revdep/checks.noindex/simulatorZ/old/simulatorZ.Rcheck/simulatorZ-Ex.R
In gbm-developers/gbm: Generalized Boosted Regression Models
pkgname <- "simulatorZ"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('simulatorZ')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("cvSubsets")
### * cvSubsets
flush(stderr()); flush(stdout())
### Name: cvSubsets
### Title: cvSubsets
### Aliases: cvSubsets
### ** Examples
library(curatedOvarianData)
data(E.MTAB.386_eset)
id <- cvSubsets(E.MTAB.386_eset, 3)
subsets <- lapply(1:3, function(i){E.MTAB.386_eset[1:10, id[[i]]]})
sapply(subsets, dim)
rm(subsets)
## Number of observations in the set does not need to be a multiple of
## the fold parameter
id2 <- cvSubsets(E.MTAB.386_eset, 5)
subsets <- lapply(1:5, function(j){E.MTAB.386_eset[1:10, id2[[j]]]})
sapply(subsets, dim)
rm(subsets)
cleanEx()
nameEx("funCV")
### * funCV
flush(stderr()); flush(stdout())
### Name: funCV
### Title: funCV
### Aliases: funCV
### ** Examples
library(curatedOvarianData)
library(GenomicRanges)
set.seed(8)
data( E.MTAB.386_eset )
eset <- E.MTAB.386_eset[1:100, 1:30]
rm(E.MTAB.386_eset)
time <- eset$days_to_death
cens.chr <- eset$vital_status
cens <- rep(0, length(cens.chr))
cens[cens.chr=="living"] <- 1
y <- Surv(time, cens)
y1 <- cbind(time, cens)
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 <- c(1588,1929,1813,1542,1830,1775)
cens <- c(1,0,1,1,1,1)
y.vars <- Surv(time, cens)
funCV(eset, 3, y)
funCV(exprs(eset), 3, y1)
funCV(sset, 3, y.vars)
## any training function will do as long as it takes the gene expression matrix X
## and response variable y(matrix, data.frame or Surv object) as parameters, and
## return the coefficients as its value
cleanEx()
nameEx("geneFilter")
### * geneFilter
flush(stderr()); flush(stdout())
### Name: geneFilter
### Title: geneFilter
### Aliases: geneFilter
### ** Examples
set.seed(8)
library(curatedOvarianData)
library(GenomicRanges)
data(GSE17260_eset)
data(E.MTAB.386_eset)
data(GSE14764_eset)
## to save time, we take a small subset from each dataset
esets.list <- list(GSE17260=GSE17260_eset[1:50, 1:10],
E.MTAB.386=E.MTAB.386_eset[1:50, 1:10],
GSE14764=GSE14764_eset[1:50, 1:10])
rm(E.MTAB.386_eset, GSE14764_eset, GSE17260_eset)
result.set <- geneFilter(esets.list, 0.1)
dim(result.set[[1]])
## as we cannot calculate correlation with one set, this function just
## delivers the same set if esets has length 1
result.oneset <- geneFilter(esets.list[1])
dim(result.oneset[[1]])
## Support matrices
X.list <- lapply(esets.list, function(eset){
return(exprs(eset)) ## Columns represent samples!
