marr

`marr`: An R/Bioconductor package for Maximum Rank Reproducibility (marr) for high-dimensional biological data.

marr measures the reproducibility of features per sample pair and sample pairs per feature in high-dimensional biological replicate experiments.

Method

The marr paper published in Journal of American Statistical Association:

Philtron, Daisy, et al. “Maximum Rank Reproducibility: A Nonparametric Approach to Assessing Reproducibility in Replicate Experiments.” Journal of the American Statistical Association 113.523 (2018): 1028-1039. https://doi.org/10.1080/01621459.2017.1397521

The marr reproducibility of metabolomics data published in BMC Bioinformatics (Research):

Ghosh, T., Philtron, D., Zhang, W. et al. "Reproducibility of mass spectrometry based metabolomics data". BMC Bioinformatics 22, 423 (2021). https://doi.org/10.1186/s12859-021-04336-9

Installing marr

The R-package marr can be installed from GitHub using the R package devtools:

Use to install the latest version of marr from GitHub:

if (!require("devtools")) install.packages("devtools")
devtools::install_github("Ghoshlab/marr")

It can also be installed using Bioconductor:

# install BiocManager from CRAN (if not already installed)
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")

# install marr package
BiocManager::install("marr")

After installation, the package can be loaded into R.

library(marr)

Using marr

The main function in the marr package is Marr(). The Marr() function needs one required object and three optional objects: (1) object: a data frame or a matrix or a Summarized Experiment with one assay object with observations (e.g., metabolites or genes) on the rows and samples as the columns (e.g. let’s call it dataSE). (2) pSamplepairs (Optional) a threshold value that lies between 0 and 1, used to assign a feature to be reproducible based on the reproducibility output of the sample pairs per feature. Default is 0.75. (3) pFeatures (Optional) a threshold value that lies between 0 and 1, used to assign a sample pair to be reproducible based on the reproducibility output of the features per sample pair. Default is 0.75. (4) alpha (Optional) level of significance to control the False Discovery Rate (FDR). Default is 0.05.

To run the Marr() function,

MarrOutput <- Marr(object = dataSE, pSamplepairs=0.75,
                   pFeatures=0.75, alpha=0.05)

Individual slots can be extracted using accessor methods:

MarrSamplepairs(MarrOutput) # extract the distribution of percent
#reproducible features (column-wise) per sample pair

MarrFeatures(MarrOutput) # extract the distribution of percent
#reproducible sample pairs (row-wise) per feature

MarrSamplepairsfiltered(MarrOutput) # extract the percent of reproducible
#features based on a threshold value

MarrFeaturesfiltered(MarrOutput) # extract the percent of reproducible
#sample pairs based on a threshold value

The percent reproducible sample pairs per feature can be directly plotted using the MarrPlotFeatures() function.

MarrPlotFeatures(MarrOutput)

The percent reproducible features per sample pair can be directly plotted using the MarrPlotSamplepairs() function.

MarrPlotSamplepairs(MarrOutput)

For more details, see vignettes.

Bug reports

Report bugs as issues on the GitHub repository new issue

Ghoshlab/marr