_activation?.toFixed(4) || ''},${ex.sequence || ''}`) + }) + } + + // Generate CSV + const csv = lines.join('\n') + + // Create download link + const filename = `feature_${feature.feature_id}_${displayTitle.replace(/[^a-z0-9]/gi, '_').substring(0, 20)}.csv` + const blob = new Blob([csv], { type: 'text/csv;charset=utf-8;' }) + const link = document.createElement('a') + link.setAttribute('href', URL.createObjectURL(blob)) + link.setAttribute('download', filename) + link.style.visibility = 'hidden' + document.body.appendChild(link) + link.click() + document.body.removeChild(link) + } + + return ( +
+
+
+
Feature #{feature.feature_id}
+ {editingTitle ? ( +
+ setUserTitle(e.target.value)} + onKeyDown={handleTitleKeyDown} + onClick={(e) => e.stopPropagation()} + style={{ + fontSize: '13px', + fontWeight: '500', + padding: '4px 8px', + border: '1px solid #76b900', + borderRadius: '4px', + flex: 1, + }} + /> + + +
+ ) : ( +
+
{displayTitle}
+ { e.stopPropagation(); setEditingTitle(true) }} + style={{ + fontSize: '11px', + color: '#999', + cursor: 'pointer', + padding: '2px 4px', + borderRadius: '3px', + userSelect: 'none', + }} + title="Click to edit title" + > + ✎ + +
+ )} +
+
+
+ Freq + {(freq * 100).toFixed(1)}% +
+
+
+
+
+ Max + {maxAct.toFixed(1)} +
+ {/* v2 roadmap placeholders — populated when real eval pipeline lands. */} +
+ Annotation + — +
+
+ Sensitivity + — +
+
+ Recon Δ + — +
+ {highScoreFrac != null && !isNaN(highScoreFrac) && ( +
+ Hi-Score + 0.6 ? '#d32f2f' : highScoreFrac < 0.4 ? '#388e3c' : '#666' }}> + {(highScoreFrac * 100).toFixed(0)}% + +
+ )} + {variantDelta != null && !isNaN(variantDelta) && ( +
+ Δ Var + 0.5 ? '#1565c0' : '#666' }}> + {variantDelta > 0 ? '+' : ''}{variantDelta.toFixed(2)} + +
+ )} + {siteDelta != null && !isNaN(siteDelta) && ( +
+ Δ Site + 0.5 ? '#7b1fa2' : '#666' }}> + {siteDelta > 0 ? '+' : ''}{siteDelta.toFixed(2)} + +
+ )} + {localDelta != null && !isNaN(localDelta) && ( +
+ Δ Local + 0.5 ? '#00695c' : '#666' }}> + {localDelta > 0 ? '+' : ''}{localDelta.toFixed(2)} + +
+ )} + {clinvarFrac != null && !isNaN(clinvarFrac) && ( +
+ ClinVar + {(clinvarFrac * 100).toFixed(0)}% +
+ )} + {phylop != null && !isNaN(phylop) && ( +
+ PhyloP + {phylop.toFixed(1)} +
+ )} + {gcMean != null && !isNaN(gcMean) && ( +
+ GC + 0.1 ? '#e65100' : '#666' }}> + {(gcMean * 100).toFixed(0)}% + +
+ )} + {trinucEntropy != null && !isNaN(trinucEntropy) && ( +
+ Trinuc H + + {trinucEntropy.toFixed(1)} + +
+ )} + {geneNUnique != null && geneNUnique > 0 && ( +
+ Genes + + {geneNUnique} + +
+ )} + {expanded ? '▼' : '▶'} +
+
+ + {/* Details and export buttons - shown when expanded */} + {expanded && ( +
+ + +
+ )} + + {expanded && ( +
+ {/* Vocabulary logits - all codons grouped by amino acid */} + {vocabLogits && vocabLogits[String(feature.feature_id)] && (() => { + const logits = vocabLogits[String(feature.feature_id)] + const CODON_AA = { + 'TTT':'F','TTC':'F','TTA':'L','TTG':'L','CTT':'L','CTC':'L','CTA':'L','CTG':'L', + 'ATT':'I','ATC':'I','ATA':'I','ATG':'M','GTT':'V','GTC':'V','GTA':'V','GTG':'V', + 'TCT':'S','TCC':'S','TCA':'S','TCG':'S','CCT':'P','CCC':'P','CCA':'P','CCG':'P', + 'ACT':'T','ACC':'T','ACA':'T','ACG':'T','GCT':'A','GCC':'A','GCA':'A','GCG':'A', + 'TAT':'Y','TAC':'Y','TAA':'*','TAG':'*','CAT':'H','CAC':'H','CAA':'Q','CAG':'Q', + 'AAT':'N','AAC':'N','AAA':'K','AAG':'K','GAT':'D','GAC':'D','GAA':'E','GAG':'E', + 'TGT':'C','TGC':'C','TGA':'*','TGG':'W','CGT':'R','CGC':'R','CGA':'R','CGG':'R', + 'AGT':'S','AGC':'S','AGA':'R','AGG':'R','GGT':'G','GGC':'G','GGA':'G','GGG':'G', + } + // Build codon logit map from all entries, excluding stop codons + const codonLogitMap = {} + for (const [codon, val] of (logits.top_positive || [])) { + if (CODON_AA[codon] !== '*') codonLogitMap[codon] = val + } + for (const [codon, val] of (logits.top_negative || [])) { + if (CODON_AA[codon] !== '*') codonLogitMap[codon] = val + } + const maxAbs = Math.max(...Object.values(codonLogitMap).map(Math.abs), 0.001) + // Group by AA, excluding stop codons + const aaGroups = {} + for (const [codon, aa] of Object.entries(CODON_AA)) { + if (aa === '*') continue + if (!aaGroups[aa]) aaGroups[aa] = [] + aaGroups[aa].push(codon) + } + const aaOrder = Object.keys(aaGroups).sort() + return ( +
+
Decoder Logits
+
+ {hoveredCodon && (() => { + const val = codonLogitMap[hoveredCodon] || 0 + const aa = CODON_AA[hoveredCodon] + return ( +
+ {hoveredCodon} ({aa}): {val.toFixed(3)} +
+ ) + })()} +
+ {aaOrder.map(aa => { + const codons = aaGroups[aa] || [] + return ( +
+
{aa}
+
+ {codons.sort().map(codon => { + const val = codonLogitMap[codon] || 0 + const h = Math.max(1, (Math.abs(val) / maxAbs) * 24) + const isHovered = hoveredCodon === codon + const barColor = val === 0 ? '#ccc' : val > 0 ? '#76b900' : '#e57373' + return ( +
setHoveredCodon(codon)} + onMouseLeave={() => setHoveredCodon(null)} + > +
+
+ ) + })} +
+
+ ) + })} +
+
+
+ ) + })()} + + {/* Analysis summary tags */} + {featureAnalysis && featureAnalysis[String(feature.feature_id)] && (() => { + const analysis = featureAnalysis[String(feature.feature_id)] + const tags = [] + const ann = analysis.codon_annotations || {} + + if (ann.amino_acid) tags.push({ label: `AA: ${ann.amino_acid.aa} (${(ann.amino_acid.fraction * 100).toFixed(0)}%)`, color: '#e3f2fd' }) + if (ann.codon_usage) tags.push({ label: `${ann.codon_usage.bias} codons`, color: '#fff3e0' }) + if (ann.wobble) tags.push({ label: `wobble ${ann.wobble.preference}`, color: '#f3e5f5' }) + if (ann.cpg) tags.push({ label: `CpG enriched`, color: '#fce4ec' }) + if (ann.position) tags.push({ label: `N-terminal`, color: '#e8f5e9' }) + + // GSEA enrichment tags + const gseaFields = [ + { key: 'gsea_GO_Biological_Process', prefix: 'GO:BP', color: '#e8eaf6' }, + { key: 'gsea_GO_Molecular_Function', prefix: 'GO:MF', color: '#ede7f6' }, + { key: 'gsea_GO_Cellular_Component', prefix: 'GO:CC', color: '#e0f2f1' }, + { key: 'gsea_InterPro_Domains', prefix: 'InterPro', color: '#fff8e1' }, + { key: 'gsea_GO_Slim', prefix: 'GO Slim', color: '#f1f8e9' }, + ] + for (const { key, prefix, color } of gseaFields) { + const val = feature[key] + if (val && val !== 'unlabeled' && val !== 'other') { + tags.push({ label: `${prefix}: ${val}`, color }) + } + } + + // Codon optimality metrics from annotations + if (ann.cai != null) tags.push({ label: `CAI: ${ann.cai.toFixed(3)}`, color: '#e0f7fa' }) + if (ann.tai != null) tags.push({ label: `tAI: ${ann.tai.toFixed(3)}`, color: '#e0f7fa' }) + if (ann.rscu != null) tags.push({ label: `RSCU: ${ann.rscu.toFixed(2)}`, color: '#e0f7fa' }) + + if (tags.length === 0) return null + return ( +
+ {tags.map((t, i) => ( + {t.label} + ))} +
+ ) + })()} + + {/* Sequence examples */} +
+
Top Activating Sequences
+
+ Align by: + {['start', 'first_activation', 'max_activation'].map(mode => ( + + ))} +
+
+ {loadingExamples ? ( +
+ Loading examples... +
+ ) : examples.length > 0 ? ( + <> + {(() => { + const visibleExamples = examples.slice(0, 6) + const { anchor: alignAnchor, totalLength } = computeAlignInfo(visibleExamples, alignMode) + return visibleExamples.map((ex, i) => ( +
+
+ + {getRegionLabel(ex)} + {ex.best_annotation && ( + {ex.best_annotation} + )} + + max: {ex.max_activation?.toFixed(3) || 'N/A'} +
+ +
+ )) + })()} + + + ) : ( +
No examples available
+ )} +
+ )} + + {showDetailPage && ( + setShowDetailPage(false)} + /> + )} +
+ ) +}) + +export default FeatureCard diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx new file mode 100644 index 0000000000..8e3ec0ce9b --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx @@ -0,0 +1,522 @@ +import React, { useState, useEffect, useRef } from 'react' +import SequenceView, { computeAlignInfo } from './SequenceView' +import { getRegionLabel } from './utils' + +// ── Codon-to-AA lookup for logit chart ────────────────────────────── +const CODON_AA = { + 'TTT':'F','TTC':'F','TTA':'L','TTG':'L','CTT':'L','CTC':'L','CTA':'L','CTG':'L', + 'ATT':'I','ATC':'I','ATA':'I','ATG':'M','GTT':'V','GTC':'V','GTA':'V','GTG':'V', + 'TCT':'S','TCC':'S','TCA':'S','TCG':'S','CCT':'P','CCC':'P','CCA':'P','CCG':'P', + 'ACT':'T','ACC':'T','ACA':'T','ACG':'T','GCT':'A','GCC':'A','GCA':'A','GCG':'A', + 'TAT':'Y','TAC':'Y','TAA':'*','TAG':'*','CAT':'H','CAC':'H','CAA':'Q','CAG':'Q', + 'AAT':'N','AAC':'N','AAA':'K','AAG':'K','GAT':'D','GAC':'D','GAA':'E','GAG':'E', + 'TGT':'C','TGC':'C','TGA':'*','TGG':'W','CGT':'R','CGC':'R','CGA':'R','CGG':'R', + 'AGT':'S','AGC':'S','AGA':'R','AGG':'R','GGT':'G','GGC':'G','GGA':'G','GGG':'G', +} + +// Group codons by amino acid, excluding stop codons +const AA_GROUPS = {} +for (const [codon, aa] of Object.entries(CODON_AA)) { + if (aa === '*') continue + if (!AA_GROUPS[aa]) AA_GROUPS[aa] = [] + AA_GROUPS[aa].push(codon) +} +const AA_ORDER = Object.keys(AA_GROUPS).sort() + +// Color palette for amino acid groups +const AA_COLORS = { + // Nonpolar + 'G': '#e8e8e8', 'A': '#c8c8c8', 'V': '#b0b0b0', 'L': '#a0a0a0', 'I': '#909090', + 'P': '#d0d0a0', 'F': '#c0b0a0', 'W': '#b0a090', 'M': '#a09080', + // Polar + 'S': '#b0d0ff', 'T': '#a0c0f0', 'C': '#90b0e0', 'Y': '#80a0d0', + 'N': '#a0d0b0', 'Q': '#90c0a0', + // Charged + 'D': '#ffb0b0', 'E': '#ffa0a0', 'K': '#b0b0ff', 'R': '#a0a0ff', 'H': '#c0b0ff', +} + +const styles = { + overlay: { + position: 'fixed', + inset: 0, + background: 'rgba(0, 0, 0, 0.5)', + zIndex: 2000, + overflowY: 'auto', + }, + page: { + maxWidth: '960px', + margin: '20px auto', + background: 'var(--bg-card)', + borderRadius: '8px', + boxShadow: '0 4px 24px rgba(0,0,0,0.2)', + color: 'var(--text)', + }, + header: { + padding: '12px 20px', + borderBottom: '1px solid var(--border-light)', + display: 'flex', + justifyContent: 'space-between', + alignItems: 'center', + }, + title: { + fontSize: '14px', + fontWeight: '700', + color: 'var(--text-heading)', + }, + subtitle: { + fontSize: '11px', + color: 'var(--text-secondary)', + }, + closeBtn: { + background: 'none', + border: '1px solid var(--border-input)', + borderRadius: '4px', + padding: '3px 10px', + cursor: 'pointer', + fontSize: '11px', + color: 'var(--text-secondary)', + }, + section: { + padding: '10px 20px', + borderBottom: '1px solid var(--border-light)', + }, + sectionTitle: { + fontSize: '11px', + fontWeight: '600', + marginBottom: '6px', + color: 'var(--text-heading)', + textTransform: 'uppercase', + }, + sectionSubtitle: { + fontSize: '9px', + color: 'var(--text-muted)', + marginBottom: '6px', + }, + statsGrid: { + display: 'grid', + gridTemplateColumns: 'repeat(4, 1fr)', + gap: '8px', + }, + statCard: { + background: 'var(--bg-card-expanded)', + borderRadius: '4px', + padding: '6px 8px', + textAlign: 'center', + }, + statNumber: { + fontSize: '14px', + fontWeight: '700', + color: 'var(--text-heading)', + }, + statLabel: { + fontSize: '8px', + color: 'var(--text-tertiary)', + textTransform: 'uppercase', + marginTop: '2px', + }, + tag: { + display: 'inline-block', + padding: '3px 8px', + borderRadius: '4px', + fontSize: '11px', + fontWeight: '500', + marginRight: '6px', + marginBottom: '4px', + }, + example: { + marginBottom: '6px', + padding: '6px 8px', + background: 'var(--bg-example)', + borderRadius: '4px', + border: '1px solid var(--border-light)', + }, + exampleMeta: { + fontSize: '10px', + color: 'var(--text-secondary)', + marginBottom: '4px', + fontFamily: 'monospace', + display: 'flex', + justifyContent: 'space-between', + }, +} + + +// ── Vocab Logit Chart Component ───────────────────────────────────── + +function VocabLogitChart({ logits }) { + if (!logits) return null + + // Build codon logit map, excluding stop codons (universally suppressed, not informative) + const codonLogitMap = {} + for (const [codon, val] of logits.top_positive) { + if (CODON_AA[codon] !== '*') codonLogitMap[codon] = val + } + for (const [codon, val] of logits.top_negative) { + if (CODON_AA[codon] !== '*') codonLogitMap[codon] = val + } + + const maxAbs = Math.max(...Object.values(codonLogitMap).map(Math.abs), 0.001) + + return ( +
+
+ {AA_ORDER.map(aa => { + const codons = AA_GROUPS[aa] || [] + const aaColor = AA_COLORS[aa] || 'var(--border-light)' + return ( +
+
+ {aa} +
+
+ {codons.sort().map(codon => { + const val = codonLogitMap[codon] || 0 + const h = Math.max(2, (Math.abs(val) / maxAbs) * 34) + const isPos = val > 0 + return ( +
+
+ + {codon} + +
+ ) + })} +
+
+ ) + })} +
+
+ Promoted + Suppressed +
+
+ ) +} + + +// ── Codon Annotations Component ───────────────────────────────────── + +function CodonAnnotations({ annotations }) { + if (!annotations || Object.keys(annotations).length === 0) { + return
No significant annotations detected
+ } + + const items = [] + + if (annotations.amino_acid) { + items.push({ + label: 'Amino Acid', + value: `${annotations.amino_acid.aa} (${(annotations.amino_acid.fraction * 100).toFixed(0)}% of activations)`, + color: '#e3f2fd', + }) + } + if (annotations.codon_usage) { + items.push({ + label: 'Codon Usage', + value: `Prefers ${annotations.codon_usage.bias} codons (${(annotations.codon_usage.fraction * 100).toFixed(0)}%)`, + color: '#fff3e0', + }) + } + if (annotations.wobble) { + items.push({ + label: 'Wobble Position', + value: `${annotations.wobble.preference} preference (${(annotations.wobble.fraction * 100).toFixed(0)}%)`, + color: '#f3e5f5', + }) + } + if (annotations.cpg) { + items.push({ + label: 'CpG Context', + value: `Enriched (${(annotations.cpg.enrichment * 100).toFixed(0)}% of activations at CpG boundaries)`, + color: '#fce4ec', + }) + } + if (annotations.position) { + items.push({ + label: 'Gene Position', + value: `N-terminal enriched (${annotations.position.enrichment.toFixed(1)}x over expected)`, + color: '#e8f5e9', + }) + } + + return ( +
+ {items.map((item, i) => ( +
+ + {item.label}: + {' '} + {item.value} +
+ ))} +
+ ) +} + + +// ── Feature Metrics Component ─────────────────────────────────────── + +function FeatureMetrics({ feature }) { + const metrics = [ + { key: 'mean_variant_1bcdwt', label: 'Mean Variant (1B CDWT)' }, + { key: 'high_score_fraction', label: 'High Score Fraction' }, + { key: 'clinvar_fraction', label: 'ClinVar Fraction' }, + { key: 'mean_phylop', label: 'Mean PhyloP' }, + { key: 'mean_variant_delta', label: 'Mean Variant Delta' }, + { key: 'mean_site_delta', label: 'Mean Site Delta' }, + { key: 'mean_local_delta', label: 'Mean Local Delta' }, + { key: 'gc_mean', label: 'GC Mean' }, + { key: 'gc_std', label: 'GC Std' }, + { key: 'trinuc_entropy', label: 'Trinuc Entropy' }, + { key: 'gene_entropy', label: 'Gene Entropy' }, + { key: 'gene_n_unique', label: 'Gene N Unique' }, + ] + + const available = metrics.filter(m => feature[m.key] != null && !isNaN(feature[m.key])) + if (available.length === 0) return null + + return ( +
+ {available.map(m => ( +
+
+ {typeof feature[m.key] === 'number' + ? Math.abs(feature[m.key]) >= 100 ? feature[m.key].toFixed(0) + : Math.abs(feature[m.key]) >= 1 ? feature[m.key].toFixed(2) + : feature[m.key].toFixed(4) + : feature[m.key]} +
+
{m.label}
+
+ ))} +
+ ) +} + + +// ── Main Detail Page ──────────────────────────────────────────────── + +export default function FeatureDetailPage({ feature, examples, vocabLogits, featureAnalysis, onClose }) { + const [alignMode, setAlignMode] = useState('max_activation') + const scrollGroupRef = useRef(null) + + const fid = String(feature.feature_id) + const logits = vocabLogits ? vocabLogits[fid] : null + const analysis = featureAnalysis ? featureAnalysis[fid] : null + + const freq = feature.activation_freq || 0 + const maxAct = feature.max_activation || 0 + const description = feature.description || `Feature ${feature.feature_id}` + + // Close on Escape + useEffect(() => { + const handleKey = (e) => { if (e.key === 'Escape') onClose() } + document.addEventListener('keydown', handleKey) + return () => document.removeEventListener('keydown', handleKey) + }, [onClose]) + + const visibleExamples = (examples || []).slice(0, 6) + const { anchor: alignAnchor, totalLength } = computeAlignInfo(visibleExamples, alignMode) + + return ( +
{ if (e.target === e.currentTarget) onClose() }}> +
+ + {/* Header + stats in one row */} +
+
+
Feature #{feature.feature_id} {description}
+
+
+
