Evaluation of statistical differential analysis methods for identification of senescent cells using single-cell transcriptomics

DESeq2 showed the highest performance in differential gene expression analysis across various conditions.

• Ten differential gene expression methods were assessed using single-cell RNA sequencing data.
• Performance metrics included false discovery rate, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve.
• DESeq2 consistently outperformed other methods, achieving the highest area under the precision-recall curve.
• The evaluation involved both simulated and real datasets with varying sample sizes and levels of sparsity.
• Findings suggest DESeq2 is the recommended method for analyzing differential gene expression in scRNA-seq data.

AI simplified