Extended Data Fig. 4: Controlling for batch effects in differential abundance analysis.
From: Differential abundance testing on single-cell data using k-nearest neighbor graphs
(a) In silico batch correction enhances the performance of DA methods in the presence of batch effects: comparison of performance of DA methods with no batch effect, with batch effects of increasing magnitude corrected with MNN, and uncorrected batch effects. Each boxplot summarises results from simulations on n=9 populations. (b) True Positive Rate (TPR, left) and False Discovery Rate (FDR, right) for recovery of cells in simulated DA regions for DA populations with increasing batch effect magnitude on the mouse gastrulation dataset. For each boxplot, results from 8 populations and 3 condition simulations per population are shown (n=24 simulations). Each panel represents a different DA method and a different simulated log-Fold Change. (c) Comparison of Milo performance with (~ batch + condition) or without (~ condition) accounting for the simulated batch in the NB-GLM. For each boxplot, results from 8 populations, simulated fold change > 1.5 and 3 condition simulations per population and fold change are shown (72 simulations per boxplot). In all panels, boxplots show the median with interquartile ranges (25–75%); whiskers extend to the largest value no further than 1.5x the interquartile range from the distance from the box, with outlier data points shown beyond this range.