+ freq: {(freq * 100).toFixed(1)}% + max: {maxAct.toFixed(1)} +
+ +
+
+ + {/* Feature Metrics from color-by columns */} +
+
Feature Metrics
+
+ Variant analysis and sequence composition metrics for this feature. +
+ +
+ + {/* Vocabulary Logits */} +
+
Decoder Logits — Promoted / Suppressed Codons
+
+ Projection of this feature's decoder weight through the Encodon LM head, with the mean logit vector subtracted. + This mean-centering removes the model's shared baseline bias (e.g. toward common codons like GCC), so values reflect what this feature specifically promotes (green) or suppresses (red) relative to the average feature. + Stop codons (TAA/TAG/TGA) are excluded — they are uniformly suppressed across all features since the model was trained on coding sequences. +
+ +
+ + {/* Gene-Level GSEA Enrichment */} + {(() => { + const gseaFields = [ + { key: 'gsea_GO_Biological_Process', label: 'GO Biological Process' }, + { key: 'gsea_GO_Molecular_Function', label: 'GO Molecular Function' }, + { key: 'gsea_GO_Cellular_Component', label: 'GO Cellular Component' }, + { key: 'gsea_InterPro_Domains', label: 'InterPro Domains' }, + { key: 'gsea_GO_Slim', label: 'GO Slim' }, + ] + const gseaEntries = gseaFields + .map(({ key, label }) => ({ label, value: feature[key] })) + .filter(e => e.value && e.value !== 'unlabeled' && e.value !== 'other') + const overallBest = feature.gsea_overall_best + if (gseaEntries.length === 0 && (!overallBest || overallBest === 'unlabeled')) return null + return ( +
+
Gene-Level Enrichment (GSEA)
+
+ Genes ranked by activation strength, tested against GO and InterPro databases. +
+ {overallBest && overallBest !== 'unlabeled' && ( +
+ Best: {overallBest} +
+ )} +
+ {gseaEntries.map(({ label, value }) => ( +
+
+ {label} +
+
{value}
+
+ ))} +
+
+ ) + })()} + + {/* Codon Annotations */} +
+
Codon-Level Annotations
+
+ Computed per-codon properties correlated with this feature's activations. +
+ +
+ + {/* Top Activating Sequences */} +
+
+
Top Activating Sequences
+
+ {['start', 'first_activation', 'max_activation'].map(mode => ( + + ))} +
+
+ + {visibleExamples.length > 0 ? ( + <> + {visibleExamples.map((ex, i) => ( +
+
+ + {getRegionLabel(ex)} + + max: {ex.max_activation?.toFixed(3)} +
+ +
+ ))} + + ) : ( +
No examples loaded
+ )} +
+ + {/* v2 roadmap placeholders — populated when annotation + conservation pipelines land. */} +
+
+ Annotations +
+
+ Annotation overlay (RefSeq, Rfam, JASPAR) — coming in v2 +
+
+ +
+
+ Conservation +
+
+ Conservation track (phyloP) — coming in v2 +
+
+ +
+
+ ) +} diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureList.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureList.jsx new file mode 100644 index 0000000000..26cd6c2457 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureList.jsx @@ -0,0 +1,83 @@ +import React, { memo } from 'react' +import FeatureCard from './FeatureCard' + +const styles = { + featureList: { + flex: 1, + overflowY: 'auto', + overflowX: 'hidden', + display: 'flex', + flexDirection: 'column', + gap: '10px', + paddingRight: '8px', + minHeight: 0, + }, +} + +function FeatureListComponent({ + filteredFeatures, + displayedCardCount, + clickedFeatureId, + features, + cardResetKey, + handleCardClick, + loadExamples, + vocabLogits, + featureAnalysis, + featureListRef, + endOfListRef, + featureRefs, +}) { + const visibleFeatures = filteredFeatures.slice(0, displayedCardCount) + const clickedIsVisible = clickedFeatureId != null && + visibleFeatures.some(f => Number(f.feature_id) === Number(clickedFeatureId)) + const clickedFeature = clickedFeatureId != null && !clickedIsVisible + ? features.find(f => Number(f.feature_id) === Number(clickedFeatureId)) + : null + + return ( +
+ {/* Only render clicked feature at top if NOT already in visible list */} + {clickedFeature && ( + { featureRefs.current[clickedFeature.feature_id] = el }} + feature={clickedFeature} + isHighlighted={true} + forceExpanded={true} + onClick={handleCardClick} + loadExamples={loadExamples} + vocabLogits={vocabLogits} + featureAnalysis={featureAnalysis} + /> + )} + {visibleFeatures.map(feature => ( + { featureRefs.current[feature.feature_id] = el }} + feature={feature} + isHighlighted={Number(clickedFeatureId) === Number(feature.feature_id)} + forceExpanded={Number(clickedFeatureId) === Number(feature.feature_id)} + onClick={handleCardClick} + loadExamples={loadExamples} + vocabLogits={vocabLogits} + featureAnalysis={featureAnalysis} + /> + ))} + {/* Sentinel element for infinite scroll detection */} +
+ {displayedCardCount < filteredFeatures.length && ( +
+ Scroll to load more... ({visibleFeatures.length} of {filteredFeatures.length}) +
+ )} + {filteredFeatures.length === 0 && clickedFeatureId == null && ( +
+ No features match your selection. +
+ )} +
+ ) +} + +export default memo(FeatureListComponent) diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/Histogram.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/Histogram.jsx new file mode 100644 index 0000000000..553330862d --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/Histogram.jsx @@ -0,0 +1,85 @@ +import React, { useEffect, useRef } from 'react' +import * as vg from '@uwdata/vgplot' + +const FILL_COLOR = "#76b900" + +function injectAxisLine(plot, marginLeft, marginRight, marginBottom, height, axisColor) { + const svg = plot.tagName === 'svg' ? plot : plot.querySelector?.('svg') + if (!svg) return + // Remove any previously injected line + svg.querySelectorAll('.x-axis-line').forEach(el => el.remove()) + const svgWidth = svg.getAttribute('width') || svg.clientWidth + const line = document.createElementNS('http://www.w3.org/2000/svg', 'line') + line.classList.add('x-axis-line') + line.setAttribute('x1', marginLeft) + line.setAttribute('x2', svgWidth - marginRight) + line.setAttribute('y1', height - marginBottom) + line.setAttribute('y2', height - marginBottom) + line.setAttribute('stroke', axisColor) + line.setAttribute('stroke-width', '1') + svg.appendChild(line) +} + +export default function Histogram({ brush, column, label }) { + const containerRef = useRef(null) + + useEffect(() => { + if (!containerRef.current || !brush) return + + // Clear previous content + containerRef.current.innerHTML = '' + + const bgColor = getComputedStyle(document.documentElement).getPropertyValue('--density-bar-bg').trim() || '#e0e0e0' + const axisColor = getComputedStyle(document.documentElement).getPropertyValue('--text-tertiary').trim() || '#888' + const width = containerRef.current.clientWidth - 20 + const height = 50 + const marginLeft = 45 + const marginBottom = 20 + const marginRight = 10 + const marginTop = 5 + + const plot = vg.plot( + // Background histogram: full data (no filterBy) + vg.rectY( + vg.from("features"), + { x: vg.bin(column), y: vg.count(), fill: bgColor, inset: 1 } + ), + // Foreground histogram: filtered data + vg.rectY( + vg.from("features", { filterBy: brush }), + { x: vg.bin(column), y: vg.count(), fill: FILL_COLOR, inset: 1 } + ), + vg.intervalX({ as: brush }), + vg.xLabel(null), + vg.yLabel(null), + vg.width(width), + vg.height(height), + vg.marginLeft(marginLeft), + vg.marginBottom(marginBottom), + vg.marginTop(marginTop), + vg.marginRight(marginRight) + ) + + containerRef.current.appendChild(plot) + + // Inject axis line into the SVG directly (immune to container resize) + // Use a short delay to ensure the SVG is rendered + const timer = setTimeout(() => { + injectAxisLine(plot, marginLeft, marginRight, marginBottom, height, axisColor) + }, 50) + + return () => { + clearTimeout(timer) + if (containerRef.current) { + containerRef.current.innerHTML = '' + } + } + }, [brush, column, label]) + + return ( +
+ ) +} diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/InfoButton.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/InfoButton.jsx new file mode 100644 index 0000000000..40184d0ef6 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/InfoButton.jsx @@ -0,0 +1,78 @@ +import React, { useState, useEffect, useRef } from 'react' +import { createPortal } from 'react-dom' + +export default function InfoButton({ text }) { + const [open, setOpen] = useState(false) + const wrapperRef = useRef(null) + const buttonRef = useRef(null) + const [pos, setPos] = useState(null) + + useEffect(() => { + if (!open) return + const handleClick = (e) => { + if (wrapperRef.current && !wrapperRef.current.contains(e.target)) { + setOpen(false) + } + } + document.addEventListener('mousedown', handleClick) + return () => document.removeEventListener('mousedown', handleClick) + }, [open]) + + useEffect(() => { + if (open && buttonRef.current) { + const rect = buttonRef.current.getBoundingClientRect() + setPos({ + top: rect.top - 8, + left: rect.left + rect.width / 2, + }) + } + }, [open]) + + return ( + + setOpen(o => !o)} + style={{ + display: 'inline-flex', + alignItems: 'center', + justifyContent: 'center', + width: '15px', + height: '15px', + borderRadius: '50%', + border: '1px solid var(--border-input)', + fontSize: '10px', + fontWeight: '600', + color: 'var(--text-tertiary)', + cursor: 'pointer', + userSelect: 'none', + lineHeight: 1, + }} + > + i + + {open && pos && createPortal( +
+ {text} +
, + document.body + )} + + ) +} diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/RegionDetailModal.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/RegionDetailModal.jsx new file mode 100644 index 0000000000..d72dc41358 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/RegionDetailModal.jsx @@ -0,0 +1,157 @@ +import React, { useEffect } from 'react' +import ReactDOM from 'react-dom' +import SequenceView from './SequenceView' +import { getRegionLabel } from './utils' + +const styles = { + backdrop: { + position: 'fixed', + inset: 0, + background: 'rgba(0,0,0,0.5)', + zIndex: 9999, + display: 'flex', + alignItems: 'center', + justifyContent: 'center', + }, + modal: { + background: '#fff', + borderRadius: '12px', + width: '90vw', + maxWidth: '1000px', + maxHeight: '85vh', + display: 'flex', + flexDirection: 'column', + overflow: 'hidden', + boxShadow: '0 20px 60px rgba(0,0,0,0.3)', + position: 'relative', + }, + closeBtn: { + position: 'absolute', + top: '12px', + right: '12px', + zIndex: 10, + background: 'rgba(255,255,255,0.9)', + border: '1px solid #ddd', + borderRadius: '50%', + width: '32px', + height: '32px', + display: 'flex', + alignItems: 'center', + justifyContent: 'center', + cursor: 'pointer', + fontSize: '16px', + color: '#555', + }, + body: { + flex: 1, + padding: '32px', + overflowY: 'auto', + display: 'flex', + flexDirection: 'column', + gap: '20px', + }, + header: { + display: 'flex', + alignItems: 'center', + gap: '8px', + flexWrap: 'wrap', + }, + regionLabel: { + fontSize: '18px', + fontWeight: '700', + fontFamily: 'monospace', + color: '#222', + }, + statsRow: { + display: 'flex', + gap: '20px', + flexWrap: 'wrap', + }, + statBox: { + padding: '10px 14px', + background: '#f9fafb', + borderRadius: '8px', + border: '1px solid #eee', + }, + statLabel: { + fontSize: '10px', + color: '#888', + textTransform: 'uppercase', + marginBottom: '2px', + }, + statValue: { + fontSize: '14px', + fontWeight: '600', + fontFamily: 'monospace', + color: '#333', + }, + sectionLabel: { + fontSize: '11px', + color: '#888', + textTransform: 'uppercase', + fontWeight: '500', + }, + sequenceBox: { + background: '#fafafa', + border: '1px solid #eee', + borderRadius: '8px', + padding: '12px', + maxHeight: '300px', + overflowY: 'auto', + }, +} + +export default function RegionDetailModal({ region, onClose }) { + useEffect(() => { + const handleKey = (e) => { if (e.key === 'Escape') onClose() } + document.addEventListener('keydown', handleKey) + return () => document.removeEventListener('keydown', handleKey) + }, [onClose]) + + const label = getRegionLabel(region) + const sequenceLength = (region.sequence || '').length + + const modal = ( +
+
e.stopPropagation()}> +
x
+ +
+
+ {label} +
+ +
+
+
Max Activation
+
{(region.max_activation || 0).toFixed(4)}
+
+
+
Sequence Length
+
{sequenceLength} bp
+
+ {region.best_annotation && ( +
+
Annotation
+
{region.best_annotation}
+
+ )} +
+ +
+
Sequence (activation highlighted)
+
+ +
+
+
+
+
+ ) + + return ReactDOM.createPortal(modal, document.body) +} diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/SequenceView.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/SequenceView.jsx new file mode 100644 index 0000000000..5583180fc5 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/SequenceView.jsx @@ -0,0 +1,267 @@ +import React, { useState, useEffect, useRef } from 'react' +import { parseBases } from './utils' + +function activationColorHex(value, maxValue) { + if (maxValue <= 0 || value <= 0) return 'transparent' + const n = Math.min(value / maxValue, 1) + const r = Math.round(255 - n * 137) + const g = Math.round(255 - n * 70) + const b = Math.round(255 * (1 - n)) + const toHex = (c) => c.toString(16).padStart(2, '0') + return `#${toHex(r)}${toHex(g)}${toHex(b)}` +} + +const BASE_WIDTH = 12 + +const styles = { + container: { + fontFamily: 'Monaco, Menlo, "Courier New", monospace', + fontSize: '11px', + lineHeight: '1.2', + overflowX: 'auto', + position: 'relative', + }, + baseRow: { + display: 'inline-flex', + whiteSpace: 'nowrap', + }, + baseBlock: { + display: 'inline-flex', + flexDirection: 'column', + alignItems: 'center', + cursor: 'default', + borderRadius: '2px', + padding: '1px 1px', + marginRight: '0px', + minWidth: `${BASE_WIDTH}px`, + }, + padBlock: { + display: 'inline-flex', + flexDirection: 'column', + alignItems: 'center', + borderRadius: '2px', + padding: '1px 1px', + marginRight: '0px', + minWidth: `${BASE_WIDTH}px`, + background: 'var(--density-bar-bg)', + }, + padText: { + fontSize: '10px', + color: 'var(--text-muted)', + }, + baseText: { + fontSize: '10px', + letterSpacing: '0.5px', + color: 'var(--text)', + }, + idxText: { + fontSize: '7px', + color: 'var(--text-tertiary)', + marginTop: '0px', + lineHeight: '1', + }, + tooltip: { + position: 'fixed', + background: 'var(--bg-card)', + color: 'var(--text)', + border: '1px solid var(--border)', + padding: '4px 8px', + borderRadius: '4px', + fontSize: '10px', + fontFamily: 'monospace', + zIndex: 1000, + pointerEvents: 'none', + whiteSpace: 'nowrap', + }, +} + +// Show index under every Nth base to keep the row scannable +const INDEX_INTERVAL = 10 + +export default function SequenceView({ + sequence, activations, maxActivation, + alignMode, alignAnchor, totalLength, + scrollGroupRef, +}) { + const [tooltip, setTooltip] = useState(null) + const scrollRef = useRef(null) + const anchorRef = useRef(null) + + const bases = parseBases(sequence) + const acts = activations ? activations.slice(0, bases.length) : [] + const maxAct = maxActivation || Math.max(...acts, 0.001) + + // Compute local anchor index + let localAnchor = 0 + if (alignMode === 'first_activation') { + localAnchor = acts.findIndex(a => a > 0) + if (localAnchor < 0) localAnchor = 0 + } else if (alignMode === 'max_activation') { + let maxVal = -1 + acts.forEach((a, i) => { if (a > maxVal) { maxVal = a; localAnchor = i } }) + } + + // Padding + const isAligned = alignMode && alignMode !== 'start' && alignAnchor != null + const leftPad = isAligned ? Math.max(0, alignAnchor - localAnchor) : 0 + const rightPad = (totalLength != null) + ? Math.max(0, totalLength - leftPad - bases.length) + : 0 + + // Scroll to anchor when alignMode changes + useEffect(() => { + if (isAligned && anchorRef.current && scrollRef.current) { + anchorRef.current.scrollIntoView({ behavior: 'instant', inline: 'center', block: 'nearest' }) + } + }, [alignMode, alignAnchor]) + + // Synchronized scrolling across sequences in the same card + useEffect(() => { + const el = scrollRef.current + if (!el || !scrollGroupRef) return + + if (!scrollGroupRef.current) scrollGroupRef.current = [] + const group = scrollGroupRef.current + if (!group.includes(el)) group.push(el) + + let isSyncing = false + const handleScroll = () => { + if (isSyncing) return + isSyncing = true + const scrollLeft = el.scrollLeft + for (const other of group) { + if (other !== el) other.scrollLeft = scrollLeft + } + isSyncing = false + } + + el.addEventListener('scroll', handleScroll) + return () => { + el.removeEventListener('scroll', handleScroll) + const idx = group.indexOf(el) + if (idx !== -1) group.splice(idx, 1) + } + }, [scrollGroupRef]) + + if (!sequence || sequence.length === 0) { + return No sequence + } + + const handleMouseEnter = (e, base, idx, act) => { + setTooltip({ + x: e.clientX + 10, + y: e.clientY - 25, + text: `${base} pos ${idx + 1} — activation: ${act.toFixed(4)}`, + }) + } + + const handleMouseMove = (e) => { + if (tooltip) { + setTooltip((prev) => prev ? { ...prev, x: e.clientX + 10, y: e.clientY - 25 } : null) + } + } + + const handleMouseLeave = () => { + setTooltip(null) + } + + const shouldShowIdx = (idx) => (idx + 1) % INDEX_INTERVAL === 0 || idx === 0 + + return ( +
