Principal Coordinates and Hotelling's TSquare
Description
The pcot2
function implements the PCOT2 testing method, which is a
twostage permutationbased approach for testing changes in activity in
prespecified gene sets.
Usage
1 
Arguments
emat 
A gene expression matrix with no missing values; Each row represents a gene and each column represents a sample. 
class 
Class labels representing two distinct experimental conditions (e.g., normal and disease). 
imat 
The gene category indicator matrix indicates presence or absence of genes in predefined gene sets (e.g., gene pathways). The indicator matrix contains rows representing gene identifiers of genes present in the expression data and columns representing predefined group names. A value of 1 indicates the presence of a gene and 0 indicates the absence for the gene in a particular group. 
permu 
Specifies whether genes or samples are permuted. By default, permutations are performed by sample ("ByColumn"). 
iter 
The number indicates how many permutations will be performed in the analysis. 
alpha 
alpha determines the significance threshold for the permutation pvalues. 
adjP.method 
Specifies that pvalues be adjusted by one of the following methods: "bonferroni", "holm", "hochberg", "hommel", "BH" (Benjamini and Hochberg), or "BY" (Benjamini and Yekutieli). 
var.equal 
Specifies the use of either a pooled estimate of correlation for the two classes or an unpooled estimate for calculating each Tsquared statistic. By default, the pooled estimate is used. 
ncomp 
The dimensionality to which the data matrix is reduced
via principal coordinates. The default dimensionality is set as

dist.method 
Specifies the method for calculating
distance in the PCO procedure. The available distance methods are
"euclidean", "maximum", "manhattan", "canberra", "binary",
"pearson","correlation" or "spearman". For additional details see the

Details
The raw permutation pvalues are adjusted for multiple testing by a call to 'p.adjust'.
Value
res.all 
A data frame which prints information for all pathways 
res.sig 
A data frame which prints information for significant pathways at a given alpha level 
comparison 
Print the contrast used in the analysis 
...
Author(s)
Sarah Song and Mik Black
See Also
corplot
,corplot2
,aveProbe
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39  ns < 40 ## 40 samples
cla < rep(c("Trt","Ctr"),each=ns/2)
ngene < 10 ## 10 genes per group
npath < 10 ## 10 groups
nreal < 3 ## alter groups ##
nnull < npathnreal ## null groups ##
pname < c(paste("RealP",1:nreal, sep=""), paste("NullP",1:nnull, sep=""))
## Three main inputs in the function ##
## [1] Simulate (gene) expression matrix (emat) ##
rmv < function(mn, covm, nr, nc){
sigma < diag(nr)
sigma[sigma==0] < covm
x1 < rmvnorm(nc/2, mean=mn, sigma=sigma)
x0 < rmvnorm(nc/2, mean=rep(0,nr), sigma=sigma)
mat < t(rbind(x1,x0))
return(mat)
}
covm < 0.9 ##covariance
ct < c(6,8,10) ##mean
library(mvtnorm)
emat < c()
for (i in 1:nreal) emat < rbind(emat, rmv(rep(ct[i],ngene),covm=covm, ngene, ns)) # for alt pathways
for (i in 1:(npathnreal)) emat < rbind(emat, rmv(mn=rep(0,ngene),covm=covm, nr=ngene, nc=ns))
dimnames(emat) < list(paste("Gene", 1:(ngene*npath),sep=""), cla)
## [2] class label ##
cla
## [3] indicator matrix (row: genes and col: pathways)
imat < kronecker(diag(npath),rep(1,ngene))
dimnames(imat) < list(paste("Gene",1:(ngene*npath), sep=""), pname)
results.pcot2 < pcot2(emat, cla, imat)
results.pcot2$res.sig
results.pcot2$res.all
