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

Yang Shi, Ji Hyun Lee, Huining Kang, Hui Jiang

Research output: Contribution to journalArticlepeer-review

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 differentiation, is used for estimating the model parameters. Compared with existing meth-ods, our approach shows improved power for detecting differential expressed genes in single-cell high-throughput gene expression data.

Original languageEnglish (US)
Article number377
JournalGenes
Volume13
Issue number2
DOIs
StatePublished - Feb 2022
Externally publishedYes

Keywords

  • Automatic differentiation
  • Differential expression
  • Single-cell RNA-seq
  • Single-cell RT-qPCR
  • Two-part mixed-model

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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