Confronting false discoveries in single-cell differential expression

🤖 Plain-English Summary

Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulations. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences.

🔑 Key Findings

• 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.

🔗 Related Resources