vc_score_perm: Computes variance component test statistic and its permuted...
In tcgsaseq: Time-Course Gene Set Analysis for RNA-Seq Data

Description Usage Arguments Value See Also Examples

This function computes an approximation of the Variance Component test for a mixture of χ^{2}s using Davies method from davies

vc_score_perm(
  y,
  x,
  indiv,
  phi,
  w,
  Sigma_xi = diag(ncol(phi)),
  na_rm = FALSE,
  n_perm = 1000,
  progressbar = TRUE,
  parallel_comp = TRUE,
  nb_cores = parallel::detectCores() - 1
)

`y`	a numeric matrix of dim `g x n` containing the raw RNA-seq counts for g genes from `n` samples
`x`	a numeric design matrix of dim `n x p` containing the `p` covariates to be adjusted for
`indiv`	a vector of length `n` containing the information for attributing each sample to one of the studied individuals. Coerced to be a `factor`
`phi`	a numeric design matrix of size `n x K` containing the `K` variables to be tested
`w`	a vector of length `n` containing the weights for the `n` samples.
`Sigma_xi`	a matrix of size `K x K` containing the covariance matrix of the `K` random effects on `phi`
`na_rm`	logical: should missing values (including `NA` and `NaN`) be omitted from the calculations? Default is `FALSE`.
`n_perm`	the number of permutation to perform. Default is `1000`.
`progressbar`	logical indicating whether a progress bar should be displayed when computing permutations (only in interactive mode).
`parallel_comp`	a logical flag indicating whether parallel computation should be enabled. Only Linux and MacOS are supported, this is ignored on Windows. Default is `TRUE`.
`nb_cores`	an integer indicating the number of cores to be used when `parallel_comp` is `TRUE`. Only Linux and MacOS are supported, this is ignored on Windows. Default is `parallel::detectCores() - 1`.

A list with the following elements:

score: an approximation of the observed set score
scores_perm: a vector containing the permuted set scores
gene_scores_unscaled: approximation of the individual gene scores
gene_scores_unscaled_perm: a list of approximation of the permuted individual gene scores

davies

#rm(list=ls())
set.seed(123)

##generate some fake data
########################
n <- 100
r <- 12
t <- matrix(rep(1:3), r/3, ncol=1, nrow=r)
sigma <- 0.4
b0 <- 1

#under the null:
b1 <- 0
#under the alternative:
b1 <- 0.7
y.tilde <- b0 + b1*t + rnorm(r, sd = sigma)
y <- t(matrix(rnorm(n*r, sd = sqrt(sigma*abs(y.tilde))), ncol=n, nrow=r) +
      matrix(rep(y.tilde, n), ncol=n, nrow=r))
x <- matrix(1, ncol=1, nrow=r)

#run test
scoreTest <- vc_score_perm(y, x, phi=t, w=matrix(1, ncol=ncol(y), nrow=nrow(y)),
                    Sigma_xi=matrix(1), indiv=rep(1:(r/3), each=3), parallel_comp = FALSE)
scoreTest$score