Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/mbqnGetNRIfeatures.R
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 >= |
method |
character specifying function for computation of quantile
normalization; "limma" (default) for |
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:
|
a matrix with the rank invariance frequencies |
|
maximum rank invariance frequency in percent |
|
index of feature with maximum rank invariance frequency |
|
table of the rank invariance frequencies in percent for each NRI/RI feature |
|
indices of features with zero sample variance after QN |
|
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.
1 2 3 4 5 | ## 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.