mbqnPlotAll: Plot RI/NRI feature frequencies and normalized/unnormalized...

In arianeschad/MBQN: Mean/Median-balanced quantile normalization

Plot RI/NRI feature frequencies and normalized/unnormalized features

Description

Check data matrix for rank invariant (RI) and nearly rank invariant (NRI) features/rows across samples and visualize result for different normalizations.

Usage

mbqnPlotAll(
  x,
  FUN = NULL,
  low_thr = 0.5,
  show_nri_only = FALSE,
  verbose = TRUE,
  ...
)

Arguments

x	a data matrix. Rows represent features, e.g. protein abundances; columns represent samples.
FUN	a function like mean, median (default), a user defined function, or a numeric vector of weights with length nrow(x) to balance each feature across samples. Functions can be parsed also as characters. If FUN = NULL, features are not balanced, i.e. normal QN is used.
low_thr	a value between [0 1]. Features with RI frequency >=low_thr are considered as NRI/RI; default 0.5.
show_nri_only	logical indicating to display only the RI/NRI detection graph.
verbose	logical indicating to print messages.
...	additional plot arguments passed to mbqnBoxplot, and mbqnPlotRI.

Details

Rank data and check if lower and upper intensity tails are dominated by few features. Apply quantile normalization without and with mean-balancing and check the standard deviation of normalized features located in the tails.

Value

A set of figures that display the detected RI/NRI features and 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.

Author(s)

Ariane Schad

References

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

See Also

mbqnPlotRI() and mbqnBoxplot() for the generation of figures, and mbqn() for normalization.

Examples

## Check data matrix for RI and NRI features
X <- matrix(c(5,2,3,NA,4,1,4,2,3,4,6,NA,1,3,1),ncol=3)
mbqnPlotAll(X, mean, low_thr = 0.5)

arianeschad/MBQN documentation built on April 1, 2022, 7:08 a.m.