})
result.set <- geneFilter(X.list, 0.1)
dim(result.set[[1]])
## 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)
s.list <- list(sset, sset)
result.set <- geneFilter(s.list, 0.9)
## the same set should resemble each other, no genes filtered
dim(assay(result.set[[1]]))
cleanEx()
nameEx("getTrueModel")
### * getTrueModel
flush(stderr()); flush(stdout())
### Name: getTrueModel
### Title: getTrueModel
### Aliases: getTrueModel
### ** Examples
library(curatedOvarianData)
data(GSE14764_eset)
data(E.MTAB.386_eset)
esets.list <- list(GSE14764=GSE14764_eset[1:500, 1:20],
E.MTAB.386=E.MTAB.386_eset[1:500, 1:20])
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))
})
res1 <- getTrueModel(esets.list, y.list, 100)
## Get true model from one set
res2 <- getTrueModel(esets.list[1], y.list[1], 100)
names(res2)
res2$lp
## note that y.list[1] cannot be replaced by y.list[[1]]
cleanEx()
nameEx("masomenos")
### * masomenos
flush(stderr()); flush(stdout())
### Name: masomenos
### Title: masomenos
### Aliases: masomenos
### ** Examples
set.seed(8)
library(curatedOvarianData)
data( E.MTAB.386_eset )
eset <- E.MTAB.386_eset[1:100, 1:30]
rm(E.MTAB.386_eset)
X <- t(exprs(eset))
time <- eset$days_to_death
cens <- sample(0:1, 30, replace=TRUE)
y <- Surv(time, cens)
beta <- masomenos(X=X, y=y)
beta
cleanEx()
nameEx("plusMinus")
### * plusMinus
flush(stderr()); flush(stdout())
### Name: plusMinus
### Title: plusMinus
### Aliases: plusMinus
### ** Examples
set.seed(8)
library(curatedOvarianData)
data( E.MTAB.386_eset )
eset <- E.MTAB.386_eset[1:100, 1:30]
rm(E.MTAB.386_eset)
X <- t(exprs(eset))
time <- eset$days_to_death
cens <- sample(0:1, 30, replace=TRUE)
y <- Surv(time, cens)
beta <- plusMinus(X, y)
beta
cleanEx()
nameEx("rowCoxTests")
### * rowCoxTests
flush(stderr()); flush(stdout())
### Name: rowCoxTests
### Title: rowCoxTests
### Aliases: rowCoxTests
### ** Examples
#test
##regressor-matrix (gene expressions)
X<-matrix(rnorm(1e6),nrow=10000)
#seed
set.seed(123)
#times
time<-rnorm(n=ncol(X),mean=100)
#censoring(1->death)
status<-rbinom(n=ncol(X),size=1, prob=0.8)
##survival object
y<-Surv(time,status)
## Do 10,000 Cox regressions:
system.time(output <- rowCoxTests(X=X,y=y, option="fast"))
cleanEx()
nameEx("simBootstrap")
### * simBootstrap
flush(stderr()); flush(stdout())
### Name: simBootstrap
### Title: simBootstrap
### Aliases: simBootstrap
### ** Examples
library(curatedOvarianData)
library(GenomicRanges)
data(E.MTAB.386_eset)
data(GSE14764_eset)
esets.list <- list(E.MTAB.386=E.MTAB.386_eset[1:200, 1:20], GSE14764=GSE14764_eset[1:200, 1:20])
rm(E.MTAB.386_eset, GSE14764_eset)
## simulate on multiple ExpressionSets
set.seed(8)
y.list <- lapply(esets.list, function(eset){
time <- eset$days_to_death
cens.chr <- eset$vital_status
cens <- rep(0, length(cens.chr))
cens[cens.chr=="living"] <- 1
return(Surv(time, cens))
})
simmodels <- simBootstrap(obj=esets.list, y.vars=y.list, 10, 100)
simmodels$obj.list[[1]]
# balance covariates
simmodels <- simBootstrap(obj=esets.list, y.vars=y.list, 10, 100,
balance.variables="tumorstage")
rm(esets.list, simmodels)
## Support RangedSummarizedExperiment
nrows <- 200; ncols <- 10
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"), 5),
row.names=LETTERS[1:10])
sset <- SummarizedExperiment(assays=SimpleList(counts=counts),
rowRanges=rowRanges, colData=colData)
s.list <- list(sset[,1:5], sset[,6:10])
time <- c(540, 527, 668, 587, 620, 540, 527, 668, 587, 620)
cens <- c(1, 0, 0, 1, 0, 1, 0, 0, 1, 0)
y.vars <- Surv(time, cens)
y.vars <- list(y.vars[1:5,],y.vars[1:5,])
simmodels <- simBootstrap(obj=s.list, y.vars=y.vars, 20, 100)
cleanEx()
nameEx("simData")
### * simData
flush(stderr()); flush(stdout())
### Name: simData
### Title: simData
### Aliases: simData
### ** Examples
library(curatedOvarianData)
library(GenomicRanges)
data(E.MTAB.386_eset)
data(GSE14764_eset)
esets.list <- list(E.MTAB.386=E.MTAB.386_eset[1:100, 1:10], GSE14764=GSE14764_eset[1:100, 1:10])
rm(E.MTAB.386_eset, GSE14764_eset)
## simulate on multiple ExpressionSets
set.seed(8)
# one-step bootstrap: skip resampling set labels
simmodels <- simData(esets.list, 20, type="one-step")
# two-step-non-parametric bootstrap
simmodels <- simData(esets.list, 10, type="two-steps")
## simulate one set
simmodels <- simData(list(esets.list[[1]]), 10, type="two-steps")
## balancing covariates
# single covariate
simmodels <- simData(list(esets.list[[1]]), 5, balance.variables="tumorstage")
