Fig. 3

Impact of imputation methods on differential expression analysis. For each imputation method, we performed three gene-level analyses. a Schematic view of evaluating differentially expressed genes (DEGs) using the overlap between bulk RNA-seq and scRNA-seq. b–d Proportion of overlap between bulk and single-cell DEGs identified using either MAST (x-axis) or Wilcoxon rank-sum test (y-axis). Note that “cl” in the names of datasets means “cell line.” e Schematic view of a null DE analysis. f–h Number of false positive DEGs averaging across all settings identified by MAST (x-axis) or Wilcoxon rank-sum test (y-axis) in null differential analyses. i Heatmap of area under a receiver operating characteristic (ROC) curve values when using the expression level of a marker gene (e.g., CD19) to predict a cell type (e.g., B cell or not) using UMI-based sorted PBMC cell types. For some imputation methods, no imputed values were returned. They are denoted as “ImputationFail”. j, k Using a UMI-based scRNA-seq dataset from cell lines (sc_10x_5cl), a heatmap showing the percentage of the overlap between bulk and single-cell DEGs identified using MAST stratified by genes with high (top 10%) or low (bottom 10%) log-fold changes. The color bar on the last column shows the mean overlap across all comparison for each method. If MAST failed to identify DEGs from the imputed profiles of any method in any dataset, we denoted it as “DifferentialFail.” Please refer to Additional file 1: Figure S5 for the Wilcoxon results