CeRNASeek (version 1.0)

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

Description

identifying miRNA sponge interactions using ceRNA.cmi.In this function We implement CMI methods to identify miRNA sponge interactions.

Usage

ceRNA.cmi(miRtar, targetce = NULL, geneexp, miRexp, numMIR = 1, num_perm = 100, 
cutoff = 0.05)

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.

numMIR

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

num_perm

The number of random default(num_perm=100).

cutoff

a numeric vector of the form method specifying threshold between ceRNA interactions default(cutoff=0.05).

Value

A list of identified miRNA sponge interactions containing following components:

• ceRNA_cmi predicted triplets and related information,a 5 columns dataframe as following:

  • targetce represented target names,respectively.

  • miRNA names of miRNA in the triplet.

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

  • cmi Conditional mutual information(CMI) of triplets calculated using expression values.

  • pvalue The p value of the identified ceRNA interaction relation by CMI.

• ceRNA_comP Number of miRNAs interacting with each target in the input file.

  • targetce represented target names,respectively.

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

  • commonMiR names of miRNA shared by predicted ceRNA.

  • xsq Chi-square value of p-value pvalue in conditional mutual information .

  • comP The p value calculated by incomplete gamma function .

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.

References

Sumazin P , Yang X , Chiu H S , et al. An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma[J]. Cell, 2011, 147(2):0-381.

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.cmi(miRtar=dataset[["miRtar"]], targetce = "PTEN", geneexp=dataset[["geneexp"]],
          numMIR = 1, miRexp=dataset[["miRexp"]],num_perm=50)
# }