# multiple covariates
simmodels <- simData(list(esets.list[[1]]), 5,
balance.variables=c("tumorstage", "age_at_initial_pathologic_diagnosis"))
## Support matrices
X.list <- lapply(esets.list, function(eset){
return(exprs(eset))
})
simmodels <- simData(X.list, 20, type="two-steps")
## 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)
s.list <- list(sset[,1:3], sset[,4:6])
simmodels <- simData(s.list, 20, type="two-steps")
cleanEx()
nameEx("simTime")
### * simTime
flush(stderr()); flush(stdout())
### Name: simTime
### Title: simTime
### Aliases: simTime
### ** Examples
library(curatedOvarianData)
data(E.MTAB.386_eset)
data(GSE14764_eset)
esets.list <- list(E.MTAB.386=E.MTAB.386_eset[1:100, 1:20], GSE14764=GSE14764_eset[1:100, 1:20])
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))
})
# To perform both parametric and non-parametric bootstrap, you can call simBootstrap()
# or, you can divide the steps into:
res <- getTrueModel(esets.list, y.list, 100)
simmodels <- simData(obj=esets.list, y.vars=y.list, n.samples=10)
# Then, use this function
simmodels <- simTime(simmodels=simmodels, original.yvars=y.list, result=res)
# it also supports performing only the parametrc bootstrap step on a list of expressionsets
# but you need to construct the parameter by scratch
res <- getTrueModel(esets.list, y.list, 100)
setsID <- seq_along(esets.list)
indices <- list()
for(i in setsID){
indices[[i]] <- seq_along(sampleNames(esets.list[[i]]))
}
simmodels <- list(obj=esets.list, y.vars=y.list, indices=indices, setsID=setsID)
new.simmodels <- simTime(simmodels=simmodels, original.yvars=y.list, result=res)
cleanEx()
nameEx("zmatrix")
### * zmatrix
flush(stderr()); flush(stdout())
### Name: zmatrix
### Title: zmatrix
### Aliases: zmatrix
### ** Examples
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)
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
gbm-developers/gbm documentation built on Feb. 16, 2024, 6:13 p.m.
R Package Documentation
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pkgname <- "simulatorZ"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('simulatorZ')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("cvSubsets")
### * cvSubsets
flush(stderr()); flush(stdout())
### Name: cvSubsets
### Title: cvSubsets
### Aliases: cvSubsets
### ** Examples
library(curatedOvarianData)
data(E.MTAB.386_eset)
id <- cvSubsets(E.MTAB.386_eset, 3)
subsets <- lapply(1:3, function(i){E.MTAB.386_eset[1:10, id[[i]]]})
sapply(subsets, dim)
rm(subsets)
## Number of observations in the set does not need to be a multiple of
## the fold parameter
id2 <- cvSubsets(E.MTAB.386_eset, 5)
subsets <- lapply(1:5, function(j){E.MTAB.386_eset[1:10, id2[[j]]]})
sapply(subsets, dim)
rm(subsets)
cleanEx()
nameEx("funCV")
### * funCV
flush(stderr()); flush(stdout())
### Name: funCV
### Title: funCV
### Aliases: funCV
### ** Examples
library(curatedOvarianData)
library(GenomicRanges)
set.seed(8)
data( E.MTAB.386_eset )
eset <- E.MTAB.386_eset[1:100, 1:30]
rm(E.MTAB.386_eset)
time <- eset$days_to_death
cens.chr <- eset$vital_status
cens <- rep(0, length(cens.chr))
cens[cens.chr=="living"] <- 1
y <- Surv(time, cens)
y1 <- cbind(time, cens)
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 <- c(1588,1929,1813,1542,1830,1775)
cens <- c(1,0,1,1,1,1)
y.vars <- Surv(time, cens)
funCV(eset, 3, y)
funCV(exprs(eset), 3, y1)
funCV(sset, 3, y.vars)
## any training function will do as long as it takes the gene expression matrix X
## and response variable y(matrix, data.frame or Surv object) as parameters, and
## return the coefficients as its value
cleanEx()
nameEx("geneFilter")
### * geneFilter
flush(stderr()); flush(stdout())
### Name: geneFilter
### Title: geneFilter
### Aliases: geneFilter
### ** Examples
set.seed(8)
library(curatedOvarianData)
library(GenomicRanges)
data(GSE17260_eset)
data(E.MTAB.386_eset)
data(GSE14764_eset)
## to save time, we take a small subset from each dataset
esets.list <- list(GSE17260=GSE17260_eset[1:50, 1:10],
E.MTAB.386=E.MTAB.386_eset[1:50, 1:10],
GSE14764=GSE14764_eset[1:50, 1:10])
rm(E.MTAB.386_eset, GSE14764_eset, GSE17260_eset)
result.set <- geneFilter(esets.list, 0.1)
dim(result.set[[1]])
## as we cannot calculate correlation with one set, this function just
## delivers the same set if esets has length 1
result.oneset <- geneFilter(esets.list[1])
dim(result.oneset[[1]])
## Support matrices
X.list <- lapply(esets.list, function(eset){
return(exprs(eset)) ## Columns represent samples!
