NIFty

Never Impute Features (thank you).

The pre-print manuscript associated with this tool can be found here: TBA

NIFty is a python program for data-driven cell annotation (classification). NIFty can be used for top-scoring pairs (TSP)-based rule generation and feature selection, classification model generation, and model application on unlabaled data. NIFty is unique in that it does not require missing-value imputation, avoids common circular analysis pitfalls by default, and overcomes batch effects. The primary application is for classifying large molecular data, like proteomics.

NIFty uses a minimum of two user-provided tables as input:

1. a table with quantification data, proteins (or some other molecular data type) as the columns and samples as the rows; and
2. a table that has the label (class) for each sample.

The output from our program depends on which functionalty the user would like to run. In the 'find_features' mode, the output is a list of the k best TSP-based features/rules that can be used to train a machine learning classifier for sample annotation. In the 'train_model' mode, the output is a machine learning model trained on the selected features. In the 'apply_model' mode, the output is a list of predicted sample labels or label probabilities from applying the trained model on experimental, unlabeled data.

The important thing is that we never impute; we can deal with null values.

After downloading this repository, run NIFty on your own data with the following command on the commandline (assuming config.toml exists in the same directory):

python nifty.py

After downloading this repository, run NIFty on your own data with the following command on the commandline (with a custom config filepath):

python nifty.py -c <config/file/path>

Requirements

NIFty requires Python (>= 3.11) and the following Python packages to be installed:

• cloudpickle
• numpy
• pandas
• scikit-learn
• statsmodels

Codebase Structure

The codebase functions as follows:

Run Modes

NIFty can be executed in several modes depending on which steps of the pipeline you want to run:

1. find_features: Generate and score rules to find the best k features for classification.
2. train_model: Train a machine learning classifier using the selected features.
3. apply_model: Apply the trained classifier on experimental, unlabeled data.

You can control this behavoir using a .toml configuration file.

File Formats and Descriptions

• A description of all necessary input files and their required formats can be found here.
• A description of all output files can be found here.

Use Cases

Each of the use-case documents below contain the following information: (1) a brief description about when to run a particular use case of NIFty; and (2) changes to default configurations needed to run that particular use case (to see a default configuration file, see File Formats and Descriptions).

• Full Pipeline (Feature Selection, Model Training, and Model Application)
• Feature Selection
• Model Training
• Model Application
• Feature Selection and Model Training
• Model Training and Model Application

Citing NIFty

TBA