mbqnGetNRIfeatures: Identify rank invariant (RI) and nearly rank invariant (NRI)...
In MBQN: Mean/Median-balanced quantile normalization

Description Usage Arguments Details Value Author(s) References See Also Examples

Compute the rank frequency of each feature of a matrix and identify NRI/RI features.

1	mbqnGetNRIfeatures(x, low_thr = 0.5, method = NULL, verbose = TRUE)

`x`	a data matrix. Rows represent features, e.g. protein abundances; columns represent samples.
`low_thr`	a value between [0 1]. Features with RI frequency >=`low_thr` are considered as NRI/RI; default 0.5.
`method`	character specifying function for computation of quantile normalization; "limma" (default) for `normalizeQuantiles()` from the limma package or "preprocessCore" for `normalize.quantiles()` from the preprocessCore package.
`verbose`	logical indicating to print messages.

Quantile normalize the data matrix and sort ranks. Determine the maximum frequency of equal rank across all columns for each feature. Features with maximum frequency above the user-defined threhold are declared as nearly rank invariant.

A list with elements:

`p`	a matrix with the rank invariance frequencies `ri.freq` and the sample coverage `sample.coverage` for all detected RI/NRI features
`max_p`	maximum rank invariance frequency in percent
`ip`	index of feature with maximum rank invariance frequency
`nri`	table of the rank invariance frequencies in percent for each NRI/RI feature
`var0_feature`	indices of features with zero sample variance after QN
`low_thr`	threshold used for NRI/RI detection from RI frequency.

Ariane Schad

Brombacher, E., Schad, A., Kreutz, C. (2020). Tail-Robust Quantile Normalization. BioRxiv.

mbqnPlotRI() for visualization of detected NRI/RI features.

## Check data matrix for RI and NRI features
set.seed(1234)
x <- mbqnSimuData("omics.dep")
RI <- mbqnGetNRIfeatures(x, low_thr = 0.5, verbose = FALSE)
mbqnPlotRI(RI)