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 are dataset- and batch-specific differences that can obscure the biological signal of interest. While there are various tools and methods to perform data integration and correct for batch effects, their performance can vary between datasets and according to the nature of the bias. Therefore, it is important to understand how batch effects manifest in order to adjust for them in a reliable way. Here, we systematically explore batch effects in scRNA-seq data from a variety of datasets according to magnitude, cell type specificity and complexity. We developed a cell-specific mixing score (\texttt{cms}) that quantifies how well cells from multiple batches are mixed. By considering distance distributions (in a lower dime

INSTRUMENT(S): Illumina HiSeq 3000

ORGANISM(S): Homo sapiens

SUBMITTER: Mark Robinson 

PROVIDER: E-MTAB-9916 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress