enrichment {anamiR} | R Documentation

## Enrich pathways with genes from putative miRNA-target gene interactions.

### Description

This function will do function analysis with genes from potential miRNA-target gene interactions in the input data.frame, which is generated by database_support, with total 4 kinds of pathway databases, including mouse and human two species, beseides, this function will permute 5000 times (Default) for each pathway to show an empirical p_value to avoid the bias from hypergeometric p-value, this indicating that it would take a few minutes to do functional analysis.

### Usage

    enrichment(data_support, org = c("hsa", "mmu"), per_time = 5000)

### Arguments

`data_support` | matrix format generated from database_support.
`org` | species of genes and miRNAs, only support "hsa", "mmu"
`per_time` | Times of permutation about each enriched pathways, higher times, more precise empirical p-value user can obtain, meanwhile, this function would cost more time. Default is 5000.

### Value

matrix format. There are 7 columns in it, including database, term, total genes of the term, targets in the term, targets in total genes of the term ( p-value.

### See Also

`phyper` for details.

### Examples

    ## Use the internal dataset
    data("mirna", package = "anamiR", envir = environment())
    data("pheno.mirna", package = "anamiR", envir = environment())
    data("mrna", package = "anamiR", envir = environment())
    data("pheno.mrna", package = "anamiR", envir = environment())

    ## SummarizedExperiment class
    require(SummarizedExperiment)
    mirna_se <- SummarizedExperiment(
     assays = SimpleList(counts=mirna),
     colData = pheno.mirna)

    ## SummarizedExperiment class
    require(SummarizedExperiment)
    mrna_se <- SummarizedExperiment(
     assays = SimpleList(counts=mrna),
     colData = pheno.mrna)

    ## Finding differential miRNA from miRNA expression data with t.test
    mirna_d <- differExp_discrete(
       se = mirna_se,
       class = "ER",
       method = "t.test"
    )

    ## Finding differential mRNA from mRNA expression data with t.test
    mrna_d <- differExp_discrete(
       se = mrna_se,
       class = "ER",
       method = "t.test"
    )

    ## Convert annotation to miRBse 21
    mirna_21 <- miR_converter(data = mirna_d, original_version = 17)

    ## Correlation
    cor <- negative_cor(mrna_data = mrna_d, mirna_data = mirna_21)

    ## Intersect with known databases
    sup <- database_support(cor_data = cor)

    ## Functional analysis
    pat <- enrichment(data_support = sup, org = "hsa", per_time = 100)

[Package anamiR version 1.8.0 Index]