Differential expression of single-cell RNA-seq data using Tweedie models.

ABSTRACT: The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-s

SUBMITTER: Mallick H 

PROVIDER: S-EPMC9288986 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature