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voomWithQualityWeights

Combining observational-level with sample-specific quality weights for RNA-seq analysis


Description

Combine voom observational-level weights with sample-specific quality weights in a designed experiment.

Usage

voomWithQualityWeights(counts, design = NULL, lib.size = NULL, normalize.method = "none",
             plot = FALSE, span = 0.5, var.design = NULL, var.group = NULL,
             method = "genebygene", maxiter = 50, tol = 1e-5, trace = FALSE, col = NULL, ...)

Arguments

counts

a numeric matrix containing raw counts, or an ExpressionSet containing raw counts, or a DGEList object.

design

design matrix with rows corresponding to samples and columns to coefficients to be estimated. Defaults to the unit vector meaning that samples are treated as replicates.

lib.size

numeric vector containing total library sizes for each sample. If NULL and counts is a DGEList then, the normalized library sizes are taken from counts. Otherwise library sizes are calculated from the columnwise counts totals.

normalize.method

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

plot

logical, should a plot of the mean-variance trend and sample-specific weights be displayed?

span

width of the lowess smoothing window as a proportion.

var.design

design matrix for the variance model. Defaults to the sample-specific model whereby each sample has a distinct quality weight.

var.group

vector or factor indicating groups to have different quality weights. This is another way to specify var.design for groupwise variance models.

method

character string specifying the method used to estimate the quality weights. Choices are "genebygene" or "reml".

maxiter

maximum number of iterations allowed for quality weight estimation when method = "reml".

tol

convergence tolerance for quality weight estimation when method = "reml".

trace

logical. If TRUE then diagnostic information is output at each iteration of the "reml" algorithm, or at every 1000th iteration of the "genebygene" algorithm.

col

colours to use in the barplot of sample-specific weights if plot=TRUE). If NULL, then bars are plotted in grey.

...

other arguments are passed to voom and hence to lmFit.

Details

This function is an alternative to voom and, like voom, is intended to process RNA-seq data prior to linear modeling in limma. It combines observational-level weights from voom with sample-specific weights estimated using the arrayWeights function. The method is described by Liu et al (2015).

Value

An EList object similar to that from voom, with an extra column sample.weights containing the vector of sample quality factors added to the targets data.frame. The weights component combines the sample weights and the usual voom precision weights.

Author(s)

Matthew Ritchie, Cynthia Liu, Gordon Smyth

References

Liu, R., Holik, A. Z., Su, S., Jansz, N., Chen, K., Leong, H. S., Blewitt, M. E., Asselin-Labat, M.-L., Smyth, G. K., Ritchie, M. E. (2015). Why weight? Combining voom with estimates of sample quality improves power in RNA-seq analyses. Nucleic Acids Research 43, e97. http://nar.oxfordjournals.org/content/43/15/e97

See Also

See also voomLmFit in the edgeR package.

A summary of limma functions for RNA-seq analysis is given in 11.RNAseq.


limma

Linear Models for Microarray Data

v3.46.0
GPL (>=2)
Authors
Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb]
Initial release
2020-10-19

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