A Two-Part Mixed Model for Differential Expression Analysis in Single-Cell High-Throughput Gene Expression Data.

ABSTRACT: The high-throughput gene expression data generated from recent single-cell RNA sequencing (scRNA-seq) and parallel single-cell reverse transcription quantitative real-time PCR (scRT-qPCR) technologies enable biologists to study the function of transcriptome at the level of individual cells. Compared with bulk RNA-seq and RT-qPCR gene expression data, single-cell data show notable distinct features, including excessive zero expression values, high variability, and clustered design. We propose to model single-cell high-throughput gene expression data using a two-part mixed model, which not only adequately accounts for the aforementioned features of single-cell expression data but also provides the flexibility of adjusting for covariates. An efficient computational algorithm, automatic differ

SUBMITTER: Shi Y 

PROVIDER: S-EPMC8872627 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature