Removal of batch effects using distribution‐matching residual networks

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.

• Publication year

  2016

• Venue

  Bioinform.

• Publication date

  2016-10-13

• Fields of study

  Biology, Medicine, Computer Science, Mathematics

• Identifiers
  DOI 10.1093/bioinformatics/btx196arXiv 1610.04181PMID 28419223PMCID PMC5870543
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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