Removal of Batch Effects using Distribution-Matching Residual Networks

TL;DR

This paper introduces a deep learning residual network method that effectively removes systematic batch effects from biological datasets like mass cytometry and single-cell RNA-seq by minimizing distribution discrepancies.

Contribution

A novel residual network approach that uses MMD minimization to correct batch effects in high-dimensional biological data.

Findings

Effectively reduces batch effects in mass cytometry data.

Successfully attenuates batch effects in single-cell RNA-seq datasets.

Outperforms existing batch correction methods.

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.