vc_score_h_perm: Computes variance component test statistic for homogeneous...
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 homogeneous trajectory based on the asymptotic distribution of a mixture of χ^{2}s using Davies method from davies

vc_score_h_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 or normalized 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` longitudinal variables to be tested (typically a vector of time points or functions of time)
`w`	a vector of length `n` containing the weights for the `n` samples, corresponding to the inverse of the diagonal of the estimated covariance matrix of y.
`Sigma_xi`	a matrix of size `K x K` containing the covariance matrix of the `K` random effects corresponding to `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
########################
ng <- 100
nindiv <- 30
nt <- 5
nsample <- nindiv*nt
tim <- matrix(rep(1:nt), nindiv, ncol=1, nrow=nsample)
tim <- cbind(tim, tim^2)
sigma <- 5
b0 <- 10

#under the null:
beta1 <- rnorm(n=ng, 0, sd=0)
#under the (heterogen) alternative:
beta1 <- rnorm(n=ng, 0, sd=0.1)
#under the (homogen) alternative:
beta1 <- rnorm(n=ng, 0.06, sd=0)

y.tilde <- b0 + rnorm(ng, sd = sigma)
y <- t(matrix(rep(y.tilde, nsample), ncol=ng, nrow=nsample, byrow=TRUE) +
      matrix(rep(beta1, each=nsample), ncol=ng, nrow=nsample, byrow=FALSE)*matrix(rep(tim, ng),
                                                            ncol=ng, nrow=nsample, byrow=FALSE) +
      matrix(rnorm(ng*nsample, sd = sigma), ncol=ng, nrow=nsample, byrow=FALSE)
      )
myindiv <- rep(1:nindiv, each=nt)
x <- cbind(1, myindiv/2==floor(myindiv/2))
myw <- matrix(rnorm(nsample*ng, sd=0.1), ncol=nsample, nrow=ng)

#run test
#We only use few permutations (10) to keep example running time low
#Otherwise one can use n_perm = 1000
score_homogen <- vc_score_h_perm(y, x, phi=tim, indiv=myindiv,
                                w=myw, Sigma_xi=cov(tim), n_perm = 10,
                                parallel_comp = FALSE)
score_homogen$score

score_heterogen <- vc_score_perm(y, x, phi=tim, indiv=myindiv,
                           w=myw, Sigma_xi=cov(tim), n_perm = 10,
                           parallel_comp = FALSE)
score_heterogen$score

scoreTest_homogen <- vc_test_asym(y, x, phi=tim, indiv=rep(1:nindiv, each=nt),
                                 w=matrix(1, ncol=ncol(y), nrow=nrow(y)), Sigma_xi=cov(tim),
                                 homogen_traj = TRUE)
scoreTest_homogen$set_pval
scoreTest_heterogen <- vc_test_asym(y, x, phi=tim, indiv=rep(1:nindiv, each=nt),
                                   w=matrix(1, ncol=ncol(y), nrow=nrow(y)), Sigma_xi=cov(tim),
                                   homogen_traj = FALSE)
scoreTest_heterogen$set_pval