Batch correction methods used in single-cell RNA sequencing analyses are often poorly calibrated

Abstract

As the number of experiments that employ single-cell RNA sequencing (scRNA-seq) grows, it opens up the possibility of combining results across experiments or processing cells from the same experiment assayed in separate sequencing runs. The gain in the number of cells that can be compared comes at the cost of batch effects that may be present. Several methods have been proposed to combat this for scRNA-seq data sets. We compare eight widely used methods used for batch correction of scRNA-seq data sets. We present a novel approach to measure the degree to which the methods alter the data in the process of batch correction, both at the fine scale, comparing distances between cells, as well as measuring effects observed across clusters of cells. We demonstrate that many of the published methods are poorly calibrated in the sense that the process of correction creates measurable artifacts in the data. In particular, MNN, SCVI, and LIGER perform poorly in our tests, often altering the data considerably. Batch correction with Combat, ComBat-seq, BBKNN, and Seurat introduces artifacts that could be detected in our setup. However, we find that Harmony is the only method that consistently performs well in all the testing methodology we present. Therefore, Harmony is the only method we recommend using when performing batch correction of scRNA-seq data.