| intern.select {globalSeq} | R Documentation |
Communicates between cursus and omnibus
by selecting the covariates of interest.
intern.select(i, Y, Ystart, Yend, X, Xloc,
window, offset, group,
perm, phi, kind)
i |
index |
Y |
RNA-Seq data:
numeric matrix with |
Ystart |
location (or start location) |
Yend |
location (or end location) |
X |
genomic profile:
numeric matrix with |
Xloc |
location covariates:
numeric vector of length |
window |
maximum distance: non-negative real number |
offset |
numeric vector of length |
group |
confounding variable:
factor of length |
perm |
number of iterations: positive integer |
phi |
dispersion parameters: vector of length |
kind |
computation : number between 0 and 1 |
The function returns a dataframe, with the p-value in the first column, and the test statistic in the second column.
A Rauschenberger, MA Jonker, MA van de Wiel, and RX Menezes (2016). "Testing for association between RNA-Seq and high-dimensional data", BMC Bioinformatics. 17:118. html pdf (open access)
This is an internal function. The user functions
are cursus, omnibus,
and proprius.
# simulate high-dimensional data
n <- 30
q <- 10
p <- 100
set.seed(1)
Y <- matrix(rnbinom(q*n,mu=10,
size=1/0.25),nrow=q,ncol=n)
X <- matrix(rnorm(p*n),nrow=p,ncol=n)
Yloc <- seq(0,1,length.out=q)
Xloc <- seq(0,1,length.out=p)
window <- 1
# hypothesis testing
cursus(Y,Yloc,X,Xloc,window)