PIGE (version 1.1)

ARTP.GE: Gene and pathway p-values using ARTP method

Description

Calculate gene- and pathway-environment interaction p-values using the Adaptive Rank Truncated Product method. This function uses mainly the function ARTP_pathway developped by Kai Yu (R package ARTP).

Usage

ARTP.GE(data.gene.pathway, list.gene.snp, p.snp.permut, p.snp.obs,
  inspect.snp.n = 1, inspect.snp.percent = 0, inspect.gene.n = 10,
  inspect.gene.percent = 0.05, temp.dir = "TEMP/", nperm)

Arguments

data.gene.pathway

Data frame (Gene X Pathways) of 0 and 1 values. The rownames (gene name considered) and the colnames (names of the studied pathways) have to be specified. The value 1 indicates that a gene is included in the corresponding pathway.

list.gene.snp

List containing for each gene the corresponding SNP ids. This list could be generated by data.to.list.gene.snp function.

p.snp.permut

the output matrix from either permutation.snp, bootstrap.snp or a file with the SNP ids and p-values (see details).

p.snp.obs

The output data frame from compute.p.snp.obs or a file with the SNP ids and p-values (see details).

inspect.snp.n

The number of candidate truncation points to inspect the top SNPs in a gene. The default is 1.

inspect.snp.percent

A value x between 0 and 1 such that a truncation point will be defined at every x percent of the top SNPs. The default is 0 so that the truncation points will be 1:inspect.snp.n

inspect.gene.n

The number of candidate truncation points to inspect the top genes in the pathway. The default is 10.

inspect.gene.percent

A value x between 0 and 1 such that a truncation point will be defined at every x percent of the top genes. The default is 0.05.

temp.dir

A folder to keep temporary files that will be created.

nperm

Number of permutation used or number of parametric bootsrap used.

Value

The returned value is a list with names "res.gene.list" and "res.pathway". res.gene.list is a list with length equals to the number of investigated pathways. Each element of the list is a data frame containing the gene name, number of SNPs belonging to the gene that were included in the analysis, and the ARTP p-value for the gene. res.pathway contains the ARTP p-values for all the pathway analysed. The results contained in res.pathway are saved in a file named "ARTP-GEI.RData". The data frame containing all the gene analysed, the number of SNPs belonging to each gene and the ARTP p-value are saved in a file named "ARTP-GENE.RData".

Details

If the p-values are not computed using permutation.snp, bootstrap.snp and compute.p.snp.obs then the format for p.snp.obs and p.snp.permut should be as follows. Both files must be uncompressed, comma seperated files with the first row as the SNP ids in the same order. Row 2 of obs.file has the observed p-values, and starting from row 2 in perm.file are the permuted p-values or the boostrap p-values. A random seed should be set before calling ARTP.GE.R in order to reproduce results. The randomness is due to the ranking of p-values, where ties are broken randomly.

References

Yu K, Li Q, Berger AW, Pfeiffer R, Rosenberg P, Caporaso N, Kraft P, Chatterjee N (2009). Pathway analysis by adaptive combination of P-values. Genet Epidemiol 33:700-709.

Examples

# NOT RUN {
data(data.pathway)
data(list.gene.snp)
# }
# NOT RUN {
data(data.pige)

###First example: compute observed p-value (orignal data) and permuted p-value
res <-data.to.PIGE(data=data.pige,data.pathway=data.pathway,
list.gene.snp=list.gene.snp,choice.pathway=c(1,2))
formul <- formula(y~factor(cov1)+factor(cov2)+factor(cov3)+factor(cov4)
+var_int)
p.snp.obs.ex <-  compute.p.snp.obs(data=data.pige,model=formul,
indice.snp=res$snp.selected,var.inter="var_int",class.inter=NULL) 
p.snp.permut.ex <-  permutation.snp(model=formul,data=data.pige,
indice.snp=res$snp.selected,var.inter="var_int",class.inter=NULL,
nbcpu=3,Npermut=9,file.out="res-permut") 
set.seed(10)
result.1 <- ARTP.GE(data.gene.pathway=data.pathway,
list.gene.snp=list.gene.snp,p.snp.permut=p.snp.permut.ex,
p.snp.obs=p.snp.obs.ex,inspect.snp.n=5,inspect.snp.percent=0.05
,inspect.gene.n=10,inspect.gene.percent=0.05,temp.dir="TEMP/"
,nperm=9)
result.1
   
##Second example: observed and permuted p-values have already been computed
path.data <- paste(system.file("sampleData", package="PIGE"),"/",sep="")
res.permut <- read.table(file=paste(path.data,"res-permut.txt",sep="")
,header=TRUE,sep=" ") 
res.obs   <- read.table(file=paste(path.data,"res-obs.txt",sep="")
,header=TRUE,sep=" ") 
result.2 <- ARTP.GE(data.gene.pathway=data.pathway,
list.gene.snp=list.gene.snp, p.snp.permut=res.permut,
p.snp.obs=res.obs,inspect.snp.n=5,inspect.snp.percent=0.05,
inspect.gene.n=10,inspect.gene.percent=0.05,temp.dir="TEMP/",nperm=90)
result.2

##Third example: Survival data 
##observed and permuted p-values have already been computed

data(data.surv)
data(data.pathway.surv)
data(list.gene.snp.surv)
path.data <- paste(system.file("sampleData", package="PIGE"),"/",sep="")
res.permut <- read.table(file=paste(path.data,"res-permut-surv.txt",sep="")
,header=TRUE,sep=" ") 
res.obs   <- read.table(file=paste(path.data,"res-obs-surv.txt",sep="")
,header=TRUE,sep=" ") 
result.3 <- ARTP.GE(data.gene.pathway=data.pathway.surv,
list.gene.snp=list.gene.snp.surv, p.snp.permut=res.permut,
p.snp.obs=res.obs,inspect.snp.n=5,inspect.snp.percent=0.05,
inspect.gene.n=10,inspect.gene.percent=0.05,temp.dir="TEMP/",nperm=90)
result.3
# }