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Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-Seq data

View ORCID ProfileMingxiang Teng, View ORCID ProfileRafael A. Irizarry
doi: https://doi.org/10.1101/090704
Mingxiang Teng
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, United States
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
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Rafael A. Irizarry
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, United States
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
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  • For correspondence: rafa@jimmy.harvard.edu
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Posted January 15, 2017.
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Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-Seq data
Mingxiang Teng, Rafael A. Irizarry
bioRxiv 090704; doi: https://doi.org/10.1101/090704
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Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-Seq data
Mingxiang Teng, Rafael A. Irizarry
bioRxiv 090704; doi: https://doi.org/10.1101/090704

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