+
+ {/* Left padding */} + {Array.from({ length: leftPad }, (_, i) => ( + + · +   + + ))} + + {/* Actual bases */} + {bases.map((base, idx) => { + const act = acts[idx] || 0 + const bg = activationColorHex(act, maxAct) + const isAnchor = isAligned && idx === localAnchor + const hasActivation = act > 0 + const activeTextColor = hasActivation ? '#000' : undefined + return ( + handleMouseEnter(e, base, idx, act)} + onMouseMove={handleMouseMove} + onMouseLeave={handleMouseLeave} + > + {base} + {shouldShowIdx(idx) ? idx + 1 : ' '} + + ) + })} + + {/* Right padding */} + {Array.from({ length: rightPad }, (_, i) => ( + + · +   + + ))} +
+ {tooltip && ( + + {tooltip.text} + + )} +
+ ) +} + +/** + * Compute alignment info for a set of examples — same logic as the codonfm + * version, just operating on per-base activation arrays rather than per-codon. + */ +export function computeAlignInfo(examples, alignMode) { + if (!examples || examples.length === 0) return { anchor: 0, totalLength: 0 } + + if (alignMode === 'start') { + const maxLen = Math.max(...examples.map(ex => (ex.activations || []).length)) + return { anchor: 0, totalLength: maxLen } + } + + let maxAnchor = 0 + for (const ex of examples) { + const acts = ex.activations || [] + let anchor = 0 + if (alignMode === 'first_activation') { + anchor = acts.findIndex(a => a > 0) + if (anchor < 0) anchor = 0 + } else if (alignMode === 'max_activation') { + let maxVal = -1 + acts.forEach((a, i) => { if (a > maxVal) { maxVal = a; anchor = i } }) + } + if (anchor > maxAnchor) maxAnchor = anchor + } + + let totalLength = 0 + for (const ex of examples) { + const acts = ex.activations || [] + let anchor = 0 + if (alignMode === 'first_activation') { + anchor = acts.findIndex(a => a > 0) + if (anchor < 0) anchor = 0 + } else if (alignMode === 'max_activation') { + let maxVal = -1 + acts.forEach((a, i) => { if (a > maxVal) { maxVal = a; anchor = i } }) + } + const leftPad = maxAnchor - anchor + const thisTotal = leftPad + acts.length + if (thisTotal > totalLength) totalLength = thisTotal + } + + return { anchor: maxAnchor, totalLength } +} diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/index.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/index.jsx new file mode 100644 index 0000000000..38853006d7 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/index.jsx @@ -0,0 +1,9 @@ +import React from 'react' +import ReactDOM from 'react-dom/client' +import App from './App' + +ReactDOM.createRoot(document.getElementById('root')).render( + + + +) diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/utils.js b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/utils.js new file mode 100644 index 0000000000..936d971eae --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/utils.js @@ -0,0 +1,22 @@ +/** + * Build a human-readable label for a genomic region example. + * Expects an object with sequence_id, start, end fields. Falls back + * gracefully if any of those are missing. + */ +export function getRegionLabel(example) { + if (!example) return '' + const sid = example.sequence_id || example.protein_id || '' + if (example.start != null && example.end != null) { + return `${sid}:${example.start}-${example.end}` + } + return sid +} + +/** + * Parse a DNA sequence into an array of single-base tokens. + * No codon framing — each base is rendered independently. + */ +export function parseBases(sequence) { + if (!sequence) return [] + return sequence.split('') +} diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/vite.config.js b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/vite.config.js new file mode 100644 index 0000000000..6df23efee3 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/vite.config.js @@ -0,0 +1,13 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +export default defineConfig({ + plugins: [react()], + root: '.', + build: { + outDir: 'dist', + }, + server: { + port: 5176, + }, +}) diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py new file mode 100644 index 0000000000..dc7f72e1cc --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py @@ -0,0 +1,239 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-Apache2 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Generate synthetic features.json + features_atlas.parquet for the evo2 SAE mockup dashboard. + +Run once, commit outputs as fixtures. No real SAE involved — this is a v1 demo of the +visualization shell. The data shape is the contract the real eval pipeline will target later. +""" + +import argparse +import json +from pathlib import Path + +import numpy as np +import pandas as pd + + +# Plausible biological labels (20 features) — visible motifs in the synthetic sequences. +LABELS = [ + "exon-start motif", + "tRNA acceptor stem", + "intergenic GC-rich", + "stop codon context", + "ribosome binding site", + "promoter -10 box", + "CpG island", + "splice donor", + "polyA signal", + "start codon ATG context", + "transposon repeat", + "rRNA conserved region", + "operon intergenic", + "frameshift-sensitive region", + "high-conservation coding", + "intron branch point", + "TF binding motif", + "phage integrase region", + "origin of replication", + "Shine-Dalgarno sequence", +] + +# Plausible accessions to rotate across examples. +SEQ_IDS = ["NC_000913.3", "NC_002695.2", "chr1", "chr17"] + +# Central motifs to splice into each feature's top-activating windows. +# Doesn't need biological rigor — just makes features visually distinguishable in the demo. +CENTRAL_MOTIFS = { + "exon-start motif": "AGGTAAGT", + "tRNA acceptor stem": "CCCGGGT", + "intergenic GC-rich": "GCGCGCGC", + "stop codon context": "TAATAATAA", + "ribosome binding site": "AGGAGG", + "promoter -10 box": "TATAAT", + "CpG island": "CGCGCGCG", + "splice donor": "GTAAGT", + "polyA signal": "AATAAA", + "start codon ATG context": "ATGGCC", + "transposon repeat": "TTAATTAA", + "rRNA conserved region": "GUCAGCUGGUC".replace("U", "T"), + "operon intergenic": "AAATTT", + "frameshift-sensitive region": "AAAAAAA", + "high-conservation coding": "GCAGCAGCA", + "intron branch point": "TACTAAC", + "TF binding motif": "TGACTCA", + "phage integrase region": "GCTAGGTGT", + "origin of replication": "ATCGATCG", + "Shine-Dalgarno sequence": "AGGAGGT", +} + + +def _random_dna(rng: np.random.Generator, length: int) -> str: + """Generate a length-N DNA string by uniform-sampling A/C/G/T.""" + return "".join(rng.choice(list("ACGT"), size=length)) + + +def _make_example(rng: np.random.Generator, label: str, feature_max: float, window: int = 200) -> dict: + """Build one top-activating example: 200bp window with a central motif + a gaussian activation bump.""" + seq = list(_random_dna(rng, window)) + + # Splice the feature's central motif into the middle ± a few bp jitter. + motif = CENTRAL_MOTIFS[label] + center = window // 2 + int(rng.integers(-5, 6)) + motif_start = center - len(motif) // 2 + for i, base in enumerate(motif): + pos = motif_start + i + if 0 <= pos < window: + seq[pos] = base + + # Activation bump: gaussian centered in [80, 120], sigma ~= 8 bp, peak = feature_max * U(0.5, 1.0). + bump_center = int(rng.integers(80, 121)) + sigma = 8.0 + peak = float(feature_max * rng.uniform(0.5, 1.0)) + positions = np.arange(window) + activations = peak * np.exp(-((positions - bump_center) ** 2) / (2 * sigma**2)) + activations[activations < 0.01] = 0.0 # zero out the tails so the JSON is sparse-ish + + seq_id = SEQ_IDS[int(rng.integers(0, len(SEQ_IDS)))] + start = int(rng.integers(1, 5_000_001)) + + return { + "sequence_id": seq_id, + "start": start, + "end": start + window, + "sequence": "".join(seq), + "activations": [round(float(a), 3) for a in activations], + "max_activation": round(float(activations.max()), 4), + "max_activation_position": int(activations.argmax()), + } + + +def _make_features(rng: np.random.Generator) -> list[dict]: + """Build the 20 synthetic feature entries for features.json.""" + features = [] + for fid, label in enumerate(LABELS): + activation_freq = float(np.exp(rng.uniform(np.log(0.001), np.log(0.1)))) + max_activation = float(rng.uniform(5.0, 30.0)) + examples = [_make_example(rng, label, max_activation) for _ in range(30)] + + features.append( + { + "feature_id": fid, + "description": label, + "label": label, + "activation_freq": round(activation_freq, 6), + "max_activation": round(max_activation, 4), + "top_positive_logits": [], + "top_negative_logits": [], + "examples": examples, + } + ) + return features + + +def _make_atlas(rng: np.random.Generator, features: list[dict]) -> pd.DataFrame: + """Build features_atlas.parquet — synthetic UMAP coords with 4 visible clusters of 5 features each.""" + n_clusters = 4 + cluster_centers = rng.uniform(-5.0, 5.0, size=(n_clusters, 2)) + coords = [] + for fid in range(len(features)): + cluster_idx = fid // (len(features) // n_clusters) + center = cluster_centers[cluster_idx] + xy = center + rng.normal(0, 0.5, size=2) + coords.append(xy) + coords = np.array(coords) + + return pd.DataFrame( + { + "feature_id": [f["feature_id"] for f in features], + "x": coords[:, 0].round(4), + "y": coords[:, 1].round(4), + "label": [f["label"] for f in features], + "activation_freq": [f["activation_freq"] for f in features], + "log_frequency": [round(float(np.log10(f["activation_freq"])), 4) for f in features], + "max_activation": [f["max_activation"] for f in features], + "cluster": [fid // (len(features) // n_clusters) for fid in range(len(features))], + } + ) + + +def _make_examples_table(features: list[dict]) -> pd.DataFrame: + """Flatten per-feature examples into a long table for feature_examples.parquet. + + One row per (feature_id, example_rank). The dashboard lazy-loads these via DuckDB. + """ + rows = [] + for feature in features: + for rank, ex in enumerate(feature["examples"]): + rows.append( + { + "feature_id": feature["feature_id"], + "example_rank": rank, + "sequence_id": ex["sequence_id"], + "start": ex["start"], + "end": ex["end"], + "sequence": ex["sequence"], + "activations": ex["activations"], + "max_activation": ex["max_activation"], + "max_activation_position": ex["max_activation_position"], + "best_annotation": None, + } + ) + return pd.DataFrame(rows) + + +def main(): + """Generate synthetic parquet fixtures (atlas + metadata + examples) for the mockup dashboard.""" + p = argparse.ArgumentParser() + p.add_argument( + "--output-dir", + type=Path, + default=Path(__file__).resolve().parent.parent / "evo2_dashboard_mockup" / "public", + help="Where to write the three parquet fixtures", + ) + p.add_argument( + "--write-json", + action="store_true", + help="Also write features.json (only useful if you point the dashboard at the legacy JSON path)", + ) + p.add_argument("--seed", type=int, default=42) + args = p.parse_args() + + rng = np.random.default_rng(args.seed) + args.output_dir.mkdir(parents=True, exist_ok=True) + + features = _make_features(rng) + + atlas = _make_atlas(rng, features) + atlas.to_parquet(args.output_dir / "features_atlas.parquet", index=False) + # feature_metadata is the same shape as the atlas for the mockup — the dashboard + # loads them as two tables but the queried columns are identical. + atlas.to_parquet(args.output_dir / "feature_metadata.parquet", index=False) + + examples = _make_examples_table(features) + examples.to_parquet(args.output_dir / "feature_examples.parquet", index=False) + + if args.write_json: + with open(args.output_dir / "features.json", "w") as f: + json.dump({"features": features}, f) + print(f"Wrote {len(features)} features -> {args.output_dir / 'features.json'}") + + print(f"Wrote {len(atlas)} atlas rows -> {args.output_dir / 'features_atlas.parquet'}") + print(f"Wrote {len(atlas)} metadata rows -> {args.output_dir / 'feature_metadata.parquet'}") + print(f"Wrote {len(examples)} example rows -> {args.output_dir / 'feature_examples.parquet'}") + + +if __name__ == "__main__": + main() From 20a34a1bae949f1056f98c760ac1a1444e71030e Mon Sep 17 00:00:00 2001 From: Polina Binder Date: Tue, 26 May 2026 23:46:43 +0000 Subject: [PATCH 05/12] evo2 dashboard mockup: trim codonfm bloat, refine labels, add unlabeled features MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Three changes on top of the initial mockup commit: 1. Drop codonfm-specific scaffolding from forked components. - .gitignore the auto-generated package-lock.json (regenerates on `npm install`) - FeatureCard.jsx: 793 -> 508 lines. Removed dead stat tiles (Hi-Score, Variant/Site/Local deltas, ClinVar, PhyloP, GC, Trinuc/Gene entropy), codonfm vocab-logits chart, codonfm GSEA tags, codonfm CSV export sections — all conditional on fields our synthetic data doesn't provide. - FeatureDetailPage.jsx: 522 -> 187 lines. Replaced codonfm-specific VocabLogitChart / CodonAnnotations / FeatureMetrics components with a simpler DNA-friendly detail view. 2. Refine the synthetic feature set. - 11 labeled DNA-native features in 3 thematic UMAP clusters: * eukaryotic regulatory (TATA box, polyA signal, CpG island, splice donor, splice acceptor) * bacterial regulatory (-10 box, -35 box, Shine-Dalgarno) * codon context (start ATG, stop TAA, stop TAG) - 9 unlabeled features in a 4th diffuse cluster (label=NULL, db_source=NULL) — mimics the realistic case where most SAE features are uninterpreted. - New `db_source` column on each feature (RefSeq / JASPAR-ENCODE / bacterial annotation / RefSeq UTR / ENCODE-RefSeq / NULL). 3. Bug fixes for cross-pod port-forward demo: - App.jsx defaults: `selectedCategory` and `histMetric3` were hardcoded to codonfm's `mean_variant_1bcdwt` column, which doesn't exist in our atlas and threw Binder errors. Switched to `cluster_id`. - Atlas column rename: `cluster` -> `cluster_id` to match what App.jsx queries. