deseq_fn: Computes gene-wise p-values from DESeq2 method using...
In tcgsaseq: Time-Course Gene Set Analysis for RNA-Seq Data

Computes gene-wise p-values from DESeq2 method using observation-wise dispersion estimates

1	deseq_fn(y, x, phi, indiv)

`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
`phi`	a numeric design matrix of size `n x K` containing the `K` variables to be tested
`indiv`	a vector of length `n` containing the information for attributing each sample to one of the studied individuals. Coerced to be a `factor`
`ind`

## Not run: 
#rm(list=ls())
set.seed(123)

##generate some fake data
########################
n <- 100
r <- 12
phi <- matrix(rep(1:3), 4, ncol=1, nrow=r)
sigma <- 0.4
b0 <- 1

#under the null:
b1 <- 0
#under the alternative:
b1 <- 0.7
y.tilde <- b0 + b1*phi + rnorm(r, sd = sigma)
y <- floor(exp(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(1:2, ncol=1, nrow=r/2)
indiv=rep(1:4, each=3)

#run test
temp <- deseq_fn(y, x, phi, indiv)

## End(Not run)