enrichment: Enrich pathways with genes from putative miRNA-target gene...
In anamiR: An integrated analysis package of miRNA and mRNA expression data

Description Usage Arguments Value See Also Examples

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.

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

`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.

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.

Hypergeometric for details.

## 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)