Analyzing single-cell sequencing data across batches is challenging. We find that the Van Elteren test, a stratified version of Wilcoxon rank-sum test, elegantly mitigates the problem. We also modified the common language effect size to supplement this test, further improving its utility. On both simulated and real patient data we show the ability of Van Elteren test to control for false positives and false negatives. The effect size also estimates the differences between cell types more accurately.
CITATION STYLE
Liang, S., Liang, Q., Chen, R., & Chen, K. (2020). Stratified Test Alleviates Batch Effects in Single-Cell Data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12099 LNBI, pp. 167–177). Springer. https://doi.org/10.1007/978-3-030-42266-0_13
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