Description Usage Arguments Details Value References Examples

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.

1 | ```
Marr(object, pSamplepairs = 0.75, pFeatures = 0.75, alpha = 0.05)
``` |

`object` |
an object which is a |

`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. |

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.

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.

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.

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