gdcCEAnalysis: Competing endogenous RNAs (ceRNAs) analysis

In Jialab-UCR/GDCRNATools: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC

Description

Identify ceRNAs by (1) number of shared miRNAs between lncRNA and mRNA; (2) expression correlation of lncRNA and mRNA; (3) regulation similarity of shared miRNAs on lncRNA and mRNA; (4) sensitivity correlation

Usage

gdcCEAnalysis(lnc, pc, deMIR = NULL, lnc.targets = "starBase",
  pc.targets = "starBase", rna.expr, mir.expr)

Arguments

lnc	a vector of Ensembl long non-coding gene ids
pc	a vector of Ensembl protein coding gene ids
deMIR	a vector of differentially expressed miRNAs. Default is NULL
lnc.targets	a character string specifying the database of miRNA-lncRNA interactions. Should be one of 'spongeScan', 'starBase', and 'miRcode'. Default is 'starBase'. Or a list of miRNA-lncRNA interactions generated by users
pc.targets	a character string specifying the database of miRNA-lncRNA interactions. Should be one of 'spongeScan', 'starBase', and 'miRcode'. Default is 'starBase'. Or a list of miRNA-lncRNA interactions generated by users
rna.expr	voom transformed gene expression data
mir.expr	voom transformed mature miRNA expression data

Value

A dataframe containing ceRNA pairs, expression correlation between lncRNA and mRNA, the number and hypergeometric significance of shared miRNAs, regulation similarity score, and the mean sensitity correlation (the difference between Pearson correlation and partial correlation) of multiple lncRNA-miRNA-mRNA triplets, etc.

Author(s)

Ruidong Li and Han Qu

References

Paci P, Colombo T, Farina L. Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer. BMC systems biology. 2014 Jul 17;8(1):83.

Examples

####### ceRNA network analysis #######
deLNC <- c('ENSG00000260920','ENSG00000242125','ENSG00000261211')
dePC <- c('ENSG00000043355','ENSG00000109586','ENSG00000144355')
genes <- c(deLNC, dePC)
samples <- c('TCGA-2F-A9KO-01', 'TCGA-2F-A9KP-01', 
            'TCGA-2F-A9KQ-01', 'TCGA-2F-A9KR-01', 
            'TCGA-2F-A9KT-01', 'TCGA-2F-A9KW-01')
rnaExpr <- data.frame(matrix(c(2.7,7.0,4.9,6.9,4.6,2.5,
                    0.5,2.5,5.7,6.5,4.9,3.8,
                    2.1,2.9,5.9,5.7,4.5,3.5,
                    2.7,5.9,4.5,5.8,5.2,3.0,
                    2.5,2.2,5.3,4.4,4.4,2.9,
                    2.4,3.8,6.2,3.8,3.8,4.2),6,6), 
                    stringsAsFactors=FALSE)
rownames(rnaExpr) <- genes
colnames(rnaExpr) <- samples

mirExpr <- data.frame(matrix(c(7.7,7.4,7.9,8.9,8.6,9.5,
                    5.1,4.4,5.5,8.5,4.4,3.5,
                    4.9,5.5,6.9,6.1,5.5,4.1,
                    12.4,13.5,15.1,15.4,13.0,12.8,
                    2.5,2.2,5.3,4.4,4.4,2.9,
                    2.4,2.7,6.2,1.5,4.4,4.2),6,6),
                    stringsAsFactors=FALSE)
colnames(mirExpr) <- samples
rownames(mirExpr) <- c('hsa-miR-340-5p','hsa-miR-181b-5p',
                    'hsa-miR-181a-5p', 'hsa-miR-181c-5p',
                    'hsa-miR-199b-5p','hsa-miR-182-5p')

ceOutput <- gdcCEAnalysis(lnc       = deLNC, 
                        pc          = dePC, 
                        lnc.targets = 'starBase', 
                        pc.targets  = 'starBase', 
                        rna.expr    = rnaExpr, 
                        mir.expr    = mirExpr)

Jialab-UCR/GDCRNATools documentation built on Nov. 28, 2020, 11:23 a.m.