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)
# 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