Removal of batch effects using distribution-matching residual networks

@article{Shaham2017RemovalOB,
  title={Removal of batch effects using distribution-matching residual networks},
  author={Uri Shaham and Kelly P. Stanton and Jijun Zhao and Huamin Li and Khadir Raddassi and Ruth R. Montgomery and Yuval Kluger},
  journal={Bioinformatics},
  year={2017},
  volume={33 16},
  pages={
          2539-2546
        }
}
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… CONTINUE READING

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