Marr {marr} | R Documentation

## Marr

### Description

This function applies an Rcpp-based implementation of a computationally efficient method for assessing reproducibility in high-throughput experiments, called the the Marr procedure. This function also defines the Marr class and constructor.

### Usage

    Marr(
      object,
      pSamplepairs = 0.75,
      pFeatures = 0.75,
      alpha = 0.05,
      featureVars = NULL
    )

### Arguments

`object` | an object which is a `matrix` or `data.frame` with features (e.g. metabolites or genes) on the rows and samples as the columns. Alternatively, a user can provide a `SummarizedExperiment` object and the `assay(object)` will be used as input for the Marr procedure.
`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.
`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.
`alpha` | (Optional) level of significance to control the False Discovery Rate (FDR). Default is 0.05.
`featureVars` | (Optional) Vector of the columns which identify features. If a 'SummarizedExperiment' is used for 'data', row variables will be used.

### Details

marr (Maximum Rank Reproducibility) is a nonparametric approach, which assesses reproducibility in high-dimensional biological replicate experiments. Although it was originally developed for RNASeq data it can be applied across many different high-dimensional biological data including MassSpectrometry based Metabolomics and ChIPSeq. The Marr procedure uses a maximum rank statistic to identify reproducible signals from noise without making any distributional assumptions of reproducible signals. This procedure can be easily applied to a variety of measurement types since it employs a rank scale.

This function computes the distributions of percent reproducible sample pairs (row-wise) per feature and percent reproducible features (column-wise) per sample pair, respectively. Additionally, it also computes the percent of reproducible sample pairs and features based on a threshold value. See the vignette for more details.

### Value

A object of the class `Marr` that contains a numeric vector of the Marr sample pairs in the `MarrSamplepairs` slot, a numeric vector of the Marr features in the `MarrFeatures` slot, a numeric value of the Marr filtered features in the `MarrSamplepairsfiltered` slot, and a numeric value of the Marr filtered sample pairs in the `MarrFeaturesfiltered` slot.

### References

Philtron, D., Lyu, Y., Li, Q. and Ghosh, D., 2018. Maximum Rank Reproducibility: A Nonparametric Approach to Assessing Reproducibility in Replicate Experiments. Journal of the American Statistical Association, 113(523), pp.1028-1039.

### Examples

    data <- matrix(rnorm(2400), nrow=200, ncol=12)
    data_Marr <- Marr(object = data, pSamplepairs=0.75,
                      pFeatures=0.75, alpha=0.05)
    data("msprepCOPD")
    data_Marr_COPD <- Marr(object = msprepCOPD, pSamplepairs=0.75,
                           pFeatures=0.75, alpha=0.05)

[Package marr version 1.2.0 Index]