Single-Cell & Spatial Omics Analysis Workflows

End-to-end analysis workflows for single-cell RNA-seq, single-cell ATAC-seq, and spatial transcriptomics data, implemented in R.

Tools & Technologies

• scRNA-seq: Seurat
• scATAC-seq: Signac
• Spatial transcriptomics: Seurat (Visium)
• Language: R
• Data sources: GEO, 10x Genomics

Overview

This repository contains independent analysis workflows for three single-cell and spatial omics modalities. Each workflow follows best practices for its respective data type, from raw data ingestion through dimensionality reduction, clustering, and visualization.

Workflows

scRNA-seq | Seurat

Analysis of scRNA-seq data from hepatoblastoma samples (3 individuals).

• QC filtering (nFeature, nCount, mitochondrial percentage thresholds)
• Normalization and highly variable gene selection
• Dimensionality reduction (PCA, UMAP)
• Graph-based clustering
• Batch correction and integration across 3 donors
• Marker gene identification and cluster annotation
• Visualization: UMAP embeddings, feature plots, dot plots, violin plots

scATAC-seq | Signac

Analysis of single-cell chromatin accessibility data.

• Fragment file processing and peak quantification
• QC filtering using TSS enrichment score and nucleosome signal
• Dimensionality reduction (LSI, UMAP)
• Graph-based clustering
• Integration with matched scRNA-seq data
• Chromatin accessibility visualization: coverage plots, genomic region tracks

Spatial Transcriptomics | Seurat (Visium)

Analysis of 10x Visium spatial transcriptomics data.

• Spatial data loading and preprocessing
• Normalization and dimensionality reduction
• Spatially variable gene identification
• Visualization of gene expression in tissue context

Setup

install.packages("Seurat")
# For Signac:
install.packages("Signac")
# Required Bioconductor packages:
BiocManager::install(c("GenomicRanges", "EnsDb.Hsapiens.v86", "BSgenome.Hsapiens.UCSC.hg38"))