Dataset Information
Removal of batch effects using distribution-matching residual networks.
Ontology highlight
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 apply our method to mass cytometry and scRNA-seq datasets, and demonstrate that it effectively attenuates batch effects.Availability and implementation
our codes and data are publicly available at https://github.com/ushaham/BatchEffectRemoval.git.Contact
yuval.kluger@yale.edu.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Shaham U
PROVIDER: S-EPMC5870543 | biostudies-literature | 2017 Aug
REPOSITORIES: biostudies-literature
Publications
Removal of batch effects using distribution-matching residual networks.
Bioinformatics (Oxford, England) 20170801 16
<h4>Motivation</h4>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 ca ...[more]