Extraction of Business Financial Information from XBRL Documents

Functions to extract business financial information from
an Extensible Business Reporting Language (XBRL) instance file and the
associated collection of files that defines its Discoverable Taxonomy
Set (DTS), usually disseminated across different remote locations.
XBRL documents employ many technologies defined by XML standards, such
as XML Schema, Namespaces, XLink, and XPath, which make the extraction
of information involved. The functions provided by this package
address this complexity, returning data frames containing the enclosed
information readily available to perform analyses.
XBRL has been successfully tested in analyzing 10-K and 10-Q
submissions from USA filing companies that are publicly available at
the EDGAR system of the Securities and Exchange Commission (SEC).
It has been reported to work with inline XBRL documents.
As published taxonomy files (such as
"http://xbrl.fasb.org/us-gaap/2013/elts/us-gaap-2013-01-31.xsd")
are immutable and are used by most filers, XBRL offers the option of
downloading them only the first time they are referred, keeping a
local cache copy that can be used from then on. This speeds-up the
process, specially on slow connections.
XBRL is not and does not aim to be (at least yet) a validating XBRL
processor.
XBRL is still a work in progress. As such, functions and produced data
frames may change structure in future versions until stabilization.

Extraction of Business Financial Information from 'XBRL'
Documents
Functions to extract business financial information from
an Extensible Business Reporting Language ('XBRL') instance file and the
associated collection of files that defines its 'Discoverable' Taxonomy
Set ('DTS').

Exploring Genomic Relations at the Gene and SNP Level Through
Enrichment, Similarity and Network Analysis
Hai Fang, Bogdan Knezevic, Katie L Burnham, Julian C Knight
Matrix, GenomicRanges, RCircos, ggplot2, ggbio, MASS,
grDevices
The central goal of XGR is to provide a data interpretation system. It is designed to make a user-defined gene or SNP list more interpretable by comprehensively utilising ontology and network information to reveal relationships and enhance opportunities for biological discovery. XGR is unique in supporting a broad range of ontologies (including knowledge of biological and molecular functions, pathways, diseases and phenotypes - in both human and mouse) and different types of networks (including functional, physical and pathway interactions). There are two core functionalities of the XGR. The first is basic infrastructures for easy access to built-in ontologies and networks. The second is extensible functions, now including 1) enrichment analysis using either built-in or custom ontologies, 2) similarity analysis for calculating semantic similarity between genes (or SNPs) based on their ontology annotation profiles, and 3) network analysis for identification of gene networks given a query list of (significant) genes or SNPs. Together with its web app, XGR aims to provide a user-friendly tool for exploring genomic relations at the gene and SNP level.
'xRDataLoader.r' 'xRdWrap.r' 'xFunArgs.r' 'xRd2HTML.r'
'xDAGanno.r' 'xDAGsim.r' 'xConverter.r' 'xEnricher.r'
'xEnricherGenes.r' 'xEnricherSNPs.r' 'xEnricherYours.r'
'xEnrichViewer.r' 'xSocialiser.r' 'xSocialiserGenes.r'
'xSocialiserSNPs.r' 'xCircos.r' 'xSubneterGenes.r'
'xSubneterSNPs.r' 'xVisNet.r' 'xPrioritiser.r'
'xPrioritiserGenes.r' 'xPrioritiserSNPs.r'
'xPrioritiserPathways.r' 'xPrioritiserManhattan.r'