Genome Biology (Jan 2022)

IDEAS: individual level differential expression analysis for single-cell RNA-seq data

  • Mengqi Zhang,
  • Si Liu,
  • Zhen Miao,
  • Fang Han,
  • Raphael Gottardo,
  • Wei Sun

DOI
https://doi.org/10.1186/s13059-022-02605-1
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 17

Abstract

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Abstract We consider an increasingly popular study design where single-cell RNA-seq data are collected from multiple individuals and the question of interest is to find genes that are differentially expressed between two groups of individuals. Towards this end, we propose a statistical method named IDEAS (individual level differential expression analysis for scRNA-seq). For each gene, IDEAS summarizes its expression in each individual by a distribution and then assesses whether these individual-specific distributions are different between two groups of individuals. We apply IDEAS to assess gene expression differences of autism patients versus controls and COVID-19 patients with mild versus severe symptoms.

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