title: categoryCompare, an analytical tool based on feature annotations.
abstract: Assessment of high-throughput-omics data initially focuses on relative or raw levels of a particular feature, such as an expression value for a transcript, protein, or metabolite. At a second level, analyses of annotations including known or predicted functions and associations of each individual feature, attempt to distill biological context. Most currently available comparative- and meta-analyses methods are dependent on the availability of identical features across data sets, and concentrate on determining features that are differentially expressed across experiments, some of which may be considered "biomarkers." The heterogeneity of measurement platforms and inherent variability of biological systems confounds the search for robust biomarkers indicative of a particular condition. In many instances, however, multiple data sets show involvement of common biological processes or signaling pathways, even though individual features are not commonly measured or differentially expressed between them. We developed a methodology, categoryCompare, for cross-platform and cross-sample comparison of high-throughput data at the annotation level. We assessed the utility of the approach using hypothetical data, as well as determining similarities and differences in the set of processes in two instances: (1) denervated skin vs. denervated muscle, and (2) colon from Crohn's disease vs. colon from ulcerative colitis (UC). The hypothetical data showed that in many cases comparing annotations gave superior results to comparing only at the gene level. Improved analytical results depended as well on the number of genes included in the annotation term, the amount of noise in relation to the number of genes expressing in unenriched annotation categories, and the specific method in which samples are combined. In the skin vs. muscle denervation comparison, the tissues demonstrated markedly different responses. The Crohn's vs. UC comparison showed gross similarities in inflammatory response in the two diseases, with particular processes specific to each disease.
authors.affiliation: Department of Molecular and Cellular Biochemistry, University of Kentucky Lexington, KY, USA.
authors.forename: Robert M
authors.initials: RM
authors.lastname: Flight
authors.affiliation: Department of Anatomical Sciences and Neurobiology, University of Louisville Louisville, KY, USA ; Department of Neurological Surgery, Kentucky Spinal Cord Injury Research Center, University of Louisville Louisville, KY, USA.
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Harrison
authors.affiliation: Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA, USA.
authors.forename: Fahim
authors.initials: F
authors.lastname: Mohammad
authors.affiliation: Miami Project to Cure Paralysis, Department of Neurological Surgery and Neurology, University of Miami Miller School of Medicine Miami, FL, USA.
authors.forename: Mary B
authors.initials: MB
authors.lastname: Bunge
authors.affiliation: Neurorestoration Group, Wolfson Centre for Age-Related Research, King's College London London, UK.
authors.forename: Lawrence D F
authors.initials: LD
authors.lastname: Moon
authors.affiliation: Department of Anatomical Sciences and Neurobiology, University of Louisville Louisville, KY, USA ; Department of Neurological Surgery, Kentucky Spinal Cord Injury Research Center, University of Louisville Louisville, KY, USA ; Department of Neurological Surgery, University of Louisville Louisville, KY, USA.
authors.forename: Jeffrey C
authors.initials: JC
authors.lastname: Petruska
authors.affiliation: Bioinformatics and Biomedical Computing Laboratory, Department of Computer Engineering and Computer Science, University of Louisville Louisville, KY, USA.
authors.forename: Eric C
authors.initials: EC
authors.lastname: Rouchka
keywords: comparative analysis; meta-analysis; metabolomics; proteomics; transcriptomics