pcot2: Principal Coordinates and Hotelling's T-Square
In pcot2: Principal Coordinates and Hotelling's T-Square method

Description Usage Arguments Details Value Author(s) See Also Examples

The pcot2 function implements the PCOT2 testing method, which is a two-stage permutation-based approach for testing changes in activity in pre-specified gene sets.

1	pcot2(emat, class = NULL, imat, permu = "ByColumn", iter = 1000, alpha = 0.05, adjP.method = "BY", var.equal = TRUE, ncomp = 2, dist.method = "euclidean")

`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 pre-defined gene sets (e.g., gene pathways). The indicator matrix contains rows representing gene identifiers of genes present in the expression data and columns representing pre-defined 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 p-values.
`adjP.method`	Specifies that p-values 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 T-squared 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 `ncomp=2`.
`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 `amap` package and the help documentation for the `Dist` function.

The raw permutation p-values are adjusted for multiple testing by a call to 'p.adjust'.

`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

...

Sarah Song and Mik Black

corplot,corplot2,aveProbe

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 <- npath-nreal   ## 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:(npath-nreal)) 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