A sound epistemological foundation for biological inquiry comes, in part, from application of valid statistical procedures. This tenet is widely appreciated by scientists studying the new realm of highdimensional biology, or 'omic' research, which involves multiplicity at unprecedented scales. Many papers aimed at the high-dimensional biology community describe the development or application of statistical techniques. The validity of many of these is questionable, and a shared understanding about the epistemological foundations of the statistical methods themselves seems to be lacking. Here we offer a framework in which the epistemological foundation of proposed statistical methods can be evaluated. The challenge we faceHigh-dimensional biology (HDB) encompasses the 'omic' technologies 1 and can involve thousands of genetic polymorphisms, sequences, expression levels, protein measurements or combination thereof. How do we derive knowledge about the validity of statistical methods for HDB? A shared understanding regarding this second-order epistemological question seems to be lacking in the HDB community. Although our comments are applicable to HDB overall, we emphasize microarrays, where the need is acute. "The field of expression data analysis is particularly active with novel analysis strategies and tools being published weekly" (ref. 2; Fig. 1), and the value of many of these methods is questionable 3 . Some results produced by using these methods are so anomalous that a breed of 'forensic' statisticians 4,5 , who doggedly detect and correct other HDB investigators' prominent mistakes, has been created.Here we offer a 'meta-methodology' and framework in which to evaluate epistemological foundations of proposed statistical methods. On the basis of this framework, we consider that many statistical methods offered to the HDB community do not have an adequate epistemological foundation. We hope the framework will help methodologists to develop robust methods and help applied investigators to evaluate whether statistical methods are valid.

No abstract

Motivation: Since 2011, The Cancer Genome Atlas’ (TCGA) files have been accessible through HTTP from a public site, creating entirely new possibilities for cancer informatics by enhancing data discovery and retrieval. Significantly, these enhancements enable the reporting of analysis results that can be fully traced to and reproduced using their source data. However, to realize this possibility, a continually updated road map of files in the TCGA is required. Creation of such a road map represents a significant data modeling challenge, due to the size and fluidity of this resource: each of the 33 cancer types is instantiated in only partially overlapping sets of analytical platforms, while the number of data files available doubles approximately every 7 months.Results: We developed an engine to index and annotate the TCGA files, relying exclusively on third-generation web technologies (Web 3.0). Specifically, this engine uses JavaScript in conjunction with the World Wide Web Consortium’s (W3C) Resource Description Framework (RDF), and SPARQL, the query language for RDF, to capture metadata of files in the TCGA open-access HTTP directory. The resulting index may be queried using SPARQL, and enables file-level provenance annotations as well as discovery of arbitrary subsets of files, based on their metadata, using web standard languages. In turn, these abilities enhance the reproducibility and distribution of novel results delivered as elements of a web-based computational ecosystem. The development of the TCGA Roadmap engine was found to provide specific clues about how biomedical big data initiatives should be exposed as public resources for exploratory analysis, data mining and reproducible research. These specific design elements align with the concept of knowledge reengineering and represent a sharp departure from top-down approaches in grid initiatives such as CaBIG. They also present a much more interoperable and reproducible alternative to the still pervasive use of data portals.Availability: A prepared dashboard, including links to source code and a SPARQL endpoint, is available at http://bit.ly/TCGARoadmap. A video tutorial is available at http://bit.ly/TCGARoadmapTutorial.Contact: robbinsd@uab.edu