intern.chromo(Y, Ystart, Yend, X, Xloc, window, offset, group, perm, nodes, phi, kind)
Y | RNA-Seq data:
numeric matrix with |
---|---|
Ystart | start location of genes:
numeric vector of length |
Yend | end location of genes:
NULL or numeric vector of length |
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 |
nodes | number of cluster nodes for parallel computation |
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.
# 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)#>#> pvalue teststat covs #> 1 1.000 8.5568448 100 #> 2 1.000 -8.2856561 100 #> 3 1.000 -4.0785062 100 #> 4 1.000 -1.4002320 100 #> 5 1.000 -3.7048737 100 #> 6 1.000 -0.1611686 100 #> 7 1.000 -5.5801200 100 #> 8 0.015 21.5790210 100 #> 9 0.008 10.4809197 100 #> 10 1.000 -10.2862190 100