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 metho

SUBMITTER: Antonsson SE 

PROVIDER: S-EPMC12315870 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature