An evaluation of statistical differential analysis methods in single-cell RNA-seq data.

ABSTRACT:

Background

Single-cell RNA Sequencing is gaining popularity in recent years. Compared to bulk RNA-Seq, single-cell RNA Sequencing allows the gene expression being measured within individual cells instead of mean gene expression levels across all cells in the sample. Thus, cell-to-cell variation of gene expressions could be examined. Gene differential expression analysis remains the major purpose in most single-cell RNA sequencing experiments and many methods have been developed in recent years to conduct gene differential expression analysis for single-cell RNA sequencing data.

Results

Through simulation studies and real data examples, we evaluated the performance of five open-source popular methods used for gene differential expression analysis in single-cell RNA sequencing

SUBMITTER: Li D 

PROVIDER: S-EPMC10055642 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature