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Differential detection workflows for multi-sample single-cell RNA-seq data.

ABSTRACT: In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.

SUBMITTER: Gilis J

PROVIDER: S-EPMC10769270 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Differential detection workflows for multi-sample single-cell RNA-seq data.

Gilis Jeroen J Perin Laura L Malfait Milan M Van den Berge Koen K Takele Assefa Alemu A Verbist Bie B Risso Davide D Clement Lieven L

bioRxiv : the preprint server for biology 20231219

In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gen ...[more]

PMID: 38187695

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