Mitigating the adverse impact of batch effects in sample pattern detection

It is well known that batch effects exist in RNA-seq data and other profiling data. Although some methods do a good job adjusting for batch effects by modifying the data matrices, it is still difficult to remove the batch effects entirely. The remaining batch effect can cause artifacts in the detection of patterns in the data.

In this study, Emory University researchers consider the batch effect issue in the pattern detection among the samples, such as clustering, dimension reduction, and construction of networks between subjects. Instead of adjusting the original data matrices, they design an adaptive method to directly adjust the dissimilarity matrix between samples. In simulation studies, the method achieved better results recovering true underlying clusters, compared to the leading batch effect adjustment method ComBat. In real data analysis, the method effectively corrected distance matrices, and improved the performance of clustering algorithms.


Flow charts for the two approaches of dissimilarity matrix correction by the interpolating quantile normalization

Availability: The R package is available at:

Fei T, Zhang T, Shi W, Yu T. (2018) Mitigating the adverse impact of batch effects in sample pattern detection. Bioinformatics [Epub ahead of print]. [abstract]

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