Publication: CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data
CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data
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Lütge, A., Zyprych-Walczak, J., Brykczynska Kunzmann, U., Crowell, H. L., Calini, D., Malhotra, D., Soneson, C., & Robinson, M. D. (2021). CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data. Life Science Alliance, 4(6), e202001004. https://doi.org/10.26508/lsa.202001004
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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 m
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- Lütge, Almut
- Zyprych-Walczak, Joanna
- Brykczynska Kunzmann, Urszula
- Crowell, Helena L
- Calini, Daniela
- Malhotra, Dheeraj
- Soneson, Charlotte
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Lütge, A., Zyprych-Walczak, J., Brykczynska Kunzmann, U., Crowell, H. L., Calini, D., Malhotra, D., Soneson, C., & Robinson, M. D. (2021). CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data. Life Science Alliance, 4(6), e202001004. https://doi.org/10.26508/lsa.202001004
