Removal of batch effects using distribution-matching residual networks - Shaham, Uri; Stanton, Kelly P; Zhao, Jun; Li, Huamin; Raddassi, Khadir; Montgomery, Ruth; Kluger, Yuval (Bioinformatics (Oxford, England), 2017)
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 (scRNA-seq), are plagued with systematic errors that may severely affect statistical analysis if the data are not properly calibrated. We propose a novel deep learning approach for removing systematic batch effects. Our method is based on a residual neural network, trained to minimize the Maximum Mean Discrepancy between the multivariate distributions of two replicates, measured in different batches. We apply our method to mass cytometry and scRNA-seq datasets, and demonstrate that it effectively attenuates batch effects. our codes and data are publicly available at https://github.com/ushaham/BatchEffectRemoval.git. yuval.kluger@yale.edu. Supplementary data are available at Bioinformatics online.