Presto scales Wilcoxon and auROC analyses to millions of observations

Korsunsky, I.; Nathan, A.; Millard, N.; Raychaudhuri, S.

doi:10.1101/653253

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bioRxiv · 10.1101/653253

Presto scales Wilcoxon and auROC analyses to millions of observations

Korsunsky, I.·Nathan, A.·Millard, N.·Raychaudhuri, S.

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Abstract

SummaryThe related Wilcoxon rank sum test and area under the receiver operator curve are ubiquitous in high dimensional biological data analysis. Current implementations do not scale readily to the increasingly large datasets generated by novel high-throughput technologies, such as single cell RNAseq. We introduce a simple and scalable implementation of both analyses, available through the R package Presto. Presto scales to big datasets, with functions optimized for both dense and sparse matrices. On a sparse dataset of 1 million observations, 10 groups, and 1,000 features, Presto performed both rank-sum and auROC analyses in only 17 seconds, compared to 6.4 hours with base R functions. Presto also includes functions to seamlessly integrate with the Seurat single cell analysis pipeline and the Bioconductor SingleCellExperiment class. Presto enables the use of robust classical analyses on big data with a simple interface and optimized implementation.\n\nAvailability and ImplementationPresto is available as an R package at https://github.com/immunogenomics/presto.\n\nContactsoumya@broadinstitute.org\n\nSupplementary InformationVignettes are available with the Presto package.

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Korsunsky, I., Nathan, A., Millard, N., Raychaudhuri, S.. 2019-05-29. Presto scales Wilcoxon and auROC analyses to millions of observations. https://doi.org/10.1101/653253

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