Fig. 20

Evaluation of eight batch-effect correction methods using simulated datasets and differential gene expression analysis. a Evaluation workflow: six sets of simulation data with predefined batch effect and differential gene expression profiles were generated using the Splatter package with varied parameters. The eight methods that return corrected expression matrices were applied to the simulated data, and the batch-corrected output were subsequently subjected to differential gene expression analysis with the Seurat package. Differentially expressed genes (DEGs) identified from the batch-corrected matrices were compared to the ground truth DEGs, and accuracy metrics including precision, recall, and F-score were calculated. b Description of the six simulated datasets. Different combinations of parameters were used to cover different scenarios of cell population sizes and drop-out rates. c F-score boxplot for the eight methods using all genes or HVGs