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)
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.
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:
In addition, cRegulome can be used with other R packages like igraph
to
study the co-regulation networks in different types of cancer.
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 CRANinstall.packages('cRegulome')
# install the development version from githubdevtools::install_github('ropensci/cRegulome') # install the development version and build vignette from github devtools::install_github('ropensci/cRegulome', build_vignettes = TRUE)
# load required librarieslibrary(cRegulome)library(RSQLite)library(ggplot2)
if(!file.exists('cRegulome.db')) { get_db(test = TRUE)} # connect to the db fileconn <- dbConnect(SQLite(), 'cRegulome.db')
Or access the same test set file from the package directly
# locate the testset file and connectfl <- system.file('extdata', 'cRegulome.db', package = 'cRegulome')conn <- dbConnect(SQLite(), fl)
# enter a custom query with different argumentsdat <- get_mir(conn, mir = 'hsa-let-7g', study = 'STES', min_abs_cor = .3, max_num = 5) # make a cmicroRNA object ob <- cmicroRNA(dat)
# print objectob
# plot objectob
More information and examples of using cRegulome
browseVignettes("cRegulome")
More about the database file here
citation("cRegulome")
- cRegulome v0.99.0 (2017-09-06) Submit to rOpenSci
- cRegulome v0.1.0 (2018-02-08) Approved by rOpenSci
- On CRAN
- fix installing in default library tree
- Reduced code dependencies
- Improved code performance
- 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.