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../evo2/evo2_dashboard_mockup/.gitignore | 4 + .../evo2_dashboard_mockup/package-lock.json | 6366 ----------------- .../public/feature_examples.parquet | Bin 182767 -> 185822 bytes .../public/feature_metadata.parquet | Bin 6558 -> 7048 bytes .../public/features_atlas.parquet | Bin 6558 -> 7048 bytes .../evo2/evo2_dashboard_mockup/src/App.jsx | 4 +- .../evo2_dashboard_mockup/src/FeatureCard.jsx | 289 +- .../src/FeatureDetailPage.jsx | 429 +- .../evo2/scripts/make_mockup_features.py | 180 +- 9 files changed, 185 insertions(+), 7087 deletions(-) create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/.gitignore delete mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/package-lock.json diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/.gitignore 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Explorer (Mockup)", sub const [selectedFeatureIds, setSelectedFeatureIds] = useState(null) // null = all selected const [mosaicReady, setMosaicReady] = useState(false) const [categoryColumns, setCategoryColumns] = useState([]) - const [selectedCategory, setSelectedCategory] = useState('mean_variant_1bcdwt') + const [selectedCategory, setSelectedCategory] = useState('cluster_id') const [hiddenCategories, setHiddenCategories] = useState(new Set()) const [clickedFeatureId, setClickedFeatureId] = useState(null) const [clusterLabels, setClusterLabels] = useState(null) @@ -208,7 +208,7 @@ export default function App({ title = "Evo 2 SAE Feature Explorer (Mockup)", sub const [showEditedOnly, setShowEditedOnly] = useState(false) // Filter for edited features only const [histMetric1, setHistMetric1] = useState('log_frequency') const [histMetric2, setHistMetric2] = useState('max_activation') - const [histMetric3, setHistMetric3] = useState('mean_variant_1bcdwt') // tracks color-by selection + const [histMetric3, setHistMetric3] = useState('cluster_id') // tracks color-by selection const featureRefs = useRef({}) const featureListRef = useRef(null) const endOfListRef = useRef(null) diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx index 97ec01e178..b32ae8b4e7 100644 --- a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx @@ -173,7 +173,7 @@ const styles = { }, } -const FeatureCard = forwardRef(function FeatureCard({ feature, isHighlighted, forceExpanded, onClick, loadExamples, vocabLogits, featureAnalysis }, ref) { +const FeatureCard = forwardRef(function FeatureCard({ feature, isHighlighted, forceExpanded, onClick, loadExamples }, ref) { const [expanded, setExpanded] = useState(false) const [showDetailPage, setShowDetailPage] = useState(false) const [examples, setExamples] = useState([]) @@ -181,7 +181,6 @@ const FeatureCard = forwardRef(function FeatureCard({ feature, isHighlighted, fo const examplesCacheRef = useRef(null) const [alignMode, setAlignMode] = useState('start') const scrollGroupRef = useRef([]) - const [hoveredCodon, setHoveredCodon] = useState(null) const [editingTitle, setEditingTitle] = useState(false) const [userTitle, setUserTitle] = useState('') const inputRef = useRef(null) @@ -232,16 +231,6 @@ const FeatureCard = forwardRef(function FeatureCard({ feature, isHighlighted, fo const freq = feature.activation_freq || 0 const maxAct = feature.max_activation || 0 - const highScoreFrac = feature.high_score_fraction - const variantDelta = feature.mean_variant_delta - const siteDelta = feature.mean_site_delta - const localDelta = feature.mean_local_delta - const clinvarFrac = feature.clinvar_fraction - const phylop = feature.mean_phylop - const gcMean = feature.gc_mean - const trinucEntropy = feature.trinuc_entropy - const geneEntropy = feature.gene_entropy - const geneNUnique = feature.gene_n_unique const rawDesc = feature.label || feature.description || `Feature ${feature.feature_id}` const description = rawDesc.toLowerCase().includes('common codons') ? 'Unidentified Feature' : rawDesc @@ -297,80 +286,10 @@ const FeatureCard = forwardRef(function FeatureCard({ feature, isHighlighted, fo lines.push(`Max Activation,${maxAct.toFixed(4)}`) lines.push('') - // Vocab logits section - const logits = vocabLogits?.[String(feature.feature_id)] - if (logits) { - lines.push('=== TOP PROMOTED CODONS ===') - lines.push('Codon,Amino Acid,Logit Value') - const CODON_AA = { - 'TTT':'F','TTC':'F','TTA':'L','TTG':'L','TCT':'S','TCC':'S','TCA':'S','TCG':'S', - 'TAT':'Y','TAC':'Y','TAA':'*','TAG':'*','TGT':'C','TGC':'C','TGA':'*','TGG':'W', - 'CTT':'L','CTC':'L','CTA':'L','CTG':'L','CCT':'P','CCC':'P','CCA':'P','CCG':'P', - 'CAT':'H','CAC':'H','CAA':'Q','CAG':'Q','CGT':'R','CGC':'R','CGA':'R','CGG':'R', - 'ATT':'I','ATC':'I','ATA':'I','ATG':'M','ACT':'T','ACC':'T','ACA':'T','ACG':'T', - 'AAT':'N','AAC':'N','AAA':'K','AAG':'K','AGT':'S','AGC':'S','AGA':'R','AGG':'R', - 'GTT':'V','GTC':'V','GTA':'V','GTG':'V','GCT':'A','GCC':'A','GCA':'A','GCG':'A', - 'GAT':'D','GAC':'D','GAA':'E','GAG':'E','GGT':'G','GGC':'G','GGA':'G','GGG':'G', - } - ;(logits.top_positive || []).forEach(([codon, val]) => { - lines.push(`${codon},${CODON_AA[codon] || '?'},${val.toFixed(4)}`) - }) - lines.push('') - - lines.push('=== TOP SUPPRESSED CODONS ===') - lines.push('Codon,Amino Acid,Logit Value') - ;(logits.top_negative || []).forEach(([codon, val]) => { - lines.push(`${codon},${CODON_AA[codon] || '?'},${val.toFixed(4)}`) - }) - lines.push('') - } - - // Codon annotations section - const analysis = featureAnalysis?.[String(feature.feature_id)] - if (analysis?.codon_annotations) { - lines.push('=== CODON ANNOTATIONS ===') - const ann = analysis.codon_annotations - if (ann.amino_acid) { - lines.push(`Amino Acid,${ann.amino_acid.aa}`) - lines.push(`AA Frequency,${(ann.amino_acid.fraction * 100).toFixed(1)}%`) - } - if (ann.codon_usage) { - lines.push(`Codon Usage,${ann.codon_usage.bias}`) - } - if (ann.wobble) { - lines.push(`Wobble Position,${ann.wobble.preference}`) - } - if (ann.cpg) { - lines.push(`CpG Enriched,Yes`) - } - if (ann.position) { - lines.push(`Position,${ann.position.label}`) - } - lines.push('') - } - - // GSEA enrichment section - const gseaCsvFields = [ - { key: 'gsea_overall_best', label: 'GSEA Overall Best' }, - { key: 'gsea_GO_Biological_Process', label: 'GSEA GO Biological Process' }, - { key: 'gsea_GO_Molecular_Function', label: 'GSEA GO Molecular Function' }, - { key: 'gsea_GO_Cellular_Component', label: 'GSEA GO Cellular Component' }, - { key: 'gsea_InterPro_Domains', label: 'GSEA InterPro Domains' }, - { key: 'gsea_GO_Slim', label: 'GSEA GO Slim' }, - ] - const gseaLines = gseaCsvFields - .filter(({ key }) => feature[key] && feature[key] !== 'unlabeled') - .map(({ key, label }) => `${label},${feature[key]}`) - if (gseaLines.length > 0) { - lines.push('=== GSEA ENRICHMENT ===') - gseaLines.forEach(l => lines.push(l)) - lines.push('') - } - // Examples section if (examples && examples.length > 0) { lines.push('=== ACTIVATION EXAMPLES ===') - lines.push('Rank,Protein ID,Max Activation,Sequence') + lines.push('Rank,Region,Max Activation,Sequence') examples.forEach((ex, i) => { lines.push(`${i + 1},${getRegionLabel(ex) || ''},${ex.max_activation?.toFixed(4) || ''},${ex.sequence || ''}`) }) @@ -488,74 +407,6 @@ const FeatureCard = forwardRef(function FeatureCard({ feature, isHighlighted, fo Recon Δ —

- {highScoreFrac != null && !isNaN(highScoreFrac) && ( -
- Hi-Score - 0.6 ? '#d32f2f' : highScoreFrac < 0.4 ? '#388e3c' : '#666' }}> - {(highScoreFrac * 100).toFixed(0)}% - -
- )} - {variantDelta != null && !isNaN(variantDelta) && ( -
- Δ Var - 0.5 ? '#1565c0' : '#666' }}> - {variantDelta > 0 ? '+' : ''}{variantDelta.toFixed(2)} - -
- )} - {siteDelta != null && !isNaN(siteDelta) && ( -
- Δ Site - 0.5 ? '#7b1fa2' : '#666' }}> - {siteDelta > 0 ? '+' : ''}{siteDelta.toFixed(2)} - -
- )} - {localDelta != null && !isNaN(localDelta) && ( -
- Δ Local - 0.5 ? '#00695c' : '#666' }}> - {localDelta > 0 ? '+' : ''}{localDelta.toFixed(2)} - -
- )} - {clinvarFrac != null && !isNaN(clinvarFrac) && ( -
- ClinVar - {(clinvarFrac * 100).toFixed(0)}% -
- )} - {phylop != null && !isNaN(phylop) && ( -
- PhyloP - {phylop.toFixed(1)} -
- )} - {gcMean != null && !isNaN(gcMean) && ( -
- GC - 0.1 ? '#e65100' : '#666' }}> - {(gcMean * 100).toFixed(0)}% - -
- )} - {trinucEntropy != null && !isNaN(trinucEntropy) && ( -
- Trinuc H - - {trinucEntropy.toFixed(1)} - -
- )} - {geneNUnique != null && geneNUnique > 0 && ( -
- Genes - - {geneNUnique} - -
- )} {expanded ? '▼' : '▶'}
@@ -587,140 +438,6 @@ const FeatureCard = forwardRef(function FeatureCard({ feature, isHighlighted, fo {expanded && (
- {/* Vocabulary logits - all codons grouped by amino acid */} - {vocabLogits && vocabLogits[String(feature.feature_id)] && (() => { - const logits = vocabLogits[String(feature.feature_id)] - const CODON_AA = { - 'TTT':'F','TTC':'F','TTA':'L','TTG':'L','CTT':'L','CTC':'L','CTA':'L','CTG':'L', - 'ATT':'I','ATC':'I','ATA':'I','ATG':'M','GTT':'V','GTC':'V','GTA':'V','GTG':'V', - 'TCT':'S','TCC':'S','TCA':'S','TCG':'S','CCT':'P','CCC':'P','CCA':'P','CCG':'P', - 'ACT':'T','ACC':'T','ACA':'T','ACG':'T','GCT':'A','GCC':'A','GCA':'A','GCG':'A', - 'TAT':'Y','TAC':'Y','TAA':'*','TAG':'*','CAT':'H','CAC':'H','CAA':'Q','CAG':'Q', - 'AAT':'N','AAC':'N','AAA':'K','AAG':'K','GAT':'D','GAC':'D','GAA':'E','GAG':'E', - 'TGT':'C','TGC':'C','TGA':'*','TGG':'W','CGT':'R','CGC':'R','CGA':'R','CGG':'R', - 'AGT':'S','AGC':'S','AGA':'R','AGG':'R','GGT':'G','GGC':'G','GGA':'G','GGG':'G', - } - // Build codon logit map from all entries, excluding stop codons - const codonLogitMap = {} - for (const [codon, val] of (logits.top_positive || [])) { - if (CODON_AA[codon] !== '*') codonLogitMap[codon] = val - } - for (const [codon, val] of (logits.top_negative || [])) { - if (CODON_AA[codon] !== '*') codonLogitMap[codon] = val - } - const maxAbs = Math.max(...Object.values(codonLogitMap).map(Math.abs), 0.001) - // Group by AA, excluding stop codons - const aaGroups = {} - for (const [codon, aa] of Object.entries(CODON_AA)) { - if (aa === '*') continue - if (!aaGroups[aa]) aaGroups[aa] = [] - aaGroups[aa].push(codon) - } - const aaOrder = Object.keys(aaGroups).sort() - return ( -
-
Decoder Logits
-
- {hoveredCodon && (() => { - const val = codonLogitMap[hoveredCodon] || 0 - const aa = CODON_AA[hoveredCodon] - return ( -
- {hoveredCodon} ({aa}): {val.toFixed(3)} -
- ) - })()} -
- {aaOrder.map(aa => { - const codons = aaGroups[aa] || [] - return ( -
-
{aa}
-
- {codons.sort().map(codon => { - const val = codonLogitMap[codon] || 0 - const h = Math.max(1, (Math.abs(val) / maxAbs) * 24) - const isHovered = hoveredCodon === codon - const barColor = val === 0 ? '#ccc' : val > 0 ? '#76b900' : '#e57373' - return ( -
setHoveredCodon(codon)} - onMouseLeave={() => setHoveredCodon(null)} - > -
-
- ) - })} -
-
- ) - })} -
-
-
- ) - })()} - - {/* Analysis summary tags */} - {featureAnalysis && featureAnalysis[String(feature.feature_id)] && (() => { - const analysis = featureAnalysis[String(feature.feature_id)] - const tags = [] - const ann = analysis.codon_annotations || {} - - if (ann.amino_acid) tags.push({ label: `AA: ${ann.amino_acid.aa} (${(ann.amino_acid.fraction * 100).toFixed(0)}%)`, color: '#e3f2fd' }) - if (ann.codon_usage) tags.push({ label: `${ann.codon_usage.bias} codons`, color: '#fff3e0' }) - if (ann.wobble) tags.push({ label: `wobble ${ann.wobble.preference}`, color: '#f3e5f5' }) - if (ann.cpg) tags.push({ label: `CpG enriched`, color: '#fce4ec' }) - if (ann.position) tags.push({ label: `N-terminal`, color: '#e8f5e9' }) - - // GSEA enrichment tags - const gseaFields = [ - { key: 'gsea_GO_Biological_Process', prefix: 'GO:BP', color: '#e8eaf6' }, - { key: 'gsea_GO_Molecular_Function', prefix: 'GO:MF', color: '#ede7f6' }, - { key: 'gsea_GO_Cellular_Component', prefix: 'GO:CC', color: '#e0f2f1' }, - { key: 'gsea_InterPro_Domains', prefix: 'InterPro', color: '#fff8e1' }, - { key: 'gsea_GO_Slim', prefix: 'GO Slim', color: '#f1f8e9' }, - ] - for (const { key, prefix, color } of gseaFields) { - const val = feature[key] - if (val && val !== 'unlabeled' && val !== 'other') { - tags.push({ label: `${prefix}: ${val}`, color }) - } - } - - // Codon optimality metrics from annotations - if (ann.cai != null) tags.push({ label: `CAI: ${ann.cai.toFixed(3)}`, color: '#e0f7fa' }) - if (ann.tai != null) tags.push({ label: `tAI: ${ann.tai.toFixed(3)}`, color: '#e0f7fa' }) - if (ann.rscu != null) tags.push({ label: `RSCU: ${ann.rscu.toFixed(2)}`, color: '#e0f7fa' }) - - if (tags.length === 0) return null - return ( -
- {tags.map((t, i) => ( - {t.label} - ))} -
- ) - })()} - {/* Sequence examples */}
Top Activating Sequences
@@ -781,8 +498,6 @@ const FeatureCard = forwardRef(function FeatureCard({ feature, isHighlighted, fo setShowDetailPage(false)} /> )} diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx index 8e3ec0ce9b..f60f68d373 100644 --- a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx @@ -2,39 +2,6 @@ import React, { useState, useEffect, useRef } from 'react' import SequenceView, { computeAlignInfo } from './SequenceView' import { getRegionLabel } from './utils' -// ── Codon-to-AA lookup for logit chart ────────────────────────────── -const CODON_AA = { - 'TTT':'F','TTC':'F','TTA':'L','TTG':'L','CTT':'L','CTC':'L','CTA':'L','CTG':'L', - 'ATT':'I','ATC':'I','ATA':'I','ATG':'M','GTT':'V','GTC':'V','GTA':'V','GTG':'V', - 'TCT':'S','TCC':'S','TCA':'S','TCG':'S','CCT':'P','CCC':'P','CCA':'P','CCG':'P', - 'ACT':'T','ACC':'T','ACA':'T','ACG':'T','GCT':'A','GCC':'A','GCA':'A','GCG':'A', - 'TAT':'Y','TAC':'Y','TAA':'*','TAG':'*','CAT':'H','CAC':'H','CAA':'Q','CAG':'Q', - 'AAT':'N','AAC':'N','AAA':'K','AAG':'K','GAT':'D','GAC':'D','GAA':'E','GAG':'E', - 'TGT':'C','TGC':'C','TGA':'*','TGG':'W','CGT':'R','CGC':'R','CGA':'R','CGG':'R', - 'AGT':'S','AGC':'S','AGA':'R','AGG':'R','GGT':'G','GGC':'G','GGA':'G','GGG':'G', -} - -// Group codons by amino acid, excluding stop codons -const AA_GROUPS = {} -for (const [codon, aa] of Object.entries(CODON_AA)) { - if (aa === '*') continue - if (!AA_GROUPS[aa]) AA_GROUPS[aa] = [] - AA_GROUPS[aa].push(codon) -} -const AA_ORDER = Object.keys(AA_GROUPS).sort() - -// Color palette for amino acid groups -const AA_COLORS = { - // Nonpolar - 'G': '#e8e8e8', 'A': '#c8c8c8', 'V': '#b0b0b0', 'L': '#a0a0a0', 'I': '#909090', - 'P': '#d0d0a0', 'F': '#c0b0a0', 'W': '#b0a090', 'M': '#a09080', - // Polar - 'S': '#b0d0ff', 'T': '#a0c0f0', 'C': '#90b0e0', 'Y': '#80a0d0', - 'N': '#a0d0b0', 'Q': '#90c0a0', - // Charged - 'D': '#ffb0b0', 'E': '#ffa0a0', 'K': '#b0b0ff', 'R': '#a0a0ff', 'H': '#c0b0ff', -} - const styles = { overlay: { position: 'fixed', @@ -63,10 +30,6 @@ const styles = { fontWeight: '700', color: 'var(--text-heading)', }, - subtitle: { - fontSize: '11px', - color: 'var(--text-secondary)', - }, closeBtn: { background: 'none', border: '1px solid var(--border-input)', @@ -87,42 +50,6 @@ const styles = { color: 'var(--text-heading)', textTransform: 'uppercase', }, - sectionSubtitle: { - fontSize: '9px', - color: 'var(--text-muted)', - marginBottom: '6px', - }, - statsGrid: { - display: 'grid', - gridTemplateColumns: 'repeat(4, 1fr)', - gap: '8px', - }, - statCard: { - background: 'var(--bg-card-expanded)', - borderRadius: '4px', - padding: '6px 8px', - textAlign: 'center', - }, - statNumber: { - fontSize: '14px', - fontWeight: '700', - color: 'var(--text-heading)', - }, - statLabel: { - fontSize: '8px', - color: 'var(--text-tertiary)', - textTransform: 'uppercase', - marginTop: '2px', - }, - tag: { - display: 'inline-block', - padding: '3px 8px', - borderRadius: '4px', - fontSize: '11px', - fontWeight: '500', - marginRight: '6px', - marginBottom: '4px', - }, example: { marginBottom: '6px', padding: '6px 8px', @@ -138,207 +65,52 @@ const styles = { display: 'flex', justifyContent: 'space-between', }, + placeholder: { + border: '1px dashed var(--border)', + borderRadius: '6px', + padding: '24px', + textAlign: 'center', + color: 'var(--text-muted)', + fontSize: '12px', + fontStyle: 'italic', + }, + placeholderLabel: { + fontSize: '13px', + fontWeight: '500', + color: 'var(--text-muted)', + marginBottom: '8px', + }, } - -// ── Vocab Logit Chart Component ───────────────────────────────────── - -function VocabLogitChart({ logits }) { - if (!logits) return null - - // Build codon logit map, excluding stop codons (universally suppressed, not informative) - const codonLogitMap = {} - for (const [codon, val] of logits.top_positive) { - if (CODON_AA[codon] !== '*') codonLogitMap[codon] = val - } - for (const [codon, val] of logits.top_negative) { - if (CODON_AA[codon] !== '*') codonLogitMap[codon] = val - } - - const maxAbs = Math.max(...Object.values(codonLogitMap).map(Math.abs), 0.001) - - return ( -
-
- {AA_ORDER.map(aa => { - const codons = AA_GROUPS[aa] || [] - const aaColor = AA_COLORS[aa] || 'var(--border-light)' - return ( -
-
- {aa} -
-
- {codons.sort().map(codon => { - const val = codonLogitMap[codon] || 0 - const h = Math.max(2, (Math.abs(val) / maxAbs) * 34) - const isPos = val > 0 - return ( -
-
- - {codon} - -
- ) - })} -
-
- ) - })} -
-
- Promoted - Suppressed -
-
- ) -} - - -// ── Codon Annotations Component ───────────────────────────────────── - -function CodonAnnotations({ annotations }) { - if (!annotations || Object.keys(annotations).length === 0) { - return
No significant annotations detected
