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Maximum rank reproducibility

Bioconductor version: Release (3.19)

marr (Maximum Rank Reproducibility) is a nonparametric approach that detects reproducible signals using a maximal rank statistic for high-dimensional biological data. In this R package, we implement functions that measures the reproducibility of features per sample pair and sample pairs per feature in high-dimensional biological replicate experiments. The user-friendly plot functions in this package also plot histograms of the reproducibility of features per sample pair and sample pairs per feature. Furthermore, our approach also allows the users to select optimal filtering threshold values for the identification of reproducible features and sample pairs based on output visualization checks (histograms). This package also provides the subset of data filtered by reproducible features and/or sample pairs.

Author: Tusharkanti Ghosh [aut, cre], Max McGrath [aut], Daisy Philtron [aut], Katerina Kechris [aut], Debashis Ghosh [aut, cph]

Maintainer: Tusharkanti Ghosh <tusharkantighosh30 at>

Citation (from within R, enter citation("marr")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

The marr user's guide HTML R Script
Reference Manual PDF


biocViews ChIPSeq, MassSpectrometry, Metabolomics, QualityControl, RNASeq, Software
Version 1.14.0
In Bioconductor since BioC 3.12 (R-4.0) (3.5 years)
License GPL (>= 3)
Depends R (>= 4.0)
Imports Rcpp, SummarizedExperiment, utils, methods, ggplot2, dplyr, magrittr, rlang, S4Vectors
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Suggests knitr, rmarkdown, BiocStyle, testthat, covr
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Source Package marr_1.14.0.tar.gz
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