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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
Published on: December 15, 2023
Removal of batch effects using distribution-matching residual networks
Uri Shaham1, Kelly P Stanton2,3, Jun Zhao3
1Department of Statistics, Yale University, New Haven, CT 06511, USA.
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.
