Clustering miRNAs-genes pairs

isoNetwork(mirna_rse, gene_rse, summarize = NULL, target = NULL,
  org = NULL, enrich = NULL, genename = "ENSEMBL", min_cor = -0.6,
  min_fc = 0.5)

Arguments

mirna_rse

SummarizedExperiment::SummarizedExperiment with miRNA information. See details.

gene_rse

SummarizedExperiment::SummarizedExperiment with gene information. See details.

summarize

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

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)

enrich

The output of clusterProfiler::enricher() of similar functions.

genename

Character keytype of the gene names in gene_rse object.

min_cor

Numeric cutoff to consider a miRNA to regulate a target.

min_fc

Numeric cutoff to consider as the minimum log2FoldChange between groups to be considered in the analysis.

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 = ego)
#> Error: summarize %in% names(colData(mirna_rse)) are not all TRUE
#> Error in obj$analysis: object of type 'closure' is not subsettable