ComBat-seq: batch effect adjustment for RNA-seq count data

The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch…

• NS
  National Science Foundation

  Award: NSF-DMS 1810829
• NI
  National Institutes of Health

  Awards: 5U01 CA220413-03, 5R01GM127430-02, 4P30CA006516-51