mbqnGetNRIfeatures: Identify rank invariant (RI) and nearly rank invariant (NRI)...

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

View source: R/mbqnGetNRIfeatures.R

Description

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

Usage

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

Arguments

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.

Details

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.

Value

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.

Author(s)

Ariane Schad

References

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

See Also

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

Examples

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## 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)

MBQN documentation built on Nov. 8, 2020, 8:13 p.m.