- } - - const items = [] - - if (annotations.amino_acid) { - items.push({ - label: 'Amino Acid', - value: `${annotations.amino_acid.aa} (${(annotations.amino_acid.fraction * 100).toFixed(0)}% of activations)`, - color: '#e3f2fd', - }) - } - if (annotations.codon_usage) { - items.push({ - label: 'Codon Usage', - value: `Prefers ${annotations.codon_usage.bias} codons (${(annotations.codon_usage.fraction * 100).toFixed(0)}%)`, - color: '#fff3e0', - }) - } - if (annotations.wobble) { - items.push({ - label: 'Wobble Position', - value: `${annotations.wobble.preference} preference (${(annotations.wobble.fraction * 100).toFixed(0)}%)`, - color: '#f3e5f5', - }) - } - if (annotations.cpg) { - items.push({ - label: 'CpG Context', - value: `Enriched (${(annotations.cpg.enrichment * 100).toFixed(0)}% of activations at CpG boundaries)`, - color: '#fce4ec', - }) - } - if (annotations.position) { - items.push({ - label: 'Gene Position', - value: `N-terminal enriched (${annotations.position.enrichment.toFixed(1)}x over expected)`, - color: '#e8f5e9', - }) - } - - return ( -
- {items.map((item, i) => ( -
- - {item.label}: - {' '} - {item.value} -
- ))} -
- ) -} - - -// ── Feature Metrics Component ─────────────────────────────────────── - -function FeatureMetrics({ feature }) { - const metrics = [ - { key: 'mean_variant_1bcdwt', label: 'Mean Variant (1B CDWT)' }, - { key: 'high_score_fraction', label: 'High Score Fraction' }, - { key: 'clinvar_fraction', label: 'ClinVar Fraction' }, - { key: 'mean_phylop', label: 'Mean PhyloP' }, - { key: 'mean_variant_delta', label: 'Mean Variant Delta' }, - { key: 'mean_site_delta', label: 'Mean Site Delta' }, - { key: 'mean_local_delta', label: 'Mean Local Delta' }, - { key: 'gc_mean', label: 'GC Mean' }, - { key: 'gc_std', label: 'GC Std' }, - { key: 'trinuc_entropy', label: 'Trinuc Entropy' }, - { key: 'gene_entropy', label: 'Gene Entropy' }, - { key: 'gene_n_unique', label: 'Gene N Unique' }, - ] - - const available = metrics.filter(m => feature[m.key] != null && !isNaN(feature[m.key])) - if (available.length === 0) return null - - return ( -
- {available.map(m => ( -
-
- {typeof feature[m.key] === 'number' - ? Math.abs(feature[m.key]) >= 100 ? feature[m.key].toFixed(0) - : Math.abs(feature[m.key]) >= 1 ? feature[m.key].toFixed(2) - : feature[m.key].toFixed(4) - : feature[m.key]} -
-
{m.label}
-
- ))} -
- ) -} - - -// ── Main Detail Page ──────────────────────────────────────────────── - -export default function FeatureDetailPage({ feature, examples, vocabLogits, featureAnalysis, onClose }) { +export default function FeatureDetailPage({ feature, examples, onClose }) { const [alignMode, setAlignMode] = useState('max_activation') const scrollGroupRef = useRef(null) - const fid = String(feature.feature_id) - const logits = vocabLogits ? vocabLogits[fid] : null - const analysis = featureAnalysis ? featureAnalysis[fid] : null - const freq = feature.activation_freq || 0 const maxAct = feature.max_activation || 0 - const description = feature.description || `Feature ${feature.feature_id}` + const description = feature.description || feature.label || `Feature ${feature.feature_id}` - // Close on Escape useEffect(() => { const handleKey = (e) => { if (e.key === 'Escape') onClose() } document.addEventListener('keydown', handleKey) return () => document.removeEventListener('keydown', handleKey) }, [onClose]) - const visibleExamples = (examples || []).slice(0, 6) - const { anchor: alignAnchor, totalLength } = computeAlignInfo(visibleExamples, alignMode) + const visibleExamples = (examples || []).slice(0, 30) + const { anchor: alignAnchor, totalLength } = computeAlignInfo(visibleExamples.slice(0, 6), alignMode) return (
{ if (e.target === e.currentTarget) onClose() }}>
- {/* Header + stats in one row */}
-
Feature #{feature.feature_id} {description}
+
+ Feature #{feature.feature_id} + + {description} + +
@@ -349,83 +121,6 @@ export default function FeatureDetailPage({ feature, examples, vocabLogits, feat
- {/* Feature Metrics from color-by columns */} -
-
Feature Metrics
-
- Variant analysis and sequence composition metrics for this feature. -
- -
- - {/* Vocabulary Logits */} -
-
Decoder Logits — Promoted / Suppressed Codons
-
- Projection of this feature's decoder weight through the Encodon LM head, with the mean logit vector subtracted. - This mean-centering removes the model's shared baseline bias (e.g. toward common codons like GCC), so values reflect what this feature specifically promotes (green) or suppresses (red) relative to the average feature. - Stop codons (TAA/TAG/TGA) are excluded — they are uniformly suppressed across all features since the model was trained on coding sequences. -
- -
- - {/* Gene-Level GSEA Enrichment */} - {(() => { - const gseaFields = [ - { key: 'gsea_GO_Biological_Process', label: 'GO Biological Process' }, - { key: 'gsea_GO_Molecular_Function', label: 'GO Molecular Function' }, - { key: 'gsea_GO_Cellular_Component', label: 'GO Cellular Component' }, - { key: 'gsea_InterPro_Domains', label: 'InterPro Domains' }, - { key: 'gsea_GO_Slim', label: 'GO Slim' }, - ] - const gseaEntries = gseaFields - .map(({ key, label }) => ({ label, value: feature[key] })) - .filter(e => e.value && e.value !== 'unlabeled' && e.value !== 'other') - const overallBest = feature.gsea_overall_best - if (gseaEntries.length === 0 && (!overallBest || overallBest === 'unlabeled')) return null - return ( -
-
Gene-Level Enrichment (GSEA)
-
- Genes ranked by activation strength, tested against GO and InterPro databases. -
- {overallBest && overallBest !== 'unlabeled' && ( -
- Best: {overallBest} -
- )} -
- {gseaEntries.map(({ label, value }) => ( -
-
- {label} -
-
{value}
-
- ))} -
-
- ) - })()} - - {/* Codon Annotations */} -
-
Codon-Level Annotations
-
- Computed per-codon properties correlated with this feature's activations. -
- -
- - {/* Top Activating Sequences */}
Top Activating Sequences
@@ -449,69 +144,39 @@ export default function FeatureDetailPage({ feature, examples, vocabLogits, feat
{visibleExamples.length > 0 ? ( - <> - {visibleExamples.map((ex, i) => ( -
-
- - {getRegionLabel(ex)} - - max: {ex.max_activation?.toFixed(3)} -
- + visibleExamples.map((ex, i) => ( +
+
+ {getRegionLabel(ex)} + max: {ex.max_activation?.toFixed(3)}
- ))} - + +
+ )) ) : (
No examples loaded
)}
{/* v2 roadmap placeholders — populated when annotation + conservation pipelines land. */} -
-
- Annotations -
-
+
+
Annotations
+
Annotation overlay (RefSeq, Rfam, JASPAR) — coming in v2
-
-
- Conservation -
-
+
+
Conservation
+
Conservation track (phyloP) — coming in v2
diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py index dc7f72e1cc..2d29167fc6 100644 --- a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py @@ -27,56 +27,53 @@ import pandas as pd -# Plausible biological labels (20 features) — visible motifs in the synthetic sequences. +# DNA-native labels for evo2 features, each with a real central signature spliced into +# the middle of the 200bp window so the mockup features are visually distinguishable. LABELS = [ - "exon-start motif", - "tRNA acceptor stem", - "intergenic GC-rich", - "stop codon context", - "ribosome binding site", - "promoter -10 box", + "Start codon (ATG) context", + "TATA box", + "Polyadenylation signal", + "Bacterial promoter -10 box", "CpG island", - "splice donor", - "polyA signal", - "start codon ATG context", - "transposon repeat", - "rRNA conserved region", - "operon intergenic", - "frameshift-sensitive region", - "high-conservation coding", - "intron branch point", - "TF binding motif", - "phage integrase region", - "origin of replication", "Shine-Dalgarno sequence", + "Bacterial promoter -35 box", + "Splice donor site", + "Splice acceptor site", + "Stop codon (TAA) context", + "Stop codon (TAG) context", ] # Plausible accessions to rotate across examples. SEQ_IDS = ["NC_000913.3", "NC_002695.2", "chr1", "chr17"] -# Central motifs to splice into each feature's top-activating windows. -# Doesn't need biological rigor — just makes features visually distinguishable in the demo. +# Central motif spliced into the middle ~20bp of each top-activating window. CENTRAL_MOTIFS = { - "exon-start motif": "AGGTAAGT", - "tRNA acceptor stem": "CCCGGGT", - "intergenic GC-rich": "GCGCGCGC", - "stop codon context": "TAATAATAA", - "ribosome binding site": "AGGAGG", - "promoter -10 box": "TATAAT", - "CpG island": "CGCGCGCG", - "splice donor": "GTAAGT", - "polyA signal": "AATAAA", - "start codon ATG context": "ATGGCC", - "transposon repeat": "TTAATTAA", - "rRNA conserved region": "GUCAGCUGGUC".replace("U", "T"), - "operon intergenic": "AAATTT", - "frameshift-sensitive region": "AAAAAAA", - "high-conservation coding": "GCAGCAGCA", - "intron branch point": "TACTAAC", - "TF binding motif": "TGACTCA", - "phage integrase region": "GCTAGGTGT", - "origin of replication": "ATCGATCG", + "Start codon (ATG) context": "GCCACCATGGCC", + "TATA box": "TATAAA", + "Polyadenylation signal": "AATAAA", + "Bacterial promoter -10 box": "TATAAT", + "CpG island": "CGCGCGCGCGCGCGCG", "Shine-Dalgarno sequence": "AGGAGGT", + "Bacterial promoter -35 box": "TTGACA", + "Splice donor site": "GTAAGT", # GT at exon-intron boundary, with consensus context + "Splice acceptor site": "TTTTCAGG", # AG at intron-exon boundary, with pyrimidine tract + "Stop codon (TAA) context": "GCCTAAGCC", # TAA in coding context + "Stop codon (TAG) context": "GCCTAGGCC", # TAG in coding context +} + +# Annotation-database source for each feature label. +DB_SOURCES = { + "Start codon (ATG) context": "RefSeq", + "TATA box": "JASPAR / ENCODE", + "Polyadenylation signal": "RefSeq UTR", + "Bacterial promoter -10 box": "bacterial annotation", + "CpG island": "ENCODE / RefSeq", + "Shine-Dalgarno sequence": "bacterial annotation", + "Bacterial promoter -35 box": "bacterial annotation", + "Splice donor site": "RefSeq", + "Splice acceptor site": "RefSeq", + "Stop codon (TAA) context": "RefSeq", + "Stop codon (TAG) context": "RefSeq", } @@ -133,6 +130,7 @@ def _make_features(rng: np.random.Generator) -> list[dict]: "feature_id": fid, "description": label, "label": label, + "db_source": DB_SOURCES[label], "activation_freq": round(activation_freq, 6), "max_activation": round(max_activation, 4), "top_positive_logits": [], @@ -144,15 +142,46 @@ def _make_features(rng: np.random.Generator) -> list[dict]: def _make_atlas(rng: np.random.Generator, features: list[dict]) -> pd.DataFrame: - """Build features_atlas.parquet — synthetic UMAP coords with 4 visible clusters of 5 features each.""" - n_clusters = 4 - cluster_centers = rng.uniform(-5.0, 5.0, size=(n_clusters, 2)) + """Build features_atlas.parquet — UMAP coords grouped into thematic clusters. + + Labeled features sit in 3 clusters: eukaryotic regulatory (0), bacterial regulatory (1), + codon context (2). Unlabeled features (label==None) land in a 4th "uninterpreted" cluster (3) + spread more diffusely between the others — mimicking what a real SAE would look like. + """ + cluster_assignments = { + "Start codon (ATG) context": 2, + "TATA box": 0, + "Polyadenylation signal": 0, + "Bacterial promoter -10 box": 1, + "CpG island": 0, + "Shine-Dalgarno sequence": 1, + "Bacterial promoter -35 box": 1, + "Splice donor site": 0, + "Splice acceptor site": 0, + "Stop codon (TAA) context": 2, + "Stop codon (TAG) context": 2, + } + cluster_centers = { + 0: (-3.0, 1.5), + 1: (3.0, 1.5), + 2: (0.0, -3.0), + 3: (0.0, 0.5), # uninterpreted: between the other clusters + } + coords = [] - for fid in range(len(features)): - cluster_idx = fid // (len(features) // n_clusters) - center = cluster_centers[cluster_idx] - xy = center + rng.normal(0, 0.5, size=2) - coords.append(xy) + cluster_ids = [] + for f in features: + if f["label"] is None: + cid = 3 + sigma = 1.4 # diffuse for the unlabeled cloud + else: + cid = cluster_assignments[f["label"]] + sigma = 0.4 + cx, cy = cluster_centers[cid] + x = cx + rng.normal(0, sigma) + y = cy + rng.normal(0, sigma) + coords.append((x, y)) + cluster_ids.append(cid) coords = np.array(coords) return pd.DataFrame( @@ -161,14 +190,63 @@ def _make_atlas(rng: np.random.Generator, features: list[dict]) -> pd.DataFrame: "x": coords[:, 0].round(4), "y": coords[:, 1].round(4), "label": [f["label"] for f in features], + "db_source": [f["db_source"] for f in features], "activation_freq": [f["activation_freq"] for f in features], "log_frequency": [round(float(np.log10(f["activation_freq"])), 4) for f in features], "max_activation": [f["max_activation"] for f in features], - "cluster": [fid // (len(features) // n_clusters) for fid in range(len(features))], + "cluster_id": cluster_ids, } ) +def _make_unlabeled_example(rng: np.random.Generator, feature_max: float, window: int = 200) -> dict: + """A top-activating example for an unlabeled feature: random sequence + gaussian activation bump.""" + seq = _random_dna(rng, window) + bump_center = int(rng.integers(60, 141)) + sigma = 8.0 + peak = float(feature_max * rng.uniform(0.5, 1.0)) + positions = np.arange(window) + activations = peak * np.exp(-((positions - bump_center) ** 2) / (2 * sigma**2)) + activations[activations < 0.01] = 0.0 + + seq_id = SEQ_IDS[int(rng.integers(0, len(SEQ_IDS)))] + start = int(rng.integers(1, 5_000_001)) + + return { + "sequence_id": seq_id, + "start": start, + "end": start + window, + "sequence": seq, + "activations": [round(float(a), 3) for a in activations], + "max_activation": round(float(activations.max()), 4), + "max_activation_position": int(activations.argmax()), + } + + +def _make_unlabeled_features(rng: np.random.Generator, n: int, start_id: int) -> list[dict]: + """Build n unlabeled features — no semantic label, random top-activator sequences.""" + out = [] + for i in range(n): + fid = start_id + i + activation_freq = float(np.exp(rng.uniform(np.log(0.001), np.log(0.1)))) + max_activation = float(rng.uniform(5.0, 30.0)) + examples = [_make_unlabeled_example(rng, max_activation) for _ in range(30)] + out.append( + { + "feature_id": fid, + "description": None, + "label": None, + "db_source": None, + "activation_freq": round(activation_freq, 6), + "max_activation": round(max_activation, 4), + "top_positive_logits": [], + "top_negative_logits": [], + "examples": examples, + } + ) + return out + + def _make_examples_table(features: list[dict]) -> pd.DataFrame: """Flatten per-feature examples into a long table for feature_examples.parquet. @@ -188,7 +266,7 @@ def _make_examples_table(features: list[dict]) -> pd.DataFrame: "activations": ex["activations"], "max_activation": ex["max_activation"], "max_activation_position": ex["max_activation_position"], - "best_annotation": None, + "best_annotation": feature["db_source"], } ) return pd.DataFrame(rows) @@ -208,6 +286,7 @@ def main(): action="store_true", help="Also write features.json (only useful if you point the dashboard at the legacy JSON path)", ) + p.add_argument("--n-unlabeled", type=int, default=9, help="How many unlabeled features to add alongside the labeled ones") p.add_argument("--seed", type=int, default=42) args = p.parse_args() @@ -215,6 +294,7 @@ def main(): args.output_dir.mkdir(parents=True, exist_ok=True) features = _make_features(rng) + features += _make_unlabeled_features(rng, n=args.n_unlabeled, start_id=len(features)) atlas = _make_atlas(rng, features) atlas.to_parquet(args.output_dir / "features_atlas.parquet", index=False) From c36ed2fae732c1d23c45ba0874a3c539c58613c7 Mon Sep 17 00:00:00 2001 From: Polina Binder Date: Wed, 27 May 2026 18:02:40 +0000 Subject: [PATCH 06/12] evo2 dashboard mockup: per-feature WebLogo PNGs via logomaker MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Each of the 11 labeled features now ships with a PWM-driven sequence logo rendered into public/logos/feature_{id}.png by logomaker. Central signatures are spec'd per label (Kozak ATG, TATA, polyA, CpG, Shine- Dalgarno, bacterial -10/-35, splice donor/acceptor, stop TAA/TAG); flanks are uniform 0-bit so the logos read as clean motif summaries rather than noisy speckle. Unlabeled features get no logo — their cards skip the section entirely. make_mockup_features.py grows _build_pwm() and _render_logo(); the metadata/atlas parquets carry a logo_path column; App.jsx detects it optionally and excludes it from category detection; FeatureCard's expanded view and FeatureDetailPage display the logo above the top- activating-sequences list. