Clustering miRNAs-genes pairs

isoNetwork(mirna_rse, gene_rse, target, org, summarize = "group",
  genename = "ENSEMBL", min_cor = -0.6)

Arguments

mirna_rse

SummarizedExperiment::SummarizedExperiment with miRNA information. See details.

gene_rse

SummarizedExperiment::SummarizedExperiment with gene information. See details.

target

Matrix with miRNAs (columns) and genes (rows) target prediction (1 if it is a target, 0 if not).

org

AnnotationDbi::AnnotationDb obejct. For example:(org.Mm.eg.db).

summarize

Character column name in colData(rse) to use to group samples and compare betweem miRNA/gene expression.

genename

Character keytype of the gene names in gene_rse object.

min_cor

Numeric cutoff to consider a miRNA to regulate a target.

Value

list with network information

Details

This function will correlate miRNA and gene expression data using a specific metadata variable to group samples and detect pattern of expression that will be annotated with GO terms. mirna_rse and gene_rse can be created using the following code: mi_rse = SummarizedExperiment(assays=SimpleList(norm=mirna_matrix), colData, metadata=list(sign=mirna_keep))

where, mirna_matrix is the normalized counts expression, colData is the metadata information and mirna_keep the list of miRNAs to be used by this function.

Examples

library(org.Mm.eg.db) library(clusterProfiler) data(isoExample) # ego <- enrichGO(row.names(assay(gene_ex_rse, "norm")), # org.Mm.eg.db, "ENSEMBL", ont = "BP") # data = isoNetwork(mirna_ex_rse, gene_ex_rse, ma_ex, org = slot(ego, "result")) # isoPlotNet(data)
#> Error: <text>:7:46: unexpected ')' #> 6: # data = isoNetwork(mirna_ex_rse, gene_ex_rse, ma_ex, #> 7: org = slot(ego, "result")) #> ^