vc_score_perm: Computes variance component test statistic and its permuted...

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

Description

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

Usage

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
)

Arguments

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 wether a progressBar 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.

Value

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 approximationq of the permuted individual gene scores

See Also

davies

Examples

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

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