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../public/feature_metadata.parquet | Bin 7048 -> 7705 bytes .../public/features_atlas.parquet | Bin 7048 -> 7705 bytes .../public/logos/feature_0.png | Bin 0 -> 21798 bytes .../public/logos/feature_1.png | Bin 0 -> 17353 bytes .../public/logos/feature_10.png | Bin 0 -> 19788 bytes .../public/logos/feature_2.png | Bin 0 -> 17994 bytes .../public/logos/feature_3.png | Bin 0 -> 16410 bytes .../public/logos/feature_4.png | Bin 0 -> 22419 bytes .../public/logos/feature_5.png | Bin 0 -> 19657 bytes .../public/logos/feature_6.png | Bin 0 -> 17391 bytes .../public/logos/feature_7.png | Bin 0 -> 16672 bytes .../public/logos/feature_8.png | Bin 0 -> 18396 bytes .../public/logos/feature_9.png | Bin 0 -> 19370 bytes .../evo2/evo2_dashboard_mockup/src/App.jsx | 8 +- .../evo2_dashboard_mockup/src/FeatureCard.jsx | 10 ++ .../src/FeatureDetailPage.jsx | 11 ++ .../evo2/scripts/make_mockup_features.py | 160 ++++++++++++++++++ 17 files changed, 188 insertions(+), 1 deletion(-) create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/logos/feature_0.png create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/logos/feature_1.png create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/logos/feature_10.png create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/logos/feature_2.png create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/logos/feature_3.png create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/logos/feature_4.png create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/logos/feature_5.png create mode 100644 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zVFe(^7v8s698EhkkPguFuLj);V~~15Q$gO-lYcqj>@^|^N_tY(B@C)Sj={@r)=Nq& z-lwGzfwSGELunN3m&Xtg6~thzdGAG66i_ZygI)qiAm*T_#L+&$C@lN}ybVW)BH&mG zpaM%E2LHbX8j$w$d(Mqu11>*?_U2UOmkQ#IT5WRkalh)7HIyPfR7wqpI?*YR`3wj0VV zc>90iUSN$}Q(OD-sCazB#YL_X#%Vmj>{VRz?PfY?sLJOu7jTHP2e<+H!OG?H4gm+Y z41mK56_u5sJ>$SGwqP8vE(4v=15Cf}z`{pNH>$<@-4148dk*nHxp`QAREuaxBmztVA0V_k& zmM)H({zb^C16Y!QS^}W{8PJ1AftAZqVEK2p&v4q8U%+)*XTCju@&q{Y^7hu&u===U zPyJ-imDkL2Z>fM>3LHdI0FCm3hEMJPedI3$cB?NNzMcA|>h;>~F8k~LwgB7Uz){Qv z5y0_Y++!zAkJ`PUF>e3<6CuJrcRkURTWqYDzfoPrF{H9 zuOr4=@X)#4z(ZC#c6A=@7EcC_*Z_wUUcL(GSTXy-99-w|SVGT^0 `"${c.name}"`) .join(', ') const extraSelect = categorySelectCols ? `, ${categorySelectCols}` : '' + // logo_path is optional — older parquets won't have it, so detect and + // include it only if the column exists. + const hasLogoPath = columns.some(c => c.name === 'logo_path') + const logoSelect = hasLogoPath ? ', logo_path' : '' const featuresResult = await vg.coordinator().query(` SELECT feature_id, @@ -605,6 +609,7 @@ export default function App({ title = "Evo 2 SAE Feature Explorer (Mockup)", sub max_activation, x, y + ${logoSelect} ${extraSelect} FROM features ORDER BY feature_id @@ -618,6 +623,7 @@ export default function App({ title = "Evo 2 SAE Feature Explorer (Mockup)", sub max_activation: row.max_activation, x: row.x, y: row.y, + logo_path: row.logo_path, } for (const col of detectedCategories) { if (col.type === 'string' || col.type === 'integer') { diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx index b32ae8b4e7..d45bc121df 100644 --- a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureCard.jsx @@ -438,6 +438,16 @@ const FeatureCard = forwardRef(function FeatureCard({ feature, isHighlighted, fo {expanded && (
+ {feature.logo_path && ( +
+
Sequence Logo
+ {`Sequence +
+ )} {/* Sequence examples */}
Top Activating Sequences
diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx index f60f68d373..b70fdcdbde 100644 --- a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/FeatureDetailPage.jsx @@ -121,6 +121,17 @@ export default function FeatureDetailPage({ feature, examples, onClose }) {
+ {feature.logo_path && ( +
+
Sequence Logo
+ {`Sequence +
+ )} +
Top Activating Sequences
diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py index 2d29167fc6..f3ccbccc99 100644 --- a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/make_mockup_features.py @@ -61,6 +61,73 @@ "Stop codon (TAG) context": "GCCTAGGCC", # TAG in coding context } +# 19bp PWM window centered on the activation peak (positions -9..+9). +PWM_WINDOW = 19 +PWM_PEAK = 9 +PWM_BASES = ["A", "C", "G", "T"] + + +# Per-position base probabilities for each feature's central signature. Positions +# outside the signature are filled with near-uniform (low information) draws so +# real-looking logos have low-info flanks. +PWM_SIGNATURES: dict[str, list[dict[str, float]]] = { + # Kozak-like GCCACCATGG — ATG at signature positions 6..8. + "Start codon (ATG) context": [ + {"G": 0.70}, {"C": 0.70}, {"C": 0.70}, {"A": 0.60}, {"C": 0.65}, + {"C": 0.60}, {"A": 0.95}, {"T": 0.95}, {"G": 0.95}, {"G": 0.75}, + ], + "TATA box": [ # TATAAA + {"T": 0.90}, {"A": 0.90}, {"T": 0.90}, + {"A": 0.70, "G": 0.20}, {"A": 0.95}, {"A": 0.80, "T": 0.15}, + ], + "Polyadenylation signal": [ # AATAAA + {"A": 0.90}, {"A": 0.90}, {"T": 0.85}, + {"A": 0.85}, {"A": 0.85}, {"A": 0.80}, + ], + "Bacterial promoter -10 box": [ # TATAAT + {"T": 0.90}, {"A": 0.85}, {"T": 0.85}, + {"A": 0.75}, {"A": 0.75}, {"T": 0.85}, + ], + # CpG-rich: alternating GC bias across 12 positions, no sharp single peak. + "CpG island": [ + {"C": 0.55, "G": 0.35}, {"G": 0.55, "C": 0.35}, + {"C": 0.55, "G": 0.35}, {"G": 0.55, "C": 0.35}, + {"C": 0.55, "G": 0.35}, {"G": 0.55, "C": 0.35}, + {"C": 0.55, "G": 0.35}, {"G": 0.55, "C": 0.35}, + {"C": 0.55, "G": 0.35}, {"G": 0.55, "C": 0.35}, + {"C": 0.55, "G": 0.35}, {"G": 0.55, "C": 0.35}, + ], + "Shine-Dalgarno sequence": [ # AGGAGGT + {"A": 0.80}, {"G": 0.90}, {"G": 0.90}, + {"A": 0.75}, {"G": 0.90}, {"G": 0.85}, {"T": 0.60}, + ], + "Bacterial promoter -35 box": [ # TTGACA + {"T": 0.90}, {"T": 0.85}, {"G": 0.85}, + {"A": 0.80}, {"C": 0.85}, {"A": 0.80}, + ], + # GT at the exon|intron boundary is essentially invariant. + "Splice donor site": [ # GT.AAGT + {"G": 0.99}, {"T": 0.99}, {"A": 0.60}, + {"A": 0.70}, {"G": 0.80}, {"T": 0.60}, + ], + # Pyrimidine tract leading into an invariant AG at the intron|exon boundary. + "Splice acceptor site": [ + {"T": 0.80}, {"T": 0.80}, {"T": 0.80}, {"T": 0.80}, + {"C": 0.70}, {"A": 0.99}, {"G": 0.99}, {"G": 0.55}, + ], + "Stop codon (TAA) context": [ # GCC.TAA.GCC — coding-context flanks + {"G": 0.45, "C": 0.40}, {"C": 0.55, "G": 0.30}, {"C": 0.50, "G": 0.35}, + {"T": 0.95}, {"A": 0.90}, {"A": 0.90}, + {"G": 0.45, "C": 0.40}, {"C": 0.55, "G": 0.30}, {"C": 0.50, "G": 0.35}, + ], + "Stop codon (TAG) context": [ + {"G": 0.45, "C": 0.40}, {"C": 0.55, "G": 0.30}, {"C": 0.50, "G": 0.35}, + {"T": 0.95}, {"A": 0.90}, {"G": 0.90}, + {"G": 0.45, "C": 0.40}, {"C": 0.55, "G": 0.30}, {"C": 0.50, "G": 0.35}, + ], +} + + # Annotation-database source for each feature label. DB_SOURCES = { "Start codon (ATG) context": "RefSeq", @@ -82,6 +149,80 @@ def _random_dna(rng: np.random.Generator, length: int) -> str: return "".join(rng.choice(list("ACGT"), size=length)) +def _build_pwm(rng: np.random.Generator, label: str | None) -> np.ndarray: + """Build a (PWM_WINDOW, 4) probability PWM for one feature label. + + Central signature pulled from PWM_SIGNATURES (or near-uniform for unlabeled + features). Flanks are exactly uniform (0 bits — blank logomaker columns) + so the logo reads as "this is the motif, everything else is background" + instead of a sea of tiny speckle letters. + """ + pwm = np.zeros((PWM_WINDOW, 4)) + uniform = np.full(4, 0.25) + signature = PWM_SIGNATURES.get(label) if label else None + + if signature is None: + # Unlabeled feature: essentially uniform — a very tight Dirichlet draw + # produces a flat, mostly-blank logo with no spurious consensus. + return np.tile(uniform, (PWM_WINDOW, 1)) + + sig_len = len(signature) + sig_start = PWM_PEAK - sig_len // 2 # center the signature on the activation peak + for i in range(PWM_WINDOW): + sig_idx = i - sig_start + if 0 <= sig_idx < sig_len: + spec = signature[sig_idx] + row = np.zeros(4) + for base, prob in spec.items(): + row[PWM_BASES.index(base)] = prob + # Distribute the remainder evenly across unspecified bases — no + # randomness, so secondary letters stay symmetric and quiet. + remainder = max(0.0, 1.0 - sum(spec.values())) + unspec = [b for b in PWM_BASES if b not in spec] + if unspec and remainder > 0: + share = remainder / len(unspec) + for b in unspec: + row[PWM_BASES.index(b)] = share + row = np.clip(row, 1e-6, None) + row /= row.sum() + pwm[i] = row + else: + # Flank: exactly uniform -> 0 bits -> blank column in the logo. + pwm[i] = uniform + + return pwm + + +def _render_logo(pwm: np.ndarray, feature_id: int, out_dir: Path) -> Path: + """Render a WebLogo-style PNG for one feature's PWM using logomaker. + + The information transform produces letter heights in bits (0..2). Position + labels are relative to the activation peak (-PWM_PEAK..+PWM_PEAK). + """ + import matplotlib + + matplotlib.use("Agg") # headless backend — safe for cron/CI + import logomaker + import matplotlib.pyplot as plt + + df = pd.DataFrame(pwm, columns=PWM_BASES) + info_df = logomaker.transform_matrix(df, from_type="probability", to_type="information") + + fig, ax = plt.subplots(figsize=(6, 1.8)) + logomaker.Logo(info_df, ax=ax, color_scheme="classic") + ax.set_xticks(range(PWM_WINDOW)) + ax.set_xticklabels([str(i - PWM_PEAK) for i in range(PWM_WINDOW)], fontsize=8) + ax.set_ylabel("Bits") + ax.set_ylim(0, 2) + ax.set_xlabel("Position relative to peak") + fig.tight_layout() + + out_path = out_dir / f"feature_{feature_id}.png" + fig.savefig(out_path, dpi=120, bbox_inches="tight") + plt.close(fig) + return out_path + + def _make_example(rng: np.random.Generator, label: str, feature_max: float, window: int = 200) -> dict: """Build one top-activating example: 200bp window with a central motif + a gaussian activation bump.""" seq = list(_random_dna(rng, window)) @@ -136,6 +277,7 @@ def _make_features(rng: np.random.Generator) -> list[dict]: "top_positive_logits": [], "top_negative_logits": [], "examples": examples, + "logo_path": f"/logos/feature_{fid}.png", } ) return features @@ -195,6 +337,7 @@ def _make_atlas(rng: np.random.Generator, features: list[dict]) -> pd.DataFrame: "log_frequency": [round(float(np.log10(f["activation_freq"])), 4) for f in features], "max_activation": [f["max_activation"] for f in features], "cluster_id": cluster_ids, + "logo_path": [f["logo_path"] for f in features], } ) @@ -242,6 +385,9 @@ def _make_unlabeled_features(rng: np.random.Generator, n: int, start_id: int) -> "top_positive_logits": [], "top_negative_logits": [], "examples": examples, + # No motif means no logo — the UI will fall back to its + # "no logo available" empty-state for unlabeled features. + "logo_path": None, } ) return out @@ -296,6 +442,20 @@ def main(): features = _make_features(rng) features += _make_unlabeled_features(rng, n=args.n_unlabeled, start_id=len(features)) + # Render one WebLogo PNG per labeled feature into /logos/. Unlabeled + # features get no logo — an empty WebLogo reads as a render bug, so we let + # the dashboard show its "no logo" empty state instead. + logo_dir = args.output_dir / "logos" + logo_dir.mkdir(parents=True, exist_ok=True) + rendered = 0 + for f in features: + if f["label"] is None: + continue + pwm = _build_pwm(rng, f["label"]) + _render_logo(pwm, f["feature_id"], logo_dir) + rendered += 1 + print(f"Wrote {rendered} logo PNGs -> {logo_dir}") + atlas = _make_atlas(rng, features) atlas.to_parquet(args.output_dir / "features_atlas.parquet", index=False) # feature_metadata is the same shape as the atlas for the mockup — the dashboard From 736571ddcf06fddcabca061c099beef4560a6824 Mon Sep 17 00:00:00 2001 From: Polina Binder Date: Wed, 27 May 2026 23:48:40 +0000 Subject: [PATCH 07/12] evo2 dashboard: ColoredSequence + GeneUMAPView + 500-gene precompute Two new visualizations for the SAE interpretability dashboard, plus the offline pipeline that produces the gene-UMAP precompute bundle. scripts/generate_fake_genes.py 500-row genes.tsv stand-in (gene_symbol, species, sequence) until a real curated catalog lands. Realistic-ish distributions across 7 species. scripts/gene_umap_precompute.py End-to-end offline pipeline: genes.tsv -> Evo2 1B layer-20 -> TopK SAE encode -> mean per gene -> UMAP (cosine) -> HDBSCAN clusters -> per-feature firing stats. Writes G.npz, genes_umap.parquet, feature_stats.parquet, manifest.json. Reuses predict_evo2 via torchrun subprocess; aggregates .pt files by seq_idx + pad_mask. Idempotent (skips predict if .pt files exist). src/ColoredSequence.jsx React component: paste a DNA sequence -> each base background-colored by its top-firing SAE feature, opacity scaled by activation strength. Two modes: top-feature (default), single-feature lookup. Builds mock activations internally when no `analysis` prop is supplied so the component works standalone before the /analyze backend is wired. Tableau-10 colorblind palette, hover tooltip with top-5 features, legend sorted by per-color position count. src/GeneUMAPView.jsx Renders the 500-gene UMAP via canvas. Loads G.bin (raw float32), genes_meta.json, feature_stats.json from public/gene_umap/. Click a feature in the sidebar -> instant recolor by activation strength (no recompute). Click Reorganize -> re-runs UMAP client-side with feature-weighted vectors (umap-js, ~2-5s at N=500), animates the transition with ease-in-out cubic. Hover shows gene metadata + top 5 firing features. src/Preview.jsx + src/index.jsx Tabbed entry at /#preview: "Main" (the existing dashboard, untouched), "ColoredSequence", "Gene UMAP". Hash-gated so / still goes to the unchanged production layout. The ColoredSequence tab includes a paste textarea so users can drop their own sequences in. public/gene_umap/ Precomputed bundle for the GeneUMAPView (G.bin 30 MB, plus small JSON metadata + per-feature stats filtered to n_firing >= 10). Dep change: umap-js for client-side reorganize. Generated genes are synthetic; replace fake_genes.tsv with a real curated 500-gene list and re-run the precompute when one is available. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../evo2/evo2_dashboard_mockup/package.json | 3 +- .../public/gene_umap/feature_stats.json | 1 + .../public/gene_umap/genes_meta.json | 1 + .../src/ColoredSequence.jsx | 422 ++++++++++++++ .../src/GeneUMAPView.jsx | 526 ++++++++++++++++++ .../evo2_dashboard_mockup/src/Preview.jsx | 262 +++++++++ .../evo2/evo2_dashboard_mockup/src/index.jsx | 7 +- .../evo2/scripts/gene_umap_precompute.py | 289 ++++++++++ .../evo2/scripts/generate_fake_genes.py | 104 ++++ 9 files changed, 1613 insertions(+), 2 deletions(-) create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/gene_umap/feature_stats.json create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/gene_umap/genes_meta.json create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/ColoredSequence.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/GeneUMAPView.