mbqnSimuData: Generate a random/structured data matrix

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

Generate a random/structured data matrix

Description

Generate a random data matrix with or without proteomics, log-transformed feature intensity-like properties.

Usage

mbqnSimuData(model = "rand", nrow = NULL, ncol = NULL, show.fig = FALSE)

Arguments

model	character indicating one of the three different type of models: "rand"(default) a Gaussian random matrix of size nrow x ncol (default 1000 x 10), "omics" a Gaussian random matrix of size 1264 x 18 that mimics intensity profiles and missing values as present in real data, and "omics.dep" is the same as "omics" but with an additional single, differentially expressed RI feature.
nrow	number of rows of data matrix (only for model = "rand").
ncol	number of columns of data matrix (only for model = "rand").
show.fig	logical inidicating whether data properties are plot to figure (only for model = "omics" and model = "omics.dep").

Details

For model "rand", each matrix element is drawn from a standard normal distribution N(0,1). For model "omics", the matrix elements of each row are drawn from a Gaussian distribution N(μ_i,σ_i^2) where the mean and standard deviation itself are drawn Gaussian distributions, i.e. σ_i~N(0,0.0625) and μ_i~N(28,4). About 35\ to the missing value pattern present in real protein LFQ intensities. For model "omics.dep", a single differentially epxressed RI feature is stacked on top of the matrix from model "omics".

Value

matrix of size nrow x ncol.

Author(s)

Ariane Schad

References

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

See Also

example_NApattern() for description of missing value pattern.

Examples

mbqnSimuData(model = "rand")
mbqnSimuData(model = "rand", 2000,6)
set.seed(1234)
mbqnSimuData(model = "omics")
set.seed(1111)
mbqnSimuData(model = "omics.dep")

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