enrichment {anamiR} | R Documentation |
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.
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)