Confronting false discoveries in single-cell differential expression

By J. Squair, M. Gautier, C. Kathe, Mark A. Anderson, N. James, T. Hutson, Rémi Hudelle, Taha Qaiser, K. Matson, Q. Barraud and 4 more. Nature Communications, 2021

744 citations recorded by the source corpus.

Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulations. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. To exemplify these principles, we exposed true and false discoveries of differentially expressed genes in the injured mouse spinal cord. Differential expression analysis of single-cell transcriptomics allows scientists to dissect cell-type-specific responses to biological perturbations. Here, the authors show that many commonly used methods are biased and can produce false discoveries.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/93bcf14bb3be8ba768c4880dc07c57ca5407cfe9

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In Molecular Biology

Citations in the corpus

Gene regulatory network inference in the era of single-cell multi-omics (2023, background): “Strategies that aggregate similar cells into pseudo-bulk profiles or metacells 146,157,158 have been reported to be beneficial 87 .”