Removal of batch effects using distribution-matching residual networks.

Journal: Bioinformatics (Oxford, England)
Published Date: Aug 15, 2017

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.

Authors

  • Uri Shaham
    Department of Statistics, Yale University, New Haven, CT 06511, USA.
  • Kelly P Stanton
    Department of Pathology, Yale School of Medicine, New Haven, CT 06510, USA.
  • Jun Zhao
  • Huamin Li
    Applied Mathematics Program, Yale University, New Haven, CT 06511, USA.
  • Khadir Raddassi
    Departments of Neurology and Immunobiology.
  • Ruth Montgomery
    Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, USA.
  • Yuval Kluger
    Department of Pathology, Yale School of Medicine, New Haven, CT 06510, USA.

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