voomWithDreamWeights: Transform RNA-Seq Data Ready for Linear Mixed Modelling with...

In variancePartition: Quantify and interpret divers of variation in multilevel gene expression experiments

Description

Transform count data to log2-counts per million (logCPM), estimate the mean-variance relationship and use this to compute appropriate observation-level weights. The data are then ready for linear mixed modelling with dream(). This method is the same as limma::voom(), except that it allows random effects in the formula

Usage

voomWithDreamWeights(
  counts,
  formula,
  data,
  lib.size = NULL,
  normalize.method = "none",
  span = 0.5,
  plot = FALSE,
  save.plot = FALSE,
  quiet = FALSE,
  BPPARAM = bpparam(),
  ...
)

Arguments

counts	a numeric matrix containing raw counts, or an ExpressionSet containing raw counts, or a DGEList object. Counts must be non-negative and NAs are not permitted.
formula	specifies variables for the linear (mixed) model. Must only specify covariates, since the rows of exprObj are automatically used a a response. e.g.: ~ a + b + (1|c) Formulas with only fixed effects also work, and lmFit() followed by contrasts.fit() are run.
data	data.frame with columns corresponding to formula
lib.size	numeric vector containing total library sizes for each sample. Defaults to the normalized (effective) library sizes in counts if counts is a DGEList or to the columnwise count totals if counts is a matrix.
normalize.method	the microarray-style normalization method to be applied to the logCPM values (if any). Choices are as for the method argument of normalizeBetweenArrays when the data is single-channel. Any normalization factors found in counts will still be used even if normalize.method="none".
span	width of the lowess smoothing window as a proportion.
plot	logical, should a plot of the mean-variance trend be displayed?
save.plot	logical, should the coordinates and line of the plot be saved in the output?
quiet	suppress message, default FALSE
BPPARAM	parameters for parallel evaluation
...	other arguments are passed to lmer.

Details

Adapted from vomm() in limma v3.40.2

Value

An EList object just like the result of limma::voom()

See Also

limma::voom()

Examples

# library(variancePartition)
library(edgeR)
library(BiocParallel)

data(varPartDEdata)

# normalize RNA-seq counts
dge = DGEList(counts = countMatrix)
dge = calcNormFactors(dge)

# specify formula with random effect for Individual
form <- ~ Disease + (1|Individual) 

# compute observation weights
vobj = voomWithDreamWeights( dge[1:20,], form, metadata)

# fit dream model 
res = dream( vobj, form, metadata)

# extract results
topTable(res, coef="Disease1")

variancePartition documentation built on Nov. 8, 2020, 5:18 p.m.