simBootstrap: simBootstrap

In simulatorZ: Simulator for Collections of Independent Genomic Data Sets

Description

the driver function to perform three-step bootstrap resampling

to get independent genomic data sets

Usage

simBootstrap(obj, y.vars, n.samples, parstep, type = "two-steps", 


    balance.variables = NULL, funSimData = simData, funTrueModel = getTrueModel, 


    funSurvTime = simTime)

Arguments

obj	a list of ExpressionSet, matrix or RangedSummarizedExperiment
y.vars	a list of reponse variables, elements can be class Surv, matrix or data.frame
n.samples	number of samples to resample in each set
parstep	step number to fit CoxBoost
type	whether to include resampling set labels
balance.variables	covariate names to balance in the simulated sets
funSimData	function to perform non-parametric bootstrap
funTrueModel	function to construct true models in original sets
funSurvTime	function to perform parametric bootstrap

Value

a list of values including:

obj.list = a list of simulated objects the same type as input

indices.list = a list of indices indicating which sample the simulated sample is in the

original set

setsID = a vector to indicate the original ID of simulated sets, if

type=="original", setsID should be 1,2,3,...

lp.list = a list of true linear predictor of each original data sets

beta.list = a list of true coefficients used for simulating observations

survH.list = list of cumulative survival hazard

censH.list = list of cumulative censoring hazard

grid.list = list of timeline grid corresponding to survH and censH respectivley

Author(s)

Yuqing Zhang, Christoph Bernau, Levi Waldron

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) 

simulatorZ documentation built on Nov. 8, 2020, 5 p.m.