variancePartition: voomWithDreamWeights – R documentation

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voomWithDreamWeights

Transform RNA-Seq Data Ready for Linear Mixed Modelling with dream()

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()

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

Quantify and interpret divers of variation in multilevel gene expression experiments

v1.20.0

GPL (>= 2)

Authors

Gabriel E. Hoffman

Initial release

2020-10-26