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Cell type-aware analysis of RNA-seq data.

ABSTRACT: Most tissue samples are composed of different cell types. Differential expression analysis without accounting for cell type composition cannot separate the changes due to cell type composition or cell type-specific expression. We propose a computational framework to address these limitations: Cell Type Aware analysis of RNA-seq (CARseq). CARseq employs a negative binomial distribution that appropriately models the count data from RNA-seq experiments. Simulation studies show that CARseq has substantially higher power than a linear model-based approach and it also provides more accurate estimate of the rankings of differentially expressed genes. We have applied CARseq to compare gene expression of schizophrenia/autism subjects versus controls, and identified the cell types underlying the difference and similarities of these two neuron-developmental diseases. Our results are consistent with the results from differential expression analysis using single cell RNA-seq data.

SUBMITTER: Jin C

PROVIDER: S-EPMC8697413 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Cell type-aware analysis of RNA-seq data.

Jin Chong C Chen Mengjie M Lin Danyu D Sun Wei W

Nature computational science 20210415 4

Most tissue samples are composed of different cell types. Differential expression analysis without accounting for cell type composition cannot separate the changes due to cell type composition or cell type-specific expression. We propose a computational framework to address these limitations: Cell Type Aware analysis of RNA-seq (CARseq). CARseq employs a negative binomial distribution that appropriately models the count data from RNA-seq experiments. Simulation studie ...[more]

PMID: 34957416

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