mbqnPlotAll | R Documentation |
Check data matrix for rank invariant (RI) and nearly rank invariant (NRI) features/rows across samples and visualize result for different normalizations.
mbqnPlotAll( x, FUN = NULL, low_thr = 0.5, show_nri_only = FALSE, verbose = TRUE, ... )
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 |
low_thr |
a value between [0 1]. Features with RI
frequency >= |
show_nri_only |
logical indicating to display only the RI/NRI detection graph. |
verbose |
logical indicating to print messages. |
... |
additional plot arguments passed to |
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.
A set of figures that display the detected RI/NRI features and 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. |
Ariane Schad
Brombacher, E., Schad, A., Kreutz, C. (2020). Tail-Robust Quantile Normalization. BioRxiv.
mbqnPlotRI()
and mbqnBoxplot()
for the generation of figures,
and mbqn()
for normalization.
## 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)
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