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Alternative splicing quantification in single cells with Leaflet

Project description

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 model representation:

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 3.9 or higher. You can easily install LeafletSC via PyPI using the following command:

pip install LeafletSC

Please also make sure you have regtools installed. 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

Capabilities

With LeafletSC, you can:

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

If you use Leaflet, please cite our paper

@unpublished{Isaev2023-bf,
  title    = "Investigating RNA splicing as a source of cellular diversity using a binomial mixture model",
  author   = "Isaev, Keren and Knowles, David A",
  journal  = "bioRxiv",
  pages    = "2023.10.17.562774",
  month    = oct,
  year     = 2023,
  language = "en"
}

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

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