makeExampleDESeqDataSet_multifac: Make a simulated DESeqDataSet for two or more experimental...

In federicomarini/pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach

Make a simulated DESeqDataSet for two or more experimental factors

Description

Constructs a simulated dataset of Negative Binomial data from different conditions. The fold changes between the conditions can be adjusted with the betaSD_condition and the betaSD_tissue arguments.

Usage

makeExampleDESeqDataSet_multifac(
  n = 1000,
  m = 12,
  betaSD_condition = 1,
  betaSD_tissue = 3,
  interceptMean = 4,
  interceptSD = 2,
  dispMeanRel = function(x) 4/x + 0.1,
  sizeFactors = rep(1, m)
)

Arguments

n	number of rows (genes)
m	number of columns (samples)
betaSD_condition	the standard deviation for condition betas, i.e. beta ~ N(0,betaSD)
betaSD_tissue	the standard deviation for tissue betas, i.e. beta ~ N(0,betaSD)
interceptMean	the mean of the intercept betas (log2 scale)
interceptSD	the standard deviation of the intercept betas (log2 scale)
dispMeanRel	a function specifying the relationship of the dispersions on 2^trueIntercept
sizeFactors	multiplicative factors for each sample

Details

This function is designed and inspired following the proposal of makeExampleDESeqDataSet() from the DESeq2 package. Credits are given to Mike Love for the nice initial implementation

Value

a DESeqDataSet() with true dispersion, intercept for two factors (condition and tissue) and beta values in the metadata columns. Note that the true betas are provided on the log2 scale.

Examples

dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3, betaSD_tissue = 1)
dds
dds2 <- makeExampleDESeqDataSet_multifac(betaSD_condition = 1, betaSD_tissue = 4)
dds2

federicomarini/pcaExplorer documentation built on Sept. 29, 2024, 9:31 p.m.