PMD Uncovers Widespread Cell-State Erasure by scRNAseq Batch Correction Methods

Abstract

AbstractSingle cell RNAseq (scRNAseq) batches range from technical replicates to multi-tissue atlases, thus requiring robust batch correction methods that operate effectively across this similarity spectrum. Currently, no metrics allow for full benchmarking across this spectrum, resulting in benchmarks that quantify removal of batch effects without quantifying preservation of real batch differences. Here, we address these gaps with a new statistical metric [Percent Maximum Difference (PMD)] that linearly quantifies batch similarity, and simulations generating cells from mixtures of distinct gene expression programs (cell-lineages/-types/-states). Using 690 real-world and 672 simulated integrations (7.2e6 cells total) we compared 7 batch integration approaches across the spectrum of similarity with batch-confounded gene expression. Count downsampling appeared the most robust, while others left residual batch effects or produced over-merged datasets. We further released open-source PMD and downsampling packages, with the latter capable of downsampling an organism atlas (245,389 cells) in tens of minutes on a standard computer.