POSTcoxph: POST Analysis of Cox Proportional Hazard Model

In xueyuancao/POST: Projection onto Orthogonal Space Testing for High Dimensional Data

POST Analysis of Cox Proportional Hazard Model

Description

Peform POST analysis with Cox proportional hazard model

Usage

POSTcoxph (exprSet, geneSet, lamda = 0.95, nboots = 100, 
           model = "Surv(EFSTIME, EFSCENSOR) ~ strata(arm2)", 
           seed = 13, ...) 

Arguments

exprSet	an ExpressionSet class contains minimum of exprs (expression matrix) of genomic data such as gene expression and phenoData (AnnotatedDataFrame of end point data). Please refer to Biobase for details on how to create such an ExpressionSet expression set.
geneSet	a GeneSetCollection class with minimum of setName and geneIDs for each GeneSet. Please refer to GSEABase for how to create such a GeneSetCollection class.
lamda	a predefined fraction of 0 to 1: the fraction of variation retained in the selected orthogonal projections
nboots	number of bootstrap samples, default=100
model	a character string to define model
seed	seed of random number generator
...	control arguments to be used in Cox proportinal hazard model, default control arguments is used if they are not supplied directly

Details

The function performs POST analysis for association of gene set with time to event endpoint in Cox proportional hazard model framework.

Value

a data frame with 5 columns

GeneSet	Gene set id
Nprobe	numbe of probes in the ExpressionSet annotated to the gene set
Nproj	number of projected vectors included in the POST analysis
Stat	statistic of quadratic form
p.value	p value of generalized Chi-square approximation

Author(s)

Xueyuan Cao [e-mail], Stanley Pounds [e-mail]

Examples

 ## load data
  data(sampExprSet)
  data(sampGeneSet)
 test<-POSTcoxph(exprSet=sampExprSet,                         
               geneSet=sampGeneSet,                         
               lamda=0.95,                      
               seed=13,                        
               nboots=100,                      
               model='Surv(time, censor) ~ ') 
 

xueyuancao/POST documentation built on May 6, 2023, 8:51 a.m.