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Confronting false discoveries in single-cell differential expression
[article]
2021
bioRxiv
pre-print
Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulation. 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
doi:10.1101/2021.03.12.435024
fatcat:uu5kny5p6zes3i53uakxyqpjwy