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

In GabrielHoffman/variancePartition: Quantify and interpret drivers of variation in multilevel gene expression experiments

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,
  weights = NULL,
  prior.count = 0.5,
  prior.count.for.weights = prior.count,
  plot = FALSE,
  save.plot = TRUE,
  rescaleWeightsAfter = FALSE,
  scaledByLib = FALSE,
  priorWeightsAsCounts = FALSE,
  BPPARAM = SerialParam(),
  ...
)

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 as 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. Setting span="auto" uses fANCOVA::loess.as() to estimate the tuning parameter from the data
weights	Can be a numeric matrix of individual weights of same dimensions as the counts, or a numeric vector of sample weights with length equal to ncol(counts)
prior.count	average count to be added to each observation to avoid taking log of zero. The count applied to each sample is normalized by library size so given equal log CPM for a gene with zero counts across multiple samples
prior.count.for.weights	count added to regularize weights
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?
rescaleWeightsAfter	default = FALSE, should the output weights be scaled by the input weights
scaledByLib	if TRUE, scale pseudocount by lib.size. Else to standard constant pseudocount addition
priorWeightsAsCounts	if weights is NULL, set weights to be equal to counts, following delta method for log2 CPM
BPPARAM	parameters for parallel evaluation
...	other arguments are passed to lmer.

Details

Adapted from voom() 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)
res <- eBayes(res)

# extract results
topTable(res, coef = "Disease1", number = 3)

GabrielHoffman/variancePartition documentation built on Nov. 8, 2024, 11:17 a.m.