LeafletSC

LeafletSC is a binomial mixture model designed for the analysis of alternative splicing events in single-cell RNA sequencing data. The model facilitates understanding and quantifying splicing variability at the single-cell level. Below is the graphical abstract of our approach:

Compatibility with sequencing platforms

LeafletSC supports analysis from the following single-cell RNA sequencing platforms:

• Smart-Seq2
• Split-seq
• 10X

Getting Started

LeafletSC is implemented in Python and requires Python version 3.10 (3.11 has not been tested yet). We recommend the following approach:

# create a conda environment with python 3.10 
conda create -n "LeafletSC" python=3.10 ipython
# activate environment 
conda activate LeafletSC
# install latest version of LeafletSC into this environment
pip install LeafletSC

Once the package is installed, you can load it in python as follows:

import LeafletSC 

# or specific submodules 
from LeafletSC.utils import *
from LeafletSC.clustering import *

Requirements

Prior to using LeafletSC, please run regtools on your single-cell BAM files. Here is an example of what this might look like in a Snakefile:

{params.regtools_path} junctions extract -a 6 -m 50 -M 500000 {input.bam_use} -o {output.juncs} -s XS -b {output.barcodes}
# Combine junctions and cell barcodes
paste --delimiters='\t' {output.juncs} {output.barcodes} > {output.juncswbarcodes}

• Once you have your junction files, you can try out the mixture model tutorial under Tutorials
• While optional, we recommend running LeafletSC intron clustering with a gtf file so that junctions can be first mapped to annotated splicing events.

Capabilities

With LeafletSC, you can:

• Infer cell states influenced by alternative splicing and identify differentially spliced regions.
• Conduct differential splicing analysis between specific cell groups if cell identities are known.
• Generate synthetic alternative splicing datasets for robust analysis testing.

How does it work?

The full method can be found in our paper while the graphical model is shown below:

If you use Leaflet, please cite our paper

@inproceedings{Isaev2023-ax,
  author = {Isaev, Keren and Knowles, David A},
  title = {Investigating RNA splicing as a source of cellular diversity using a binomial mixture model},
  booktitle = {Proceedings of Machine Learning Research: MLCB 2023},
  year = {2023},
  url = {https://proceedings.mlr.press/v240/isaev24a.html}
}

Potential errors:

If you have any errors with the package Polars, please ensure you install polars-lts-cpu:

pip install polars-lts-cpu

To-do:

1. Add documentation and some tests for how to run the simulation code
2. Add 10X/split-seq mode in addition to smart-seq2
3. Extend framework to seurat/scanpy anndata objects
4. Add notes on generative model and inference method
5. Clean up dependencies