})
result.set <- geneFilter(X.list, 0.1)
dim(result.set[[1]])
## 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)
s.list <- list(sset, sset)
result.set <- geneFilter(s.list, 0.9)
## the same set should resemble each other, no genes filtered
dim(assay(result.set[[1]]))
cleanEx()
nameEx("getTrueModel")
### * getTrueModel
flush(stderr()); flush(stdout())
### Name: getTrueModel
### Title: getTrueModel
### Aliases: getTrueModel
### ** Examples
library(curatedOvarianData)
data(GSE14764_eset)
data(E.MTAB.386_eset)
esets.list <- list(GSE14764=GSE14764_eset[1:500, 1:20],
E.MTAB.386=E.MTAB.386_eset[1:500, 1:20])
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))
})
res1 <- getTrueModel(esets.list, y.list, 100)
## Get true model from one set
res2 <- getTrueModel(esets.list[1], y.list[1], 100)
names(res2)
res2$lp
## note that y.list[1] cannot be replaced by y.list[[1]]
cleanEx()
nameEx("masomenos")
### * masomenos
flush(stderr()); flush(stdout())
### Name: masomenos
### Title: masomenos
### Aliases: masomenos
### ** Examples
set.seed(8)
library(curatedOvarianData)
data( E.MTAB.386_eset )
eset <- E.MTAB.386_eset[1:100, 1:30]
rm(E.MTAB.386_eset)
X <- t(exprs(eset))
time <- eset$days_to_death
cens <- sample(0:1, 30, replace=TRUE)
y <- Surv(time, cens)
beta <- masomenos(X=X, y=y)
beta
cleanEx()
nameEx("plusMinus")
### * plusMinus
flush(stderr()); flush(stdout())
### Name: plusMinus
### Title: plusMinus
### Aliases: plusMinus
### ** Examples
set.seed(8)
library(curatedOvarianData)
data( E.MTAB.386_eset )
eset <- E.MTAB.386_eset[1:100, 1:30]
rm(E.MTAB.386_eset)
X <- t(exprs(eset))
time <- eset$days_to_death
cens <- sample(0:1, 30, replace=TRUE)
y <- Surv(time, cens)
beta <- plusMinus(X, y)
beta
cleanEx()
nameEx("rowCoxTests")
### * rowCoxTests
flush(stderr()); flush(stdout())
### Name: rowCoxTests
### Title: rowCoxTests
### Aliases: rowCoxTests
### ** Examples
#test
##regressor-matrix (gene expressions)
X<-matrix(rnorm(1e6),nrow=10000)
#seed
set.seed(123)
#times
time<-rnorm(n=ncol(X),mean=100)
#censoring(1->death)
status<-rbinom(n=ncol(X),size=1, prob=0.8)
##survival object
y<-Surv(time,status)
## Do 10,000 Cox regressions:
system.time(output <- rowCoxTests(X=X,y=y, option="fast"))
cleanEx()
nameEx("simBootstrap")
### * simBootstrap
flush(stderr()); flush(stdout())
### Name: simBootstrap
### Title: simBootstrap
### Aliases: simBootstrap
### ** Examples
library(curatedOvarianData)
library(GenomicRanges)
data(E.MTAB.386_eset)
data(GSE14764_eset)
esets.list <- list(E.MTAB.386=E.MTAB.386_eset[1:200, 1:20], GSE14764=GSE14764_eset[1:200, 1:20])
rm(E.MTAB.386_eset, GSE14764_eset)
## simulate on multiple ExpressionSets
set.seed(8)
y.list <- lapply(esets.list, function(eset){
time <- eset$days_to_death
cens.chr <- eset$vital_status
cens <- rep(0, length(cens.chr))
cens[cens.chr=="living"] <- 1
return(Surv(time, cens))
})
simmodels <- simBootstrap(obj=esets.list, y.vars=y.list, 10, 100)
simmodels$obj.list[[1]]
# balance covariates
simmodels <- simBootstrap(obj=esets.list, y.vars=y.list, 10, 100,
balance.variables="tumorstage")
rm(esets.list, simmodels)
## Support RangedSummarizedExperiment
nrows <- 200; ncols <- 10
