CeRNASeek (version 2.1.3)

ceRNA.cernia: identifying miRNA sponge interactions using ceRNA.cernia function.

Description

identifying miRNA sponge interactions using ceRNA.cernia.In this function We implement cernia score to identify miRNA sponge interactions. The fraction of common miRNAs; 1. The fraction of common miRNAs; 2. The density of the MREs for all shared miRNAs; 3. The distribution of MREs of the putative ceRNAs; 4. The relation between the overall number of MREs for a putative ceRNA, compared with the number of miRNAs that yield these MREs; 5. The density of the hybridization energies related to MREs for all shared miRNAs; 6. The DT-Hybrid recommendation scores; 7. The pairwise Pearson correlation between putative ceRNA expressions.

Usage

ceRNA.cernia(miRtar, targetce = NULL, geneexp, miRexp, mres, numMIR = 3, cor_cutoff = 0,
             s_cutoff = 0.5)

Arguments

miRtar

A data frame representing the relationship between miRNA and target. The data frame contains the name of the miRNA and target regulatory relationship.

targetce

a character string (vector) specifying candidate target name to analyse (default (targetce = NULL)).

geneexp

An input target expression data frame, the columns are genes and the rows are samples.The expression value may be gene expression ,non-coding RNA expression or expression values of circRNAs and so on.

miRexp

An input miRNA expression data frame, the columns are miRNA and the rows are samples.

mres

miRNA response elements (mres) data frame, each row contains five elements: mirna, target, energy, gap_l, gap_r.

numMIR

a numeric vector that specify the minimum number of 2 gene-shared miRNAs.

cor_cutoff

a numeric vector of the form method specifying threshold between ceRNA interactions correlation,default(cor_cutoff=0).

s_cutoff

the threshold of seven comprehensive scores.

Value

A list of identified miRNA sponge interactions containing following components:

• targetce represented target names,respectively.

• anotherce names of modulators that another target(modulators) constitutes a ceRNA interaction relation.

• miRNAs_num names of miRNA in the triplet.

• Score 1 The fraction of common miRNAs;

• Score 2 The density of the MREs for all shared miRNAs;

• Score 3 The distribution of MREs of the putative ceRNAs;

• Score 4 The relation between the overall number of MREs for a putative ceRNA, compared with the number of miRNAs that yield these MREs;

• Score 5 The density of the hybridization energies related to MREs for all shared miRNAs;

• Score 6 The DT-Hybrid recommendation scores;

• Score 7 The pairwise Pearson correlation between putative ceRNA expressions.

• Normalized score combine the sum of 7 scores and standardize by z-score.

Details

Note:All the arguments without default value must be assigned.The miRNA in the file of the target-miRNA regulatory relationship pair should also be present in the expression profile data file. Internal functions (parMM, graphWeights, recommendation, dtHybrid) of cernia method are from the website: https://github.com/dsardina/cernia Copyright 2016 Rosalba Giugno Licensed under the Apache License, Version 2.0 (the 'License')

References

Sardina D S , Alaimo S , Ferro A , et al. A novel computational method for inferring competing endogenous interactions[J]. Briefings in Bioinformatics, 2016:bbw084.

Examples

# NOT RUN {
##identifying miRNA sponge interactions.
##Here we take six candidate targets(modulators) and corresponding expression 
##data for example,Specify target(PTEN) to predict ceRNA interaction.
ceRNA.cernia(miRtar=dataset[["miRtar"]], targetce = "PTEN", geneexp=dataset[["geneexp"]],
          numMIR = 1, miRexp=dataset[["miRexp"]], mres=dataset[["mres"]], 
		  cor_cutoff = 0.2, s_cutoff = 0.5) 
# }