Investigating RNA splicing as a source of cellular diversity using a binomial mixture model

Keren Isaev, David A. Knowles
Proceedings of the 18th Machine Learning in Computational Biology meeting, PMLR 240:163-175, 2024.

Abstract

Alternative splicing (AS) contributes significantly to RNA and protein variability yet its role in defining cellular diversity is not fully understood. While Smart-seq2 offers enhanced coverage across transcripts compared to 10X single cell RNA-sequencing (scRNA-seq), current computational methods often miss the full complexity of AS. Most approaches for single cell based differential splicing analysis focus on simple AS events such as exon skipping, and rely on predefined cell type labels or low-dimensional gene expression representations. This limits their ability to detect more complex AS events and makes them dependent on prior knowledge of cell classifications. Here, we present Leaflet, a splice junction centric approach inspired by Leafcutter, our tool for quantifying RNA splicing variation with bulk RNA-seq. Leaflet is a probabilistic mixture model designed to infer AS-driven cell states without the need for cell type labels. We detail Leaflet’s generative model, inference methodology, and its efficiency in detecting differentially spliced junctions. By applying Leaflet to the Tabula Muris brain cell dataset, we highlight cell-state specific splicing patterns, offering a deeper insight into cellular diversity beyond that captured by gene expression alone.

Cite this Paper

BibTeX
@InProceedings{pmlr-v240-isaev24a, title = {Investigating RNA splicing as a source of cellular diversity using a binomial mixture model}, author = {Isaev, Keren and Knowles, David A.}, booktitle = {Proceedings of the 18th Machine Learning in Computational Biology meeting}, pages = {163--175}, year = {2024}, editor = {Knowles, David A. and Mostafavi, Sara}, volume = {240}, series = {Proceedings of Machine Learning Research}, month = {30 Nov--01 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v240/isaev24a/isaev24a.pdf}, url = {https://proceedings.mlr.press/v240/isaev24a.html}, abstract = {Alternative splicing (AS) contributes significantly to RNA and protein variability yet its role in defining cellular diversity is not fully understood. While Smart-seq2 offers enhanced coverage across transcripts compared to 10X single cell RNA-sequencing (scRNA-seq), current computational methods often miss the full complexity of AS. Most approaches for single cell based differential splicing analysis focus on simple AS events such as exon skipping, and rely on predefined cell type labels or low-dimensional gene expression representations. This limits their ability to detect more complex AS events and makes them dependent on prior knowledge of cell classifications. Here, we present Leaflet, a splice junction centric approach inspired by Leafcutter, our tool for quantifying RNA splicing variation with bulk RNA-seq. Leaflet is a probabilistic mixture model designed to infer AS-driven cell states without the need for cell type labels. We detail Leaflet’s generative model, inference methodology, and its efficiency in detecting differentially spliced junctions. By applying Leaflet to the Tabula Muris brain cell dataset, we highlight cell-state specific splicing patterns, offering a deeper insight into cellular diversity beyond that captured by gene expression alone.} }
Endnote
%0 Conference Paper %T Investigating RNA splicing as a source of cellular diversity using a binomial mixture model %A Keren Isaev %A David A. Knowles %B Proceedings of the 18th Machine Learning in Computational Biology meeting %C Proceedings of Machine Learning Research %D 2024 %E David A. Knowles %E Sara Mostafavi %F pmlr-v240-isaev24a %I PMLR %P 163--175 %U https://proceedings.mlr.press/v240/isaev24a.html %V 240 %X Alternative splicing (AS) contributes significantly to RNA and protein variability yet its role in defining cellular diversity is not fully understood. While Smart-seq2 offers enhanced coverage across transcripts compared to 10X single cell RNA-sequencing (scRNA-seq), current computational methods often miss the full complexity of AS. Most approaches for single cell based differential splicing analysis focus on simple AS events such as exon skipping, and rely on predefined cell type labels or low-dimensional gene expression representations. This limits their ability to detect more complex AS events and makes them dependent on prior knowledge of cell classifications. Here, we present Leaflet, a splice junction centric approach inspired by Leafcutter, our tool for quantifying RNA splicing variation with bulk RNA-seq. Leaflet is a probabilistic mixture model designed to infer AS-driven cell states without the need for cell type labels. We detail Leaflet’s generative model, inference methodology, and its efficiency in detecting differentially spliced junctions. By applying Leaflet to the Tabula Muris brain cell dataset, we highlight cell-state specific splicing patterns, offering a deeper insight into cellular diversity beyond that captured by gene expression alone.
APA
Isaev, K. & Knowles, D.A.. (2024). Investigating RNA splicing as a source of cellular diversity using a binomial mixture model. Proceedings of the 18th Machine Learning in Computational Biology meeting, in Proceedings of Machine Learning Research 240:163-175 Available from https://proceedings.mlr.press/v240/isaev24a.html.

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