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/Preview.jsx create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/gene_umap_precompute.py create mode 100644 bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/scripts/generate_fake_genes.py diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/package.json b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/package.json index 82810c8cd0..53674056a3 100644 --- a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/package.json +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/package.json @@ -15,7 +15,8 @@ "embedding-atlas": "^0.16.1", "lucide-react": "^0.577.0", "react": "^18.2.0", - "react-dom": "^18.2.0" + "react-dom": "^18.2.0", + "umap-js": "^1.4.0" }, "devDependencies": { "@vitejs/plugin-react": "^4.2.0", diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/gene_umap/feature_stats.json b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/gene_umap/feature_stats.json new file mode 100644 index 0000000000..5c01d6f51e --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/public/gene_umap/feature_stats.json @@ -0,0 +1 @@ +[{"feature_id": 854, "n_firing": 500, "mean_act_when_firing": 0.5735225219726563}, {"feature_id": 14158, "n_firing": 500, "mean_act_when_firing": 0.422054931640625}, {"feature_id": 14972, "n_firing": 500, "mean_act_when_firing": 0.0019399394989013672}, {"feature_id": 753, "n_firing": 500, "mean_act_when_firing": 0.012941115379333496}, {"feature_id": 14430, 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"Saccharomyces cerevisiae", "x": 1.5776240825653076, "y": -1.7245346307754517, "cluster_id": 12}, {"gene_symbol": "SOX6", "species": "Saccharomyces cerevisiae", "x": 0.940380334854126, "y": -2.373828649520874, "cluster_id": -1}, {"gene_symbol": "PIK35", "species": "Drosophila melanogaster", "x": -1.6441000699996948, "y": 3.4560630321502686, "cluster_id": 14}, {"gene_symbol": "SOX22", "species": "Escherichia coli", "x": 8.983061790466309, "y": -3.2110300064086914, "cluster_id": 3}, {"gene_symbol": "VEGF3", "species": "Escherichia coli", "x": -1.8818421363830566, "y": 5.850708484649658, "cluster_id": 17}, {"gene_symbol": "ERK7", "species": "Escherichia coli", "x": -0.6577836871147156, "y": 1.291436791419983, "cluster_id": 11}, {"gene_symbol": "NOTCH20", "species": "Saccharomyces cerevisiae", "x": 1.4435938596725464, "y": -2.739870309829712, "cluster_id": 12}, {"gene_symbol": "HOX14", "species": "Mus musculus", "x": -2.1256144046783447, "y": 7.13798189163208, "cluster_id": 16}, {"gene_symbol": "RB19", "species": "Homo sapiens", "x": -1.8379857540130615, "y": 1.7883915901184082, "cluster_id": 11}, {"gene_symbol": "NFKB14", "species": "Escherichia coli", "x": 8.513481140136719, "y": -3.1021852493286133, "cluster_id": 3}, {"gene_symbol": "RPL16", "species": "Homo sapiens", "x": -1.8401623964309692, "y": 6.060258865356445, "cluster_id": 17}, {"gene_symbol": "P538", "species": "Arabidopsis thaliana", "x": 1.5242985486984253, "y": -1.4720219373703003, "cluster_id": 12}]} \ No newline at end of file diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/ColoredSequence.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/ColoredSequence.jsx new file mode 100644 index 0000000000..a6c9a6d4ca --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/ColoredSequence.jsx @@ -0,0 +1,422 @@ +import React, { useMemo, useState } from 'react' + +// Tableau 10 colorblind-friendlier palette. Feature IDs map to colors by +// (feature_id % palette.length). Same feature -> same color, deterministically. +const PALETTE = [ + '#4E79A7', // blue + '#F28E2B', // orange + '#E15759', // red + '#76B7B2', // teal + '#59A14F', // green + '#EDC948', // yellow + '#B07AA1', // purple + '#FF9DA7', // pink + '#9C755F', // brown + '#BAB0AC', // gray + '#499894', // dark teal + '#D37295', // dark pink +] + +// Show plain text (no background) when the top feature's activation is below +// this fraction of the sequence-wide max. Tweak if "interesting" regions +// look too sparse or too noisy. +const ACTIVATION_FLOOR_FRAC = 0.10 + +// Opacity floor + ceiling. Keep ceiling well below 1 so the base letter +// stays readable against the colored background. +const OPACITY_FLOOR = 0.15 +const OPACITY_CEILING = 0.85 + +// Line-wrap at 60 bases — standard biology convention (FASTA, GenBank). +const BASES_PER_LINE = 60 + + +function colorForFeature(featureId) { + if (featureId == null || featureId < 0) return null + return PALETTE[featureId % PALETTE.length] +} + + +function labelForFeature(featureId, catalog) { + if (!catalog || featureId == null || featureId < 0) return `feature_${featureId}` + const entry = catalog[featureId] || catalog.get?.(featureId) + if (!entry) return `feature_${featureId}` + return entry.label || entry.description || `feature_${featureId}` +} + + +// Build mock /analyze-shaped data for development without the backend. +function buildMockAnalysis(sequence) { + const length = sequence.length + // 5 hand-rolled features with locality patterns so colored regions are + // visible. Real backend output will have ~10 top features per position. + const FEATURES = [101, 207, 314, 422, 588] + const topk_features = [] + const topk_activations = [] + for (let i = 0; i < length; i++) { + const acts = [0, 0, 0, 0, 0] + // Feature 101: peaks around bases 50-100 + acts[0] = Math.max(0, 0.8 - Math.abs(i - 75) / 30) + // Feature 207: peaks at start codon-ish positions (every 90 bases) + acts[1] = Math.exp(-Math.pow((i % 90) - 5, 2) / 10) * 0.6 + // Feature 314: peaks in middle third + acts[2] = i > length / 3 && i < (2 * length) / 3 ? 0.4 + 0.3 * Math.sin(i / 7) : 0 + // Feature 422: low baseline + occasional spikes + acts[3] = i % 17 === 3 ? 0.7 : 0.05 + // Feature 588: tapering toward end + acts[4] = Math.max(0, (i - length / 2) / (length / 2)) * 0.5 + // Sort descending, keep top 10 (or all 5 here) + const pairs = FEATURES.map((f, k) => [f, acts[k]]).sort((a, b) => b[1] - a[1]) + topk_features.push(pairs.map((p) => p[0])) + topk_activations.push(pairs.map((p) => p[1])) + } + return { length, topk_features, topk_activations } +} + + +export default function ColoredSequence({ + sequence, + analysis, + featureCatalog, + mode = 'top', // 'top' | 'single' + singleFeatureId = null, + onBaseClick, + baseFontSize = 13, +}) { + // Fall back to mock data when no analysis prop is supplied. Pluggable: pass + // a real /analyze response and the component renders the real signal. + const data = useMemo(() => analysis ?? buildMockAnalysis(sequence), [sequence, analysis]) + + // Sequence-wide max activation across top-1 per position. Used to normalize + // each position's opacity into [OPACITY_FLOOR, OPACITY_CEILING]. + const maxAct = useMemo(() => { + if (mode === 'single' && singleFeatureId != null) { + let m = 0 + for (let i = 0; i < sequence.length; i++) { + const feats = data.topk_features[i] || [] + const acts = data.topk_activations[i] || [] + const idx = feats.indexOf(singleFeatureId) + if (idx >= 0 && acts[idx] > m) m = acts[idx] + } + return m + } + let m = 0 + for (let i = 0; i < sequence.length; i++) { + const a = data.topk_activations[i]?.[0] ?? 0 + if (a > m) m = a + } + return m + }, [sequence, data, mode, singleFeatureId]) + + // Count how many positions each color shows up at — drives the legend. + const colorUsage = useMemo(() => { + const counts = new Map() // featureId -> count + for (let i = 0; i < sequence.length; i++) { + const feats = data.topk_features[i] || [] + const acts = data.topk_activations[i] || [] + if (mode === 'single') { + if (singleFeatureId == null) continue + const idx = feats.indexOf(singleFeatureId) + if (idx < 0 || acts[idx] < ACTIVATION_FLOOR_FRAC * maxAct) continue + counts.set(singleFeatureId, (counts.get(singleFeatureId) || 0) + 1) + } else { + const f = feats[0] + const a = acts[0] ?? 0 + if (f == null || a < ACTIVATION_FLOOR_FRAC * maxAct) continue + counts.set(f, (counts.get(f) || 0) + 1) + } + } + return [...counts.entries()].sort((a, b) => b[1] - a[1]) + }, [sequence, data, mode, singleFeatureId, maxAct]) + + // Pre-compute per-base styling + tooltip data. Keeps render fast even on + // long sequences (e.g. 5 kb genes). + const baseStyles = useMemo(() => { + const styles = [] + for (let i = 0; i < sequence.length; i++) { + const feats = data.topk_features[i] || [] + const acts = data.topk_activations[i] || [] + let featureId = null + let activation = 0 + if (mode === 'single' && singleFeatureId != null) { + const idx = feats.indexOf(singleFeatureId) + if (idx >= 0) { + featureId = singleFeatureId + activation = acts[idx] + } + } else { + featureId = feats[0] + activation = acts[0] ?? 0 + } + const color = colorForFeature(featureId) + const threshold = ACTIVATION_FLOOR_FRAC * maxAct + let bg = 'transparent' + if (color && activation >= threshold && maxAct > 0) { + const t = Math.min(1, activation / maxAct) + const alpha = OPACITY_FLOOR + t * (OPACITY_CEILING - OPACITY_FLOOR) + bg = hexWithAlpha(color, alpha) + } + styles.push({ bg, featureId, activation }) + } + return styles + }, [sequence, data, mode, singleFeatureId, maxAct]) + + // Split the sequence into lines of BASES_PER_LINE for the standard + // sequence-view layout (position numbers at line breaks). + const lines = [] + for (let start = 0; start < sequence.length; start += BASES_PER_LINE) { + const end = Math.min(start + BASES_PER_LINE, sequence.length) + lines.push({ start, end, bases: sequence.slice(start, end) }) + } + + return ( +
+
+ {lines.map((line) => ( +
+ + {String(line.start + 1).padStart(6, ' ')} + + + {[...line.bases].map((base, j) => { + const i = line.start + j + const { bg, featureId, activation } = baseStyles[i] + return ( + + ) + })} + +
+ ))} +
+ + +
+ ) +} + + +function Base({ + base, + position, + background, + featureId, + activation, + topFeatures, + topActivations, + featureCatalog, + onClick, +}) { + const [hover, setHover] = useState(false) + const handleClick = (e) => { + e.stopPropagation() + onClick?.({ position, base, topFeatures, topActivations }) + } + return ( + setHover(true)} + onMouseLeave={() => setHover(false)} + onClick={handleClick} + style={{ + background, + padding: '0 1px', + borderRadius: '2px', + cursor: 'pointer', + position: 'relative', + }} + > + {base} + {hover && ( + + )} + + ) +} + + +function Tooltip({ position, base, topFeatures, topActivations, featureCatalog }) { + const rows = topFeatures.slice(0, 5).map((f, k) => ({ + featureId: f, + label: labelForFeature(f, featureCatalog), + activation: topActivations[k] ?? 0, + color: colorForFeature(f), + })) + return ( +
+
+ pos {position + 1} (base {base}) +
+ + + {rows.map((r) => ( + + + + + ))} + +
+ {r.label} + {r.activation.toFixed(3)}
+
+ ) +} + + +function Legend({ usage, catalog, maxAct }) { + if (usage.length === 0) { + return
No features above activation threshold
+ } + return ( +
+
Colors in this sequence (by # of positions):
+
+ {usage.map(([fid, n]) => ( + + + {labelForFeature(fid, catalog)} + ×{n} + + ))} +
+
max activation in sequence: {maxAct.toFixed(3)}
+
+ ) +} + + +// Add an alpha channel to a 6-digit hex color. Keeps the palette source as +// plain hex so palette swaps don't have to redo opacity math. +function hexWithAlpha(hex, alpha) { + const a = Math.round(alpha * 255).toString(16).padStart(2, '0') + return `${hex}${a}` +} + + +const styles = { + container: { + fontFamily: 'monospace', + background: 'var(--bg-card-expanded)', + border: '1px solid var(--border-light)', + borderRadius: '6px', + padding: '10px 12px', + }, + sequenceBlock: { + whiteSpace: 'pre-wrap', + lineHeight: '1.5', + color: 'var(--text)', + }, + line: { + display: 'flex', + gap: '8px', + alignItems: 'baseline', + }, + position: { + color: 'var(--text-muted)', + userSelect: 'none', + fontFamily: 'monospace', + minWidth: '50px', + textAlign: 'right', + }, + bases: { + fontFamily: 'monospace', + letterSpacing: '0.5px', + }, + tooltip: { + position: 'absolute', + top: '110%', + left: '50%', + transform: 'translateX(-50%)', + zIndex: 100, + background: 'var(--bg-card)', + border: '1px solid var(--border)', + borderRadius: '4px', + padding: '6px 8px', + fontSize: '11px', + minWidth: '220px', + boxShadow: '0 2px 8px rgba(0,0,0,0.2)', + pointerEvents: 'none', + fontFamily: 'system-ui, sans-serif', + }, + tooltipHeader: { + fontWeight: 600, + marginBottom: '4px', + paddingBottom: '4px', + borderBottom: '1px solid var(--border-light)', + color: 'var(--text-heading)', + }, + tooltipTable: { + width: '100%', + borderCollapse: 'collapse', + }, + tooltipActCell: { + textAlign: 'right', + fontFamily: 'monospace', + color: 'var(--text-secondary)', + }, + colorDot: { + display: 'inline-block', + width: '8px', + height: '8px', + borderRadius: '2px', + marginRight: '6px', + verticalAlign: 'middle', + }, + legend: { + marginTop: '10px', + paddingTop: '8px', + borderTop: '1px solid var(--border-light)', + fontSize: '11px', + fontFamily: 'system-ui, sans-serif', + color: 'var(--text-secondary)', + }, + legendTitle: { + fontSize: '10px', + textTransform: 'uppercase', + color: 'var(--text-tertiary)', + marginBottom: '6px', + }, + legendItems: { + display: 'flex', + flexWrap: 'wrap', + gap: '12px', + }, + legendItem: { + display: 'inline-flex', + alignItems: 'center', + fontSize: '11px', + }, + legendCount: { + marginLeft: '4px', + fontFamily: 'monospace', + color: 'var(--text-muted)', + }, + legendMeta: { + marginTop: '6px', + fontSize: '10px', + color: 'var(--text-muted)', + }, + legendEmpty: { + fontSize: '11px', + fontStyle: 'italic', + color: 'var(--text-muted)', + marginTop: '8px', + }, +} diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/GeneUMAPView.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/GeneUMAPView.jsx new file mode 100644 index 0000000000..c7c54e7dd4 --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/GeneUMAPView.jsx @@ -0,0 +1,526 @@ +import React, { useEffect, useMemo, useRef, useState } from 'react' +import { UMAP } from 'umap-js' + +// 500-gene UMAP view with feature-conditional re-embedding. +// +// Loads three precomputed files from /gene_umap/ (in the dashboard's public/): +// - G.bin: float32 [n_genes * n_features] raw bytes +// - genes_meta.json: per-gene metadata + base UMAP coords + HDBSCAN cluster +// - feature_stats.json: features with n_firing >= 10 (clickable list) +// +// Interactions: +// - Click a feature -> recolor every gene point by G[gene_idx, feature_idx]. +// - Click "Reorganize" -> re-run UMAP client-side on feature-weighted vectors +// (G[i, :] * (1 + lambda * indicator(feature == clicked))). Animate transition. +// - Hover gene -> tooltip with symbol, species, cluster, top 5 firing features. + +const LAMBDA = 5.0 +const REORG_ANIM_MS = 800 + +// Tableau-10-ish palette used for HDBSCAN cluster coloring. Index -1 (noise) +// maps to grey at the end. +const CLUSTER_PALETTE = [ + '#4E79A7', '#F28E2B', '#E15759', '#76B7B2', '#59A14F', + '#EDC948', '#B07AA1', '#FF9DA7', '#9C755F', '#499894', + '#D37295', '#86BCB6', '#FABFD2', '#D7B5A6', '#BA9582', + '#A0CBE8', '#FFBE7D', '#8CD17D', '#B6992D', '#79706E', + '#D4A6C8', +] +const NOISE_COLOR = '#BAB0AC' + + +function colorForCluster(cid) { + if (cid == null || cid < 0) return NOISE_COLOR + return CLUSTER_PALETTE[cid % CLUSTER_PALETTE.length] +} + + +// Linear viridis-ish ramp for activation-strength coloring. +function colorForActivation(t) { + // t in [0, 1]. Dark blue at 0 -> yellow at 1. + const stops = [ + [0.0, [68, 1, 84]], + [0.25, [59, 82, 139]], + [0.5, [33, 145, 140]], + [0.75, [94, 201, 98]], + [1.0, [253, 231, 37]], + ] + for (let i = 1; i < stops.length; i++) { + if (t <= stops[i][0]) { + const [t0, c0] = stops[i - 1] + const [t1, c1] = stops[i] + const u = (t - t0) / (t1 - t0) + const r = Math.round(c0[0] + u * (c1[0] - c0[0])) + const g = Math.round(c0[1] + u * (c1[1] - c0[1])) + const b = Math.round(c0[2] + u * (c1[2] - c0[2])) + return `rgb(${r}, ${g}, ${b})` + } + } + const [, c] = stops[stops.length - 1] + return `rgb(${c[0]}, ${c[1]}, ${c[2]})` +} + + +// Load the precomputed gene-UMAP bundle: G.bin (binary float32), genes_meta.json, +// feature_stats.json. Returns { G, genes, featureStats, n_genes, n_features }. +async function loadGeneUMAPBundle(baseURL = '/gene_umap') { + const [gMetaResp, fStatsResp, gBinResp] = await Promise.all([ + fetch(`${baseURL}/genes_meta.json`), + fetch(`${baseURL}/feature_stats.json`), + fetch(`${baseURL}/G.bin`), + ]) + const genesMeta = await gMetaResp.json() + const featureStats = await fStatsResp.json() + const gBuffer = await gBinResp.arrayBuffer() + // float32 little-endian (assumes precompute ran on a little-endian host; + // x86/ARM both fit). + const G = new Float32Array(gBuffer) + return { + G, + genes: genesMeta.genes, + n_genes: genesMeta.n_genes, + n_features: genesMeta.n_features, + featureStats, + } +} + + +export default function GeneUMAPView({ height = 600, bundleURL = '/gene_umap' }) { + const [bundle, setBundle] = useState(null) + const [error, setError] = useState(null) + const [selectedFeature, setSelectedFeature] = useState(null) + const [reorgCoords, setReorgCoords] = useState(null) // null = use base coords + const [reorgFeatureId, setReorgFeatureId] = useState(null) + const [reorgRunning, setReorgRunning] = useState(false) + const [animFrame, setAnimFrame] = useState(1.0) // 0 -> 1 transition factor + const [hoverIdx, setHoverIdx] = useState(null) + const canvasRef = useRef(null) + + useEffect(() => { + loadGeneUMAPBundle(bundleURL) + .then((b) => setBundle(b)) + .catch((e) => setError(e.message)) + }, [bundleURL]) + + // Build the per-point color array based on current selection mode. + const pointColors = useMemo(() => { + if (!bundle) return null + const { G, n_features, genes } = bundle + if (selectedFeature == null) { + return genes.map((g) => colorForCluster(g.cluster_id)) + } + // Feature-strength coloring. + let maxAct = 0 + for (let i = 0; i < genes.length; i++) { + const v = G[i * n_features + selectedFeature] + if (v > maxAct) maxAct = v + } + return genes.map((g, i) => { + const v = G[i * n_features + selectedFeature] + if (maxAct <= 0) return NOISE_COLOR + return colorForActivation(v / maxAct) + }) + }, [bundle, selectedFeature]) + + // Resolve current displayed coords with smooth interpolation between base + // and reorg coords during the transition window. + const displayCoords = useMemo(() => { + if (!bundle) return null + const base = bundle.genes.map((g) => [g.x, g.y]) + if (reorgCoords == null) return base + return base.map((b, i) => { + const r = reorgCoords[i] + const t = animFrame + return [b[0] + (r[0] - b[0]) * t, b[1] + (r[1] - b[1]) * t] + }) + }, [bundle, reorgCoords, animFrame]) + + // Draw to canvas whenever coords or colors change. + useEffect(() => { + if (!bundle || !displayCoords || !pointColors) return + const canvas = canvasRef.current + if (!canvas) return + const ctx = canvas.getContext('2d') + const w = canvas.width + const h = canvas.height + ctx.clearRect(0, 0, w, h) + // Scale displayCoords -> canvas pixels. + let minX = Infinity, maxX = -Infinity, minY = Infinity, maxY = -Infinity + for (const [x, y] of displayCoords) { + if (x < minX) minX = x + if (x > maxX) maxX = x + if (y < minY) minY = y + if (y > maxY) maxY = y + } + const pad = 30 + const sx = (w - 2 * pad) / Math.max(1e-9, maxX - minX) + const sy = (h - 2 * pad) / Math.max(1e-9, maxY - minY) + const s = Math.min(sx, sy) + const ox = pad + ((w - 2 * pad) - s * (maxX - minX)) / 2 + const oy = pad + ((h - 2 * pad) - s * (maxY - minY)) / 2 + for (let i = 0; i < displayCoords.length; i++) { + const [x, y] = displayCoords[i] + const px = ox + (x - minX) * s + const py = oy + (y - minY) * s + ctx.fillStyle = pointColors[i] + ctx.globalAlpha = hoverIdx != null && i !