Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data.

ABSTRACT: Systematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single-cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single-cell expression data. The zero-inflated version of Poisson/negative binomial and log-normal distributions have emerged as the most popular alternatives owing to their ability to accommodate high dropout rates, as commonly observed in single-cell data. Although the majority of the parametric approaches directly model ex

SUBMITTER: Gupta K 

PROVIDER: S-EPMC8015842 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature