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Motivation: 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.
Results: In this study, we consider the batch effect issue in the pattern detection among the samples, such as clustering and subject-wise network analysis. Instead of adjusting the original data matrices, we design an adaptive method to directly adjust the distance 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 improved the performance of standard clustering algorithms.
Availability: The R package is available at: https://github.com/tengfei-emory/QuantNorm
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