  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.
