CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data.

ABSTRACT: A key challenge in single-cell RNA-sequencing (scRNA-seq) data analysis is batch effects that can obscure the biological signal of interest. Although there are various tools and methods to correct for batch effects, their performance can vary. Therefore, it is important to understand how batch effects manifest to adjust for them. Here, we systematically explore batch effects across various scRNA-seq datasets according to magnitude, cell type specificity, and complexity. We developed a cell-specific mixing score (cms) that quantifies mixing of cells from multiple batches. By considering distance distributions, the score is able to detect local batch bias as well as differentiate between unbalanced batches and systematic differences between cells of the same cell type. We compare metrics in

SUBMITTER: Lutge A 

PROVIDER: S-EPMC7994321 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature