CASREL: Cell-specific alternative splicing regulation inference via explainable learning

CASREL is a machine learning framework that infers RBP–alternative splicing regulatory networks de novo from single-cell RNA sequencing data, free of prior RBP–RNA binding annotations. By leveraging distinctive properties of single-cell splicing profiles and integrating ensemble learning with SHAP-based interpretation, CASREL enables accurate, interpretable, and cell-specific mapping of splicing regulation in physiological contexts.

🚀 Quick Start

The demo/ directory contains an example notebook and a minimal test dataset to help users get started with CASREL. You can open and run CASREL_demo_notebook.ipynb directly in Google Colab — the entire process takes approximately 10–15 minutes. These materials demonstrate the complete workflow using a simplified dataset while serving as templates for analyzing your own data.

Tutorial

Environment Setup

> conda create -n casrel -c conda-forge python=3.10.12 -y
> source activate casrel
> pip install -r requirements.txt

# Optional but not recommended
> conda install -c conda-forge numpy pandas scikit-learn lightgbm easydict -y
> conda install -c pytorch pytorch cpuonly -y

Usage

First enter the directory src as the CWD.

Prepare the single-cell AS matrix

python get_filter/get_filter.py [-h] [--input YOUR_TAB_FILE] [--filter-output YOUR_AS_FILE]

optional arguments:
  -h, --help            show this help message and exit

  --splice_file YOUR_AS_FILE
                        Glob path pattern for tab files (e.g. /path/to/*SJ.out.tab). The SJ.out.tab file can be obtained by aligning the raw sequencing data in the regular STAR pipeline.

  --filter-output YOUR_AS_FILE
                        Filter result output prefix (will generate *_start.csv and *_end.csv).

Data preprocessing

python preprocess.py [-h] [--splice_file YOUR_AS_FILE] [--gene_file YOUR_RBP_EXPRESSION_FILE] [--output_dir YOUR_OUTPUT_DIR]

optional arguments:
  -h, --help            show this help message and exit

  --splice_file YOUR_AS_FILE
                        path to the single-cell AS matrix file ('output files' of the step 'Prepare the single-cell AS matrix'). If the path is xxx, we expect there would be xxx_start.csv and xxx_end.csv to represent the 3’ and 5’ AS data. xxx_start.csv and xxx_end.csv can also be obtained from the BAM files by SCASL.

  --gene_file YOUR_RBP_EXPRESSION_FILE
                        path to the single-cell RBP expression file. Should be a csv file.

  --output_dir YOUR_OUTPUT_DIR
                        output directory path.

Train the models and explain the models

python train.py [-h] [-k K] [-i INPUT] [-b] [-o OUTPUT]

optional arguments:

  -h, --help            show this help message and exit.

  -k K                  column (site) index to train the model with. -1 means all columns. You can either specify a single column like 0, 1, or 2, or a range like 0-2, or a list wrapper by quotes
                        and separated by commas like "8, 10, 12".

  -i INPUT, --input INPUT
                        input directory path (also the 'output directory' of the step 'Data preprocessing').

  -b, --batch           Whether to enable batch mode. If this is enabled, a single model will try to capture and output the predictions for all the sites specified.

  -o OUTPUT, --output OUTPUT
                        output directory path.

It is recommended to run multiple processes at once. You can either invoke multiple processes manually by specifying different k ranges (like -k "0-399", -k "400-799", etc.) or by using slurm and invoking multiple batch jobs.

Screening of AS regulatory factors

python reg_summary.py [-h] [-b BASE_DIR]

optional arguments:
  -h, --help            show this help message and exit.

  -b BASE_DIR, --base BASE_DIR
                        Input root directory (contains k1-k5 subdirectories, the 'output directory' of the step 'Train the models and explain the models'), also the output directory. Defaults to the current directory.

Outputs

Regulator Contribution Summary

The primary output of CASREL is a CSV file named Regulator_contribution_summary.csv, containing the predicted RNA splicing regulatory circuitry. This file will be generated in the user-specified output directory.

File Structure

Column	Description
RBP	RNA binding protein (regulatory factor)
AS_Event	Alternative splicing event (splicing site) regulated by the RBP
Contribution	Predicted regulatory contribution score
Regulation	Regulatory direction (+ for promotion, - for inhibition)

This output file provides a comprehensive overview of the predicted regulatory relationships between RBPs and their target splicing events, including both the strength and direction of regulation.