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Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences

View ORCID ProfileAnqi Zhu, Joseph G. Ibrahim, View ORCID ProfileMichael I. Love
doi: https://doi.org/10.1101/303255
Anqi Zhu
1department of Biostatistics, University of North Carolina-Chapel Hill
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Joseph G. Ibrahim
1department of Biostatistics, University of North Carolina-Chapel Hill
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Michael I. Love
1department of Biostatistics, University of North Carolina-Chapel Hill
2Department of Genetics, University of North Carolina-Chapel Hill
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  • For correspondence: michaelisaiahlove@gmail.com
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Abstract

In RNA-seq differential expression analysis, investigators aim to detect genes with changes in expression across conditions, despite technical and biological variability. A common task is to accurately estimate the effect size. When the counts are low or highly variable, the simple effect size estimate has high variance, leading to poor ranking of genes by effect size. Here we propose apeglm, which uses a heavy-tailed Cauchy prior distribution for effect sizes, resulting in lower bias than previous shrinkage estimators, while still reducing variance. apeglm is available at http://bioconductor.org/packages/apeglm, and can be used from within the DESeq2 software.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 17, 2018.
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Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences
Anqi Zhu, Joseph G. Ibrahim, Michael I. Love
bioRxiv 303255; doi: https://doi.org/10.1101/303255
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Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences
Anqi Zhu, Joseph G. Ibrahim, Michael I. Love
bioRxiv 303255; doi: https://doi.org/10.1101/303255

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