Obtain and Visualize Regulome-Gene Expression Correlations in Cancer

Builds a 'SQLite' database file of pre-calculated transcription factor/microRNA-gene correlations (co-expression) in cancer from the Cistrome Cancer Liu et al. (2011) and 'miRCancerdb' databases (in press). Provides custom classes and functions to query, tidy and plot the correlation data.

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Transcription factors and microRNAs are important for regulating the gene expression in normal physiology and pathological conditions. Many bioinformatics tools were built to predict and identify transcription factors and microRNA targets and their role in development of diseases including cancers. The availability of public access high-throughput data allowed for data-driven predictions and discoveries. Here, we build on some of these tools and integrative analyses and provide a tool to access, manage and visualize data from open source databases. cRegulome provides a programmatic access to the regulome (microRNA and transcription factor) correlations with target genes in cancer. The package obtains a local instance of Cistrome Cancer and miRCancerdb databases and provides classes and methods to interact with and visualize the correlation data.

What is cRegulome used for?

cRegulome provides programmatic access to regulome-gene correlation data in cancer from different data sources. Researches who are interested in studying the role of microRNAs and transcription factors in cancer can use this package to construct a small or large scale queries to answer different questions:

  • Which microRNAs and/or transcription factors are associated with a particular set of genes?
  • What different regulation patterns a microRNA or a transcription factor can take in different types of cancer?
  • For a given set of regulatory elements, which genes are likely to be regulated by these elements in a certain type of cancer?

In addition, cRegulome can be used with other R packages like igraph to study the co-regulation networks in different types of cancer.

Getting started

To get starting with cRegulome we show a very quick example. We first start by downloading a small test database file, make a simple query and convert the output to a cRegulome object to print and visualize.

# install the package from CRAN
# install the development version from github
# install the development version and build vignette from github 
devtools::install_github('ropensci/cRegulome', build_vignettes = TRUE)
# load required libraries
if(!file.exists('cRegulome.db')) {
    get_db(test = TRUE)
# connect to the db file
conn <- dbConnect(SQLite(), 'cRegulome.db')

Or access the same test set file from the package directly

# locate the testset file and connect
fl <- system.file('extdata', 'cRegulome.db', package = 'cRegulome')
conn <- dbConnect(SQLite(), fl)
# enter a custom query with different arguments
dat <- get_mir(conn,
               mir = 'hsa-let-7g',
               study = 'STES',
               min_abs_cor = .3,
               max_num = 5)
# make a cmicroRNA object   
ob <- cmicroRNA(dat)
# print object
# plot object


More information and examples of using cRegulome


More about the database file here




cRegulome 0.99.0

- cRegulome v0.99.0 (2017-09-06) Submit to rOpenSci

cRegulome 0.1.0

- cRegulome v0.1.0 (2018-02-08) Approved by rOpenSci

cRegulome 0.1.1

- fix installing in default library tree

cRegulome 0.2.0

- Reduced code dependencies
- Improved code performance

cRegulome 0.2.0

- Bug fix: since 0.2.0 the argument targets_only did not work  properly.
The bug is fixed and tested in this release.
- Added the option directed to cor_igraph which allowes for constructing
a directed graph when desired.
- Added the option to limit the query output of get_tf and get_mir to 
a predifined set of genes.

Reference manual

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