Removal of batch effects using distribution-matching residual networks.

ABSTRACT:

Motivation

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.

Results

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 app

SUBMITTER: Shaham U 

PROVIDER: S-EPMC5870543 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature