ScShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell-cell variability therefore are useful for going beyond changes based on average expression and, instead, identifying genes with homogeneous expression versus those that vary widely from cell to cell.

Results

We present a novel statistical framework, scShapes, for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. Ho

SUBMITTER: Dharmaratne M 

PROVIDER: S-EPMC9871437 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature