Abstract
BackgroundStatistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem.ResultsThe terms in OBCS including ‘data collection’, ‘data transformation in statistics’, ‘data visualization’, ‘statistical data analysis’, and ‘drawing a conclusion based on data’, cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. Currently, OBCS comprehends 878 terms, representing 20 BFO classes, 403 OBI classes, 229 OBCS specific classes, and 122 classes imported from ten other OBO ontologies.We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. Other ongoing projects using OBCS for statistical data processing are also discussed.The OBCS source code and documentation are available at: https://github.com/obcs/obcs.ConclusionsThe Ontology of Biological and Clinical Statistics (OBCS) is a community-based open source ontology in the domain of biological and clinical statistics. OBCS is a timely ontology that represents statistics-related terms and their relations in a rigorous fashion, facilitates standard data analysis and integration, and supports reproducible biological and clinical research.
Highlights
Statistics play a critical role in biological and clinical research
To facilitate discovery and usability of these data to external users, and to address the goal of reproducibility, ImmPort is seeking wherever possible to draw on publicly available ontologies such as the Cell Ontology (CL) [5] and the Protein Ontology (PRO) [6] as sources for the terms used in these templates
The meta-data schema of OBCS is implemented using OWL annotation properties defined in the Information Artifact Ontology (IAO, http://purl.obolibrary.org/obo/iao), which is widely used by Open Biological and Biomedical Ontologies (OBO) Foundry and other ontologies
Summary
Statistics play a critical role in biological and clinical research. most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The movement to advance reproducibility of research advocates the use of open data, standard operating procedures, and reproducibility of methods used in both computation [1] and statistics [2]. To support such reproducibility it is imperative that standard metadata formats be used to describe how results were generated. To facilitate discovery and usability of these data to external users, and to address the goal of reproducibility, ImmPort is seeking wherever possible to draw on publicly available ontologies such as the Cell Ontology (CL) [5] and the Protein Ontology (PRO) [6] as sources for the terms used in these templates
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