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Limma-voom customized for Jam

Source: R/jam_secontrasts.R
voom_jam.Rd

Limma-voom customized for Jam

Usage

voom_jam(
  counts,
  design = NULL,
  lib.size = NULL,
  normalize.method = "none",
  block = NULL,
  correlation = NULL,
  weights = NULL,
  span = 0.5,
  plot = FALSE,
  save.plot = TRUE,
  verbose = FALSE,
  ...
)

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.

design

design matrix with rows corresponding to samples and columns to coefficients to be estimated. Defaults to model.matrix(~group, data=counts$samples) if counts is a DGEList, otherwise defaults to the unit vector meaning that all samples are treated as replicates.

lib.size

numeric vector containing the library sizes for each sample. Defaults to the columnwise count totals if counts is a matrix or to normLibSizes(counts) if counts is a DGEList.

normalize.method

the microarray-style normalization method to be applied to the logCPM values. Choices are as for the method argument of normalizeBetweenArrays when the data is single-channel.

block

vector or factor specifying a blocking variable on the samples. Has length equal to the number of samples (ncol(counts)).

correlation

the intrablock correlation. Normally a single numeric value between -1 and 1, but a vector of genewise correlations is also allowed.

weights

prior 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), or a numeric vector of gene weights with length equal to nrow(counts).

span

width of the smoothing window used for the lowess mean-variance trend. Expressed as a proportion between 0 and 1.

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?

Details

This function is based directly upon limma::voom() with a small adjustment to handle the presence of NA values, which otherwise causes the stats::lowess() output to be clearly incorrect. The correction removes NA values during this step, producing a result as expected.

See also

Other jamses stats: ebayes2dfs(), format_hits(), handle_na_values(), hit_array_to_list(), process_sestats_to_hitim(), run_limma_replicate(), save_sestats(), se_contrast_stats(), sestats_to_df()