voom_weights | R Documentation |
Implementation of the procedure described in Law et al. for estimating precision weights from RNA-seq data.
voom_weights( y, x, preprocessed = FALSE, doPlot = FALSE, lowess_span = 0.5, R = NULL )
y |
a matrix of size |
x |
a matrix of size |
preprocessed |
a logical flag indicating whether the expression data have
already been preprocessed (e.g. log2 transformed). Default is |
doPlot |
a logical flag indicating whether the mean-variance plot should be drawn.
Default is |
lowess_span |
smoother span for the lowess function, between 0 and 1. This gives
the proportion of points in the plot which influence the smooth at each value.
Larger values give more smoothness. Default is |
R |
library.size (optional, important to provide if |
a vector of length n
containing the computed precision weights
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014). voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 15(2), R29.
lowess
approxfun
voom
#rm(list=ls()) set.seed(123) G <- 10000 n <- 12 p <- 2 y <- sapply(1:n, FUN=function(x){rnbinom(n=G, size=0.07, mu=200)}) x <- sapply(1:p, FUN=function(x){rnorm(n=n, mean=n, sd=1)}) my_w <- voom_weights(y, x, doPlot=TRUE) if (requireNamespace("limma", quietly = TRUE)) { w_voom <- limma::voom(counts=y, design=x, plot=TRUE) #slightly faster - same results all.equal(my_w, w_voom$weights) } ## Not run: microbenchmark::microbenchmark(limma::voom(counts=t(y), design=x, plot=FALSE), voom_weights(x, y, doPlot=FALSE), times=30) ## End(Not run)
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