BatchQC: Interactive software for evaluating sample and batch effects in genomic data

Solaiappan Manimaran, Heather Marie Selby, Kwame Okrah, Claire Ruberman, Jeffrey T. Leek, John Quackenbush, Benjamin Haibe-Kains, Hector Corrada Bravo, W. Evan Johnson

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. There are several existing batch adjustment tools for '-omics' data, but they do not indicate a priori whether adjustment needs to be conducted or how correction should be applied. We present a software pipeline, BatchQC, which addresses these issues using interactive visualizations and statistics that evaluate the impact of batch effects in a genomic dataset. BatchQC can also apply existing adjustment tools and allow users to evaluate their benefits interactively. We used the BatchQC pipeline on both simulated and real data to demonstrate the effectiveness of this software toolkit.

Original languageEnglish (US)
Pages (from-to)3836-3838
Number of pages3
Issue number24
StatePublished - Dec 15 2016

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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