== hoverIdx ? 0.4 : 1.0 + ctx.beginPath() + ctx.arc(px, py, hoverIdx === i ? 6 : 4, 0, Math.PI * 2) + ctx.fill() + } + ctx.globalAlpha = 1.0 + }, [bundle, displayCoords, pointColors, hoverIdx]) + + // Animate from base coords -> new reorg coords with requestAnimationFrame. + useEffect(() => { + if (reorgCoords == null) { + setAnimFrame(1.0) + return + } + let raf + const start = performance.now() + const tick = (now) => { + const t = Math.min(1, (now - start) / REORG_ANIM_MS) + // Ease-in-out cubic. + const eased = t < 0.5 ? 4 * t * t * t : 1 - Math.pow(-2 * t + 2, 3) / 2 + setAnimFrame(eased) + if (t < 1) raf = requestAnimationFrame(tick) + } + setAnimFrame(0) + raf = requestAnimationFrame(tick) + return () => cancelAnimationFrame(raf) + }, [reorgCoords]) + + const handleClickFeature = (fid) => { + setSelectedFeature(fid === selectedFeature ? null : fid) + } + + const handleReorganize = async () => { + if (!bundle || selectedFeature == null) return + setReorgRunning(true) + setReorgFeatureId(selectedFeature) + // Build feature-weighted vectors (umap-js doesn't accept precomputed + // distances, so encode the weighting into the vector itself). + const { G, n_features, n_genes } = bundle + const vecs = new Array(n_genes) + for (let i = 0; i < n_genes; i++) { + const row = new Float32Array(n_features) + const base = i * n_features + for (let f = 0; f < n_features; f++) row[f] = G[base + f] + // Amplify the selected feature: row[f] *= (1 + LAMBDA) if f == selected + // This pulls points with high activation of the clicked feature closer. + row[selectedFeature] *= 1 + LAMBDA + vecs[i] = Array.from(row) + } + // Defer to next tick so the spinner renders before the heavy compute. + await new Promise((r) => setTimeout(r, 16)) + const reducer = new UMAP({ nComponents: 2, nNeighbors: 15, minDist: 0.1 }) + const coords = reducer.fit(vecs) + setReorgCoords(coords) + setReorgRunning(false) + } + + const handleResetLayout = () => { + setReorgCoords(null) + setReorgFeatureId(null) + } + + if (error) { + return
Failed to load gene UMAP bundle: {error}
+ } + if (!bundle) { + return
Loading 500-gene UMAP…
+ } + + return ( +
+
+ Gene UMAP — {bundle.n_genes} genes × {bundle.n_features} features +
+ + {reorgCoords && ( + + )} +
+
+ +
+
+ { + if (!displayCoords) return + const rect = e.currentTarget.getBoundingClientRect() + const mx = ((e.clientX - rect.left) / rect.width) * 700 + const my = ((e.clientY - rect.top) / rect.height) * (height - 20) + // Re-derive transform (same as draw effect). + let minX = Infinity, maxX = -Infinity, minY = Infinity, maxY = -Infinity + for (const [x, y] of displayCoords) { + if (x < minX) minX = x + if (x > maxX) maxX = x + if (y < minY) minY = y + if (y > maxY) maxY = y + } + const pad = 30 + const w = 700, h = height - 20 + const sx = (w - 2 * pad) / Math.max(1e-9, maxX - minX) + const sy = (h - 2 * pad) / Math.max(1e-9, maxY - minY) + const s = Math.min(sx, sy) + const ox = pad + ((w - 2 * pad) - s * (maxX - minX)) / 2 + const oy = pad + ((h - 2 * pad) - s * (maxY - minY)) / 2 + let best = null + let bestD2 = 100 // pixel² threshold for "hover" + for (let i = 0; i < displayCoords.length; i++) { + const [x, y] = displayCoords[i] + const px = ox + (x - minX) * s + const py = oy + (y - minY) * s + const d2 = (px - mx) ** 2 + (py - my) ** 2 + if (d2 < bestD2) { + bestD2 = d2 + best = i + } + } + setHoverIdx(best) + }} + onMouseLeave={() => setHoverIdx(null)} + /> + {hoverIdx != null && bundle.genes[hoverIdx] && ( + + )} + {reorgCoords && reorgFeatureId != null && ( +
+ layout: feature {reorgFeatureId} emphasized +
+ )} +
+ +
+
+ Features (n_firing ≥ 10) · {bundle.featureStats.length} +
+
+ {bundle.featureStats.slice(0, 80).map((fs) => ( +
handleClickFeature(fs.feature_id)} + style={{ + ...styles.featureRow, + background: + selectedFeature === fs.feature_id ? 'var(--bg-card-expanded)' : 'transparent', + borderLeft: + selectedFeature === fs.feature_id + ? '3px solid var(--accent)' + : '3px solid transparent', + }} + > + #{fs.feature_id} + + {fs.n_firing} genes · μ={fs.mean_act_when_firing.toFixed(2)} + +
+ ))} + {bundle.featureStats.length > 80 && ( +
… + {bundle.featureStats.length - 80} more
+ )} +
+
+
+
+ ) +} + + +function HoverTooltip({ gene, G, idx, bundle }) { + // Compute top 5 firing features for this gene (no per-gene cache for brevity). + const top5 = useMemo(() => { + const base = idx * bundle.n_features + const pairs = [] + for (let f = 0; f < bundle.n_features; f++) { + const v = G[base + f] + if (v > 0) pairs.push([f, v]) + } + pairs.sort((a, b) => b[1] - a[1]) + return pairs.slice(0, 5) + }, [G, idx, bundle]) + return ( +
+
{gene.gene_symbol}
+
{gene.species}
+
+ cluster: {gene.cluster_id < 0 ? 'noise' : gene.cluster_id} +
+
top 5 features
+ {top5.map(([f, v]) => ( +
+ #{f} + {v.toFixed(3)} +
+ ))} +
+ ) +} + + +const styles = { + container: { + background: 'var(--bg-card)', + border: '1px solid var(--border)', + borderRadius: '8px', + padding: '12px', + }, + toolbar: { + display: 'flex', + justifyContent: 'space-between', + alignItems: 'center', + marginBottom: '10px', + }, + title: { + fontSize: '13px', + fontWeight: 600, + color: 'var(--text-heading)', + }, + toolbarActions: { display: 'flex', gap: '8px' }, + btn: { + padding: '4px 12px', + border: '1px solid var(--border-input)', + borderRadius: '4px', + background: 'var(--bg-input)', + fontSize: '11px', + cursor: 'pointer', + color: 'var(--text-secondary)', + }, + btnPrimary: { + padding: '4px 12px', + border: '1px solid var(--accent)', + borderRadius: '4px', + background: 'var(--accent)', + fontSize: '11px', + cursor: 'pointer', + color: 'white', + fontWeight: 500, + }, + btnDisabled: { + padding: '4px 12px', + border: '1px solid var(--border-light)', + borderRadius: '4px', + background: 'var(--bg-input)', + fontSize: '11px', + color: 'var(--text-muted)', + cursor: 'not-allowed', + }, + canvasWrap: { + flex: 1, + position: 'relative', + background: 'var(--bg-card-expanded)', + border: '1px solid var(--border-light)', + borderRadius: '6px', + overflow: 'hidden', + }, + reorgBadge: { + position: 'absolute', + top: '8px', + left: '8px', + background: 'rgba(0,0,0,0.6)', + color: '#fff', + padding: '3px 8px', + borderRadius: '3px', + fontSize: '10px', + fontFamily: 'monospace', + }, + sidebar: { + width: '240px', + flexShrink: 0, + display: 'flex', + flexDirection: 'column', + background: 'var(--bg-card-expanded)', + border: '1px solid var(--border-light)', + borderRadius: '6px', + overflow: 'hidden', + }, + sidebarTitle: { + fontSize: '10px', + fontWeight: 600, + textTransform: 'uppercase', + color: 'var(--text-tertiary)', + padding: '8px 12px', + borderBottom: '1px solid var(--border-light)', + }, + featureList: { + overflow: 'auto', + flex: 1, + fontSize: '11px', + fontFamily: 'monospace', + }, + featureRow: { + display: 'flex', + justifyContent: 'space-between', + padding: '4px 9px', + cursor: 'pointer', + userSelect: 'none', + }, + featureId: { color: 'var(--text-heading)' }, + featureCount: { color: 'var(--text-muted)' }, + featureMore: { + padding: '6px 9px', + color: 'var(--text-muted)', + fontStyle: 'italic', + fontSize: '10px', + }, + tooltip: { + position: 'absolute', + pointerEvents: 'none', + top: '10px', + right: '10px', + background: 'var(--bg-card)', + border: '1px solid var(--border)', + borderRadius: '4px', + padding: '8px 10px', + fontSize: '11px', + fontFamily: 'system-ui, sans-serif', + minWidth: '180px', + boxShadow: '0 2px 8px rgba(0,0,0,0.2)', + color: 'var(--text)', + }, + tooltipTitle: { fontWeight: 600, fontSize: '13px', color: 'var(--text-heading)' }, + tooltipMeta: { color: 'var(--text-secondary)', fontSize: '11px' }, + tooltipSection: { + fontSize: '10px', + textTransform: 'uppercase', + color: 'var(--text-tertiary)', + marginTop: '8px', + paddingTop: '6px', + borderTop: '1px solid var(--border-light)', + }, + tooltipRow: { + display: 'flex', + justifyContent: 'space-between', + fontFamily: 'monospace', + fontSize: '11px', + }, + tooltipAct: { color: 'var(--text-secondary)' }, + loading: { + padding: '40px', + textAlign: 'center', + color: 'var(--text-muted)', + fontStyle: 'italic', + }, + error: { + padding: '20px', + color: '#c34', + background: '#fee', + borderRadius: '4px', + fontFamily: 'monospace', + fontSize: '12px', + }, +} diff --git a/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/Preview.jsx b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/Preview.jsx new file mode 100644 index 0000000000..b46360146b --- /dev/null +++ b/bionemo-recipes/interpretability/sparse_autoencoders/recipes/evo2/evo2_dashboard_mockup/src/Preview.jsx @@ -0,0 +1,262 @@ +import React, { useState } from 'react' +import App from './App' +import ColoredSequence from './ColoredSequence' +import GeneUMAPView from './GeneUMAPView' + +// Hit http://localhost:5176/#preview to see all three views side by side. +// Tabs switch between the existing dashboard ("Main") and the two new +// components (ColoredSequence, GeneUMAPView) without restructuring the +// existing layout. + +const MOCK_500BP = ( + 'ATGCGCAATCGTAGCTTAGCATCGATCGTAGCTATCGATCGTACGTACGTAGCTAGCTAGCTAGCTAGCAATCGTAGCATCGTAG' + + 'CTAGCATCGTAGCTAGCTACGTACGTAGCTAGCTAGCTAATCGGGGGGGGGGGCATCGCGCGCGCGCGCGCATCGTAGCTAGCTA' + + 'GCATCGTAGCTAGCATGCTAGCATGCTAGCTAGCTAGCATCGATGCTAGCATGCTAGCATGCTAGCATCGTAGCATCGTAGCATC' + + 'GTAGCTAGCTAATCGATCGTAGCTAGCATCGATCGTAGCTAGCAATCGTAGCTAGCTAGCTAGCATCGTAGCTAGCTAGCTAGCT' + + 'AGCTAGCATCGTAGCATGCTAGCATGCTAGCATCGTAGCTAGCATGCTAGCAATCGGGGGCATCGCGCGCGCGCATCGTAGCATC' + + 'GTAGCTAGCTAGCTAGCATCGATCGTAGCATCGTAGCAATCGTAGCATCGATCGAATCGTAGCAATCGTAGCATCGTACGTACGT' + + 'AGCTAGCTAGCTAATCGATCGATCGTAGCATCGTACGTACGTACGT' +).slice(0, 500) + +const FAKE_FEATURE_CATALOG = { + 101: { label: 'TATA-box-like' }, + 207: { label: 'start-codon (ATG)' }, + 314: { label: 'GC-rich exon' }, + 422: { label: 'splice donor' }, + 588: { label: 'polyA signal' }, +} + +const TABS = [ + { id: 'main', label: 'Main dashboard (features + atlas + WebLogos)' }, + { id: 'sequence', label: 'ColoredSequence (mock 500bp)' }, + { id: 'genes', label: 'Gene UMAP (500 genes, precomputed)' }, +] + +const styles = { + container: { + fontFamily: 'system-ui, sans-serif', + color: 'var(--text, #222)', + background: 'var(--bg, #fafafa)', + minHeight: '100vh', + display: 'flex', + flexDirection: 'column', + }, + tabBar: { + display: 'flex', + gap: '4px', + padding: '8px 16px', + background: 'var(--bg-card, #fff)', + borderBottom: '1px solid var(--border, #ddd)', + flexShrink: 0, + }, + tab: (active) => ({ + padding: '6px 14px', + border: '1px solid', + borderColor: active ? 'var(--accent, #76b900)' : 'var(--border, #ddd)', + background: active ? 'var(--bg-card-expanded, #f0f8e8)' : '#fff', + borderRadius: '4px', + cursor: 'pointer', + fontSize: '12px', + fontWeight: active ? 600 : 400, + color: active ? 'var(--accent, #76b900)' : 'var(--text-secondary, #555)', + }), + tabContent: { + flex: 1, + overflow: 'auto', + background: 'var(--bg, #fafafa)', + }, + sequenceWrap: { padding: '24px' }, + genesWrap: { padding: '24px' }, + title: { fontSize: '20px', fontWeight: 600, marginBottom: '4px' }, + subtitle: { fontSize: '12px', color: 'var(--text-secondary, #666)', marginBottom: '16px' }, + toggleRow: { + display: 'flex', + gap: '8px', + marginBottom: '12px', + alignItems: 'center', + fontSize: '12px', + }, + inputRow: { + marginBottom: '12px', + padding: '10px 12px', + background: 'var(--bg-card, #fff)', + border: '1px solid var(--border, #ddd)', + borderRadius: '6px', + }, + seqInput: { + width: '100%', + fontFamily: 'monospace', + fontSize: '12px', + padding: '8px', + border: '1px solid var(--border, #ddd)', + borderRadius: '4px', + resize: 'vertical', + boxSizing: 'border-box', + }, + inputActions: { + display: 'flex', + gap: '8px', + alignItems: 'center', + marginTop: '8px', + fontSize: '11px', + }, + btnPrimary: { + padding: '5px 14px', + border: '1px solid var(--accent, #76b900)', + background: 'var(--accent, #76b900)', + color: '#fff', + borderRadius: '4px', + cursor: 'pointer', + fontSize: '11px', + fontWeight: 600, + }, + btnSecondary: { + padding: '5px 12px', + border: '1px solid var(--border, #ddd)', + background: '#fff', + color: 'var(--text-secondary, #555)', + borderRadius: '4px', + cursor: 'pointer', + fontSize: '11px', + }, + seqMeta: { + marginLeft: 'auto', + color: 'var(--text-secondary, #666)', + fontSize: '11px', + fontFamily: 'monospace', + }, + toggle: (active) => ({ + padding: '4px 10px', + border: `1px solid ${active ? 'var(--accent, #76b900)' : 'var(--border, #ddd)'}`, + background: active ? 'var(--bg-card-expanded, #f0f8e8)' : '#fff', + borderRadius: '4px', + cursor: 'pointer', + fontSize: '11px', + fontWeight: active ? 600 : 400, + color: active ? 'var(--accent, #76b900)' : 'var(--text, #555)', + }), +} + + +export default function Preview() { + const [tab, setTab] = useState('main') + const [mode, setMode] = useState('top') + const [singleFeatureId, setSingleFeatureId] = useState(101) + const [pastedSeq, setPastedSeq] = useState('') // user-entered sequence, raw + const [activeSeq, setActiveSeq] = useState(MOCK_500BP) // sequence currently rendered + + // Strip whitespace + newlines + numbers (typical when pasting from FASTA), + // uppercase, then keep only A/C/G/T/N. Falls back to mock if input is empty. + const cleanSeq = (raw) => { + const cleaned = (raw || '').toUpperCase().replace(/[^ACGTN]/g, '') + return cleaned.length > 0 ? cleaned : MOCK_500BP + } + + return ( +
+
+ {TABS.map((t) => ( + + ))} +
+ +
+ {tab === 'main' && ( + // The full existing dashboard — feature catalog, UMAP atlas, FeatureCard + // expansions with WebLogo PNGs, histograms. Untouched. +
+ +
+ )} + + {tab === 'sequence' && ( +
+
ColoredSequence
+
+ Paste a DNA sequence below — each base background-colored by its top-firing + SAE feature. Mock activations (real backend not wired yet); swap the + analysis prop with a real /analyze response to use live inference. + Defaults to a 500 bp example if input is empty. +
+ +
+