scShapes

scShapes identifies differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs).

Key Features:

• Distribution modeling: Models each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth.
• Kolmogorov-Smirnov pre-filtering: Performs a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions before GLM modeling.
• Model selection: Selects optimal models by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic.

Scientific Applications:

• Differential distribution analysis: Identifies genes with differential distributions in scRNA-seq data between treatment conditions.

Methodology:

Gene expression read counts are modeled using generalized linear models with Poisson, Negative Binomial, Zero-inflated Poisson, and Zero-inflated Negative Binomial error distributions; a Kolmogorov-Smirnov test pre-selects genes belonging to the ZINB distribution family, followed by model selection via Bayesian Information Criterion and likelihood ratio test.