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Linear models enable powerful differential activity analysis in massively parallel reporter assays

Leslie Myint, View ORCID ProfileDimitrios G. Avramopoulos, View ORCID ProfileLoyal A. Goff, View ORCID ProfileKasper D. Hansen
doi: https://doi.org/10.1101/196394
Leslie Myint
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
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Dimitrios G. Avramopoulos
2McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
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  • ORCID record for Dimitrios G. Avramopoulos
Loyal A. Goff
2McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
3Department of Neuroscience, Johns Hopkins School of Medicine
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Kasper D. Hansen
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
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  • For correspondence: khansen@jhsph.edu
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Abstract

Massively parallel reporter assays (MPRAs) have emerged as a popular means for understanding noncoding variation in a variety of conditions. While a large number of experiments have been described in the literature, analysis typically uses ad-hoc methods. There has been little attention to comparing performance of methods across datasets.

We present the mpralm method which we show is calibrated and powerful, by analyzing its performance on multiple MPRA datasets. We show that it outperforms existing statistical methods for analysis of this data type, in the first comprehensive evaluation of statistical methods on several datasets. We investigate theoretical and real-data properties of barcode summarization methods and show an unappreciated impact of summarization method for some datasets. Finally, we use our model to conduct a power analysis for this assay and show substantial improvements in power by performing up to 6 replicates per condition, whereas sequencing depth has smaller impact; we recommend to always use at least 4 replicates. Together, these results inform recommendations for differential analysis, general group comparisons, and power analysis and will help improve design and analysis of MPRA experiments. An R package is available from the Bioconductor project at https://bioconductor.org/packages/mpra.

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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 February 21, 2018.
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Linear models enable powerful differential activity analysis in massively parallel reporter assays
Leslie Myint, Dimitrios G. Avramopoulos, Loyal A. Goff, Kasper D. Hansen
bioRxiv 196394; doi: https://doi.org/10.1101/196394
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Linear models enable powerful differential activity analysis in massively parallel reporter assays
Leslie Myint, Dimitrios G. Avramopoulos, Loyal A. Goff, Kasper D. Hansen
bioRxiv 196394; doi: https://doi.org/10.1101/196394

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