Benchmarking Test Statistics and False Discovery Rate Procedures for Single-Cell Differential Expression

Source abstract

Single-cell differential expression analysis combines a gene-level statistic with a false discovery rate (FDR) procedure. We examine how these two choices affect error control and power by pairing four statistics with BH, e-BH, data-splitting mirror, and target–decoy procedures. The benchmark uses zero-inflated negative-binomial simulations and a donor-level model fitted to Kang18 control data, with known signal genes in both designs. We vary dropout, signal type, gene number, and cross-gene dependence. Higher dropout reduces power across all 16 combinations in the mean-shift experiment, whereas null FDR depends on the statistic and its tail calibration. Distribution-sensitive statistics detect equal-mean dispersion changes that mean-based statistics largely miss. The apparent power gain of data splitting and target–decoy at a larger gene count is not reproduced when the number of signals is held fixed. The working e-value is sensitive to null-score calibration, and donor dependence affects some procedures without a common monotone pattern. These results show why benchmark comparisons should separate the signal being tested, the calibration of the input evidence, and the multiple-testing rule.