A test metric for assessing single-cell RNA-seq batch correction

• Published in: Nature methods Vol. 16; no. 1; pp. 43 - 49
• Main Authors: Büttner, Maren, Miao, Zhichao, Wolf, F. Alexander, Teichmann, Sarah A., Theis, Fabian J.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.01.2019; Nature Publishing Group
• Subjects: 631/114/2401; 631/114/794; 631/208/199; Algorithms; Analysis; Bias; Bioinformatics; Biological effects; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Cells (Biology); Cluster Analysis; Data analysis; Data integration; Data processing; Datasets; Experiments; Gene expression; Genomes; Genomics; Hypotheses; Integration; Laboratories; Leukocytes (mononuclear); Life Sciences; Nearest-neighbor; Neighborhoods; Peripheral blood mononuclear cells; Populations; Principal components analysis; Proteomics; Ribonucleic acid; RNA; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods

Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations, but as with all genomics experiments, batch effects can hamper data integration and interpretation. The success of batch-effect correction is often evaluated by visual inspection of low-dimensional embeddin...