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arXiv · 1610.04181

Removal of Batch Effects using Distribution-Matching Residual Networks

Uri Shaham·Kelly P. Stanton·Jun Zhao·Huamin Li·Khadir Raddassi·Ruth Montgomery·Yuval Kluger
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Abstract

Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument, and random measurement errors. Several novel biological technologies, such as mass cytometry and single-cell RNA-seq, are plagued with systematic errors that may severely affect statistical analysis if the data is not properly calibrated. We propose a novel deep learning approach for removing systematic batch effects. Our method is based on a residual network, trained to minimize the Maximum Mean Discrepancy (MMD) between the multivariate distributions of two replicates, measured in different batches. We apply our method to mass cytometry and single-cell RNA-seq datasets, and demonstrate that it effectively attenuates batch effects.

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Uri Shaham, Kelly P. Stanton, Jun Zhao, Huamin Li, Khadir Raddassi, Ruth Montgomery, Yuval Kluger. 2018-01-08. Removal of Batch Effects using Distribution-Matching Residual Networks. https://doi.org/10.1093/bioinformatics%2Fbtx196

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