plot_weights: Plotting mean-variance fit for precision weights estimation

In dearseq: Differential Expression Analysis for RNA-seq data through a robust variance component test

Description

Display the variability with respect to the level of expression and the associated smoothed estimation of precision weights accounting for heteroscedasticity.

Usage

plot_weights(x)

Arguments

x

a list (such as outputed by the functions sp_weights or voom_weights) containing the following components: weights: a matrix n x G containing the estimated precision weights plot_utilities: a list containing the following elements: reverse_trans: a function encoding the reverse function used for smoothing the observations before computing the weights method: the weight computation method (either "voom" or "loclin") smth: the vector of the smoothed values computed gene_based: a logical indicating whether the computed weights are based on average at the gene level or on individual observations mu: the transformed observed counts or averages v: the observed variability estimates

Value

a ggplot object

Author(s)

Boris Hejblum

Examples

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)})

if(interactive()){
 w <- sp_weights(y, x, use_phi=FALSE, na.rm = TRUE, gene_based=TRUE)
 plot_weights(w)

 vw <-  voom_weights(y, x)
 plot_weights(vw)
}

dearseq documentation built on Nov. 8, 2020, 5:49 p.m.