Nothing
# 'FAIME' is a new algorithm to predict Functional Analysis of Individual Microarray Expression.
# Yu Sun: I've removed most of (in-line) comments for tidying with formatR.
# This R code is composed one main function 'runFAIME', which is a R wrap for FAIME. Programmer: Xinan Yang (xyang2 at
# uchicago.edu)
# The functions depends on Biobase package. Declaration of instants which need to be locally modified by user. ArrayInput #
# mRNA-expression input file name. Geneset2gene # an 2 column matrix mapping between GO to genes FDR.T = 0.05 # Threshold to call a
# gene-set as significant genewprobe # array of microarray probe IDs with annotated gene symbol
# parameters (inputs): sampleExp: A vector of normalized exp value with probeName
# genemembers: A vector of probeName of the gene members of a geneset of interest weightRank: A logical to decide whether weighted
# rank to be used, default is TRUE. if weithRank='mild', the exp( (rank-N)/N) was applied which controls the weights between 0 and
# 1 output: y: A score assigned to each microRNA
FAIME <- function(sampleExp, genemembers, na.last = TRUE, weightRank, logCheck = FALSE) {
# check if it log transformed, if not, log transformed # by this transformation, negative value will be scaled to NA #
if (logCheck) {
if (max(sampleExp) > 20)
sampleExp <- log2(SampleExp)
}
if (any(is.na(names(sampleExp))))
stop("Please input sampleExp with probe IDs")
allGenes <- names(sampleExp)
N <- length(allGenes)
nongenemembers <- allGenes[-which(allGenes %in% genemembers)]
# Expression-Anchored Pathway Profiles of Individual Samples Predicts Survival, Yang X et al. Step 1: Calculation of weighted rank
# of gene expression # Rank from the lowest to highest, thus the leading up-regulated genes get the higher weighted score #
rankedExp <- rank(sampleExp, na.last = na.last)
if (weightRank == "mild") {
rankscore <- rankedExp * exp((rankedExp - N)/N)
} else {
if (weightRank) {
rankscore <- rankedExp * exp(rankedExp/N)
} else {
rankscore <- rankedExp
}
}
# Step 2: Calculate F-score for each individual gene-set per a sample using mRNA expression of their gene members and that of its
# none-members #
ST <- sum(rankscore[genemembers])/length(genemembers)
SN <- sum(rankscore[nongenemembers])/length(nongenemembers)
y <- ST - SN
return(y)
}
# parameters (inputs): dat: An expression matrix, row for a probeName and column for a sample genewprobe: A vector of gene Symbol
# for rows of dat, the names of which is probeName geneset2gene: An one-to-one mapping matrix with two columns, the 1st column is
# geneset ID/name, and the 2nd is its gene members weightRank: A logical to decide whether weighted rank to be used, default is
# TRUE. if weithRank='mild' the exp( (rank-N)/N) was applied which controls the weights between 0 and 1 output: res: A matrix of
# transformed microRNA profiling, the score calculated by FAIME
runFAIME <- function(dat, genewprobe, geneset2gene, na.last = TRUE, weightRank = TRUE) {
if (is(dat, "SummarizedExperiment"))
dat <- assay(dat)
if (is(dat, "ExpressionSet"))
dat <- exprs(dat)
allSym <- genewprobe[rownames(dat)]
seeds <- unique(geneset2gene[, 1])
res <- matrix(nrow = length(seeds), ncol = ncol(dat))
rownames(res) <- seeds
colnames(res) <- colnames(dat)
geneIDs <- rownames(dat)
for (i in seq_len(length(seeds))) {
genemembers <- geneset2gene[which(geneset2gene[, 1] %in% seeds[i]), 2]
# Expression-Anchored Pathway Profiles of Individual Samples Predicts Survival, Yang X et al.
targetP <- unlist(allSym[which(allSym %in% genemembers)])
for (j in seq_len(ncol(dat))) {
oneSampleExp <- dat[, j]
names(oneSampleExp) <- geneIDs
res[i, j] <- FAIME(oneSampleExp, genemembers = names(targetP), na.last = na.last, weightRank = weightRank)
}
}
length(which(is.na(res[, 1])))
return(res)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.