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.
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
Paper (in preparation)
Ghosh, Tusharkanti, et al. “Reproducibility of Mass Spectrometry based Metabolomics Data”
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)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.
Report bugs as issues on the GitHub repository new issue