| zmatrix {simulatorZ} | R Documentation |
generate a matrix of c statistics
zmatrix(obj, y.vars, fold, trainingFun = masomenos, cvFun = funCV,
cvSubsetFun = cvSubsets, covar = NULL)
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 |
covar |
other covariates to be added as predictors |
outputs one matrix of validation statistics
Yuqing Zhang, Christoph Bernau, Levi Waldron
library(curatedOvarianData)
library(GenomicRanges)
data(E.MTAB.386_eset)
data(GSE14764_eset)
esets.list <- list(E.MTAB.386=E.MTAB.386_eset[1:100, 1:30], GSE14764=GSE14764_eset[1:100, 1:30])
rm(E.MTAB.386_eset, GSE14764_eset)
## 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 <- rep(0, length(cens.chr))
cens[cens.chr=="living"] <- 1
return(Surv(time, cens))
})
# generate on original ExpressionSets
z <- zmatrix(esets.list, y.list, 3)
# generate on simulated ExpressionSets
simmodels <- simBootstrap(esets.list, y.list, 100, 100)
z <- zmatrix(simmodels$obj.list, simmodels$y.vars.list, 3)
# support matrix
X.list <- lapply(esets.list, function(eset){
return(exprs(eset)) ### columns represent samples !!
})
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)