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"), 5),
row.names=LETTERS[1:10])
sset <- SummarizedExperiment(assays=SimpleList(counts=counts),
rowRanges=rowRanges, colData=colData)
s.list <- list(sset[,1:5], sset[,6:10])
time <- c(540, 527, 668, 587, 620, 540, 527, 668, 587, 620)
cens <- c(1, 0, 0, 1, 0, 1, 0, 0, 1, 0)
y.vars <- Surv(time, cens)
y.vars <- list(y.vars[1:5,],y.vars[1:5,])
simmodels <- simBootstrap(obj=s.list, y.vars=y.vars, 20, 100)
cleanEx()
nameEx("simData")
### * simData
flush(stderr()); flush(stdout())
### Name: simData
### Title: simData
### Aliases: simData
### ** Examples
library(curatedOvarianData)
library(GenomicRanges)
data(E.MTAB.386_eset)
data(GSE14764_eset)
esets.list <- list(E.MTAB.386=E.MTAB.386_eset[1:100, 1:10], GSE14764=GSE14764_eset[1:100, 1:10])
rm(E.MTAB.386_eset, GSE14764_eset)
## simulate on multiple ExpressionSets
set.seed(8)
# one-step bootstrap: skip resampling set labels
simmodels <- simData(esets.list, 20, type="one-step")
# two-step-non-parametric bootstrap
simmodels <- simData(esets.list, 10, type="two-steps")
## simulate one set
simmodels <- simData(list(esets.list[[1]]), 10, type="two-steps")
## balancing covariates
# single covariate
simmodels <- simData(list(esets.list[[1]]), 5, balance.variables="tumorstage")
# multiple covariates
simmodels <- simData(list(esets.list[[1]]), 5,
balance.variables=c("tumorstage", "age_at_initial_pathologic_diagnosis"))
## Support matrices
X.list <- lapply(esets.list, function(eset){
return(exprs(eset))
})
simmodels <- simData(X.list, 20, type="two-steps")
## 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)
s.list <- list(sset[,1:3], sset[,4:6])
simmodels <- simData(s.list, 20, type="two-steps")
cleanEx()
nameEx("simTime")
### * simTime
flush(stderr()); flush(stdout())
### Name: simTime
### Title: simTime
### Aliases: simTime
### ** Examples
library(curatedOvarianData)
data(E.MTAB.386_eset)
data(GSE14764_eset)
esets.list <- list(E.MTAB.386=E.MTAB.386_eset[1:100, 1:20], GSE14764=GSE14764_eset[1:100, 1:20])
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))
})
# To perform both parametric and non-parametric bootstrap, you can call simBootstrap()
# or, you can divide the steps into:
res <- getTrueModel(esets.list, y.list, 100)
simmodels <- simData(obj=esets.list, y.vars=y.list, n.samples=10)
# Then, use this function
simmodels <- simTime(simmodels=simmodels, original.yvars=y.list, result=res)
# it also supports performing only the parametrc bootstrap step on a list of expressionsets
# but you need to construct the parameter by scratch
res <- getTrueModel(esets.list, y.list, 100)
setsID <- seq_along(esets.list)
indices <- list()
for(i in setsID){
indices[[i]] <- seq_along(sampleNames(esets.list[[i]]))
}
simmodels <- list(obj=esets.list, y.vars=y.list, indices=indices, setsID=setsID)
new.simmodels <- simTime(simmodels=simmodels, original.yvars=y.list, result=res)
cleanEx()
nameEx("zmatrix")
### * zmatrix
flush(stderr()); flush(stdout())
### Name: zmatrix
### Title: zmatrix
### Aliases: zmatrix
### ** Examples
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)
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
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