pcot2 {pcot2} | R Documentation |
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.
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 principle 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
S.Song and M.Black (2006) PCOT2: an R package for assessing expression changes in groups of related genes. Submitted to Bioinformatics.
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