summary.minPtest: Summary method for a "minPtest" object

In minPtest: Gene region-level testing procedure for SNP data, using the min P test resampling approach

Description

Summary method for objects of class "minPtest"

Usage

## S3 method for class 'minPtest'
summary(object, level = 0.05, sign.SNP = FALSE, ...)

Arguments

object	an object of class minPtest, i.e. the output of a minPtest call.
level	a numeric threshold that specifies which genes are shown in the summary, i.e. the genes with adjusted permutation-based p-values smaller than or equal to that threshold are printed. Default is 0.05.
sign.SNP	a logical value; if TRUE, print, in addition to the genes selected by a level, the SNPs with adjusted marginal p-values smaller than or equal to the level (same as for genes) located on these genes. Default is FALSE, all SNP located on these genes, selected according to the level, are shown. Default is FALSE.
...	Further arguments for the summary method. Not used.

Details

Prints the genes with adjusted permutation-based p-value smaller than or equal to a level, the corresponding permutation-based p-values, the adjusted permutation-based p-values and the SNPs located on these genes, either all SNPs or SNPs selected by the level, sorted by the adjusted marginal p-values, with marginal p-values and adjusted marginal p-values.

Value

summary.minPtest returns a list. Each item characterizes a gene, selected according to a level, list items are named by means of these genes. Each gene item contains a list of data frames, a data frame for the permutation-based p-values and adjusted permutation-based p-values for this gene and a data frame for the marginal p-values and adjusted marginal p-values for the SNPs located on that gene, either all SNPs or SNPs selected by the level.

Author(s)

Stefanie Hieke [e-mail]

See Also

generateSNPs, minPtest

Examples

## Continuing the example from minPtest and generateSNPs:
# generate a data set consisting of 100 subjects and 200 SNPs on 5 genes.

SNP <- c(6,26,54,135,156,186)
BETA <- c(0.9,0.7,1.5,0.5,0.6,0.8)
SNPtoBETA <- matrix(c(SNP,BETA),ncol=2,nrow=6)
colnames(SNPtoBETA) <- c("SNP.item","SNP.beta")

set.seed(191)
sim1 <- generateSNPs(n=100,gene.no=5,block.no=4,block.size=10,p.same=0.9,
	             p.different=0.75,p.minor=c(0.1,0.4,0.1,0.4),
                     n.sample=80,SNPtoBETA=SNPtoBETA)

# Cochran Armitage Trend Test without covariates and default permutations.
# Example: Run R sequential

### Seed
set.seed(10)
seed1 <- sample(1:1e7,size=1000)
###
minPtest.object <- minPtest(y=sim1$y, x=sim1$x, SNPtoGene=sim1$SNPtoGene,
	                    seed=seed1)
###
summary(minPtest.object)

minPtest documentation built on May 29, 2017, 1:06 p.m.