voomWithQualityWeights: Combining observational-level with sample-specific quality...

Description Usage Arguments Details Value Author(s) References See Also

View source: R/voomWithQualityWeights.R


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


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, ...) 



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


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.


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.


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


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


width of the lowess smoothing window as a proportion.


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


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


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


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


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


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


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.


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).


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.


Matthew Ritchie, Cynthia Liu, Gordon Smyth


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

voom, arrayWeights

See also voomLmFit in the edgeR package.

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

limma documentation built on Nov. 8, 2020, 8:28 p.m.