get.permu {ELMER} | R Documentation

## get.permu to generate permutation results for calculation of empirical P values for each enhancer-gene linkage.

### Description

get.permu is a function to use the same statistic model to calculate random enhancer-gene pairs. Based on the permutation value, empirical P value can be calculated for the real enhancer-gene pair (see reference).

### Usage

    get.permu(data,
              geneID,
              methy = NULL,
              unmethy = NULL,
              percentage = 0.2,
              rm.probes = NULL,
              correlation = "negative",
              permu.size = 10000,
              permu.dir = NULL,
              cores = 1)

### Arguments

`data` | A multiAssayExperiment with DNA methylation and Gene Expression data. See `createMAE` function.
`geneID` | A vector lists the genes' ID.
`methy` | Index of M (methylated) group.
`unmethy` | Index of U (unmethylated) group.
`percentage` | A number ranges from 0 to 1 specifying the percentage of samples of group 1 and group 2 groups used to link probes to genes. Default is 0.2.
`rm.probes` | A vector lists the probes name.
`correlation` | Type of correlation to identify. Default is negative: look for hypomethylation and increase target expression.
`permu.size` | A number specify the times of permuation. Default is 10000.
`permu.dir` | A path where the output of permuation will be.
`cores` | A interger which defines number of core to be used in parallel process. Default is 1: don't use parallel process.

### Value

Permutations

### Note

Permutation is the most time consuming step. It is recommended to use multiple cores for this step. Default permutation time is 1000 which may need 12 hrs by 4 cores. However 10,000 permutations is recommended to get high confidence results. But it may cost 2 days.

### Author(s)

Lijing Yao (creator: lijingya@usc.edu) Tiago C Silva (maintainer: tiagochst@usp.br)

### References

Yao, Lijing, et al. "Inferring regulatory element landscapes and transcription factor networks from cancer methylomes." Genome biology 16.1 (2015): 1.

### Examples

    data <- ELMER:::getdata("elmer.data.example")
    permu <-get.permu(data = data,
                      geneID=rownames(getExp(data)),
                      rm.probes=c("cg00329272","cg10097755"),
                      permu.size=5)

[Package ELMER version 2.12.0 Index]