ArticleNature communications2023
A Bayesian model for unsupervised detection of RNA splicing based subtypes in cancers.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA.The annals of applied statistics · 2025Article
- Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.Briefings in bioinformatics · 2024Article
- Trametinib Sensitivity is Defined by a Myeloid Differentiation Profile in Acute Myeloid Leukemia.Drugs in R&D · 2024Article
- IsoTools: a flexible workflow for long-read transcriptome sequencing analysis.Bioinformatics (Oxford, England) · 2023Article
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7 authors.
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Abstract
Identification of cancer sub-types is a pivotal step for developing personalized treatment. Specifically, sub-typing based on changes in RNA splicing has been motivated by several recent studies. We thus develop CHESSBOARD, an unsupervised algorithm tailored for RNA splicing data that captures "tiles" in the data, defined by a subset of unique splicing changes in a subset of patients. CHESSBOARD allows for a flexible number of tiles, accounts for uncertainty of splicing quantification, and is able to model missing values as additional signals. We first apply CHESSBOARD to synthetic data to assess its domain specific modeling advantages, followed by analysis of several leukemia datasets. We show detected tiles are reproducible in independent studies, investigate their possible regulatory drivers and probe their relation to known AML mutations. Finally, we demonstrate the potential clinical utility of CHESSBOARD by supplementing mutation based diagnostic assays with discovered splicing profiles to improve drug response correlation.
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