BatchQC: interactive software for evaluating sample and batch effects in genomic data
Selby, Heather Marie
Leek, Jeffrey T.
Bravo, Hector Corrada
Johnson, W. Evan
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CitationManimaran, Solaiappan, Heather Marie Selby, Kwame Okrah, Claire Ruberman, Jeffrey T. Leek, John Quackenbush, Benjamin Haibe-Kains, Hector Corrada Bravo, and W. Evan Johnson. 2016. “BatchQC: interactive software for evaluating sample and batch effects in genomic data.” Bioinformatics 32 (24): 3836-3838. doi:10.1093/bioinformatics/btw538. http://dx.doi.org/10.1093/bioinformatics/btw538.
AbstractSequencing 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. Availability and Implementation: BatchQC is available through Bioconductor: http://bioconductor.org/packages/BatchQC and GitHub: https://github.com/mani2012/BatchQC. Contact: firstname.lastname@example.org Supplementary information: Supplementary data are available at Bioinformatics online.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:29739133
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