zmatrix {simulatorZ} | R Documentation |
generate a matrix of c statistics
zmatrix(obj, y.vars, fold, trainingFun = masomenos, cvFun = funCV, cvSubsetFun = cvSubsets)
obj |
a list of ExpressionSet, matrix or RangedSummarizedExperiment objects. If its elements are matrices, columns represent samples |
y.vars |
a list of response variables, all the response variables shold be matrix, data.frame(with 2 columns) or Surv object |
fold |
cvFun parameter, in this case passes to funCV() |
trainingFun |
training function |
cvFun |
function to perform cross study within one set |
cvSubsetFun |
function to divide the expression sets into subsets for cross validation |
outputs one matrix of validation statistics
Yuqing Zhang, Christoph Bernau, Levi Waldron
library(curatedOvarianData) library(GenomicRanges) data(GSE17260_eset) data(E.MTAB.386_eset) data(GSE14764_eset) esets <- list(GSE17260=GSE17260_eset, E.MTAB.386=E.MTAB.386_eset, GSE14764=GSE14764_eset) esets.list <- lapply(esets, function(eset){ return(eset[1:500, 1:20]) }) ## simulate on multiple ExpressionSets set.seed(8) y.list <- lapply(esets.list, function(eset){ time <- eset$days_to_death cens.chr <- eset$vital_status cens <- c() for(i in seq_along(cens.chr)){ if(cens.chr[i] == "living") cens[i] <- 1 else cens[i] <- 0 } y <- Surv(time, cens) return(y) }) # generate on original ExpressionSets z <- zmatrix(esets.list, y.list, 3) # generate on simulated ExpressionSets simmodels <- simBootstrap(esets.list, y.list, 10, 100) z <- zmatrix(simmodels$obj.list, simmodels$y.vars.list, 3) # support matrix X.list <- lapply(esets.list, function(eset){ newx <- exprs(eset) ### columns represent samples !! return(newx) }) z <- zmatrix(X.list, y.list, 3) # support RangedSummarizedExperiment nrows <- 200; ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)), IRanges(floor(runif(200, 1e5, 1e6)), width=100), strand=sample(c("+", "-"), 200, TRUE)) colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) sset <- SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=colData) time <- sample(4500:4700, 6, replace=TRUE) cens <- sample(0:1, 6, replace=TRUE) y.vars <- Surv(time, cens) z <- zmatrix(list(sset[,1:3], sset[,4:6]), list(y.vars[1:3,],y.vars[4:6,]), 3)