ceRNAR package

We present a novel method for identification of ceRNA-miRNA triplets at specific miRNA expression level. Building on correlation-based approach, we present a computationally efficient approach to be applied following identification of ceRNA-miRNA triplets that is related to the etiology of diseases.

Our approach leverages the genom-wide expression profiles and the correlation-based approach to identify ceRNA-miRNA triplets.It is particularly well suited for the studies having both mRNA and miRNA gene expression data. When applied to such studies, ceRNAR provides a scheme for unveiling the novel ceRNAs that regulate the biological systems and analyzing these novel findings for further biological interpretation.

This package also provides several downstream analyses, including functional, network, survival and peak location analyses, by which to further investigate the identified ceRNA-miRNA triplets and visualize the analytical results to aid in the understanding of the role of such triplets in biological mechanism.

Installation

install.packages("devtools")
library(devtools)
install_github("ywhsiao/ceRNAR")
library(ceRNAR)

Example code

I. using an example data to conduct all functions in this package

1. import example data and check the data

data(gene_exp)
data(mirna_exp)
data(surv_data)

ceRNACustomize(project_name = 'demo', disease_name = 'DLBC', gene_exp = gene_exp, 
               mirna_exp = mirna_exp, surv_data = surv_data)

2. obtain putative mRNA-ceRNA pairs

ceRNAputativePairs(project_name = 'demo', disease_name = 'DLBC', filtering = 'less')

3. conduct main algorithm through one of following ways

• through ceRNAMethod()

ceRNAMethod(project_name = 'demo', disease_name = "DLBC", window_size = 10, num_workers = 1)

• through ceRNApairFilering() and SegmentClusteringPlusPeakMerging()

ceRNApairFilering(project_name = 'demo', disease_name = "DLBC", window_size = 10, num_workers = 1)
SegmentClusteringPlusPeakMerging(project_name = 'demo', disease_name = "DLBC", window_size = 10, num_workers = 1)

4. conduct downstream analysis

ceRNAFunction(project_name = 'demo', disease_name = 'DLBC', pairs_cutoff = 1)
ceRNALocation(project_name = 'demo', disease_name = 'DLBC', mirna = 'hsa-miR-101-3p', window_size = 10)
ceRNAModule(project_name = 'demo', disease_name = 'DLBC', pairs_cutoff = 5, 
            column_sum = 1)
ceRNASurvival(project_name = 'demo', disease_name = 'DLBC', mirnas = 'hsa-miR-101-3p')
ceRNAValidate(project_name = 'demo', disease_name = 'DLBC')
library(SPONGE)
ceRNAIntegrate(project_name = 'demo', disease_name = 'DLBC')

II. (Alternative) retrieving TCGA data from GDC Xena Hub

1. retrieve TCGA data, and check the data

ceRNATCGA(project_name = 'TCGA',disease_name = 'DLBC', timeout = 5000000)

2. change project name and repeat above-mentioned steps from step 2 to step 4