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Nonparametric analysis of contributions to variance in genomics and epigenomics data

View ORCID ProfileDavid M. Moskowitz, View ORCID ProfileWilliam J. Greenleaf
doi: https://doi.org/10.1101/314112
David M. Moskowitz
1Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA
2Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
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William J. Greenleaf
2Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
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  • For correspondence: wjg@stanford.edu
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Abstract

Functional genomics studies, despite increasingly varied assay types and complex experimental designs, are typically analyzed by methods that are unable to identify confounding effects and that incorporate parametric assumptions particular to gene expression data. We present MAVRIC, a nonparametric method to quantify variance explained by experimental covariates and perform differential analysis on arbitrary data types. We demonstrate that MAVRIC can accurately associate covariates with underlying data variance, deliver sensitive and specific identification of genomic loci with differential counts, and provide effective noise reduction of large-scale consortium data sets.

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Posted May 04, 2018.
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Nonparametric analysis of contributions to variance in genomics and epigenomics data
David M. Moskowitz, William J. Greenleaf
bioRxiv 314112; doi: https://doi.org/10.1101/314112
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Nonparametric analysis of contributions to variance in genomics and epigenomics data
David M. Moskowitz, William J. Greenleaf
bioRxiv 314112; doi: https://doi.org/10.1101/314112

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