Application of visualization tools to the analysis of histopathological data enhances biological insight and interpretation.

@article{citeulike:1022428, abstract = {Gene expression profiling, metabolomic screens, and other high-dimensional methods have become an integral part of many biological investigations. To facilitate interpretation of these data, it is important to have detailed phenotypic data-including histopathology-to which these data can be associated, or anchored. However, as the amount of phenotypic data increases, associations within and across these data can be difficult to visualize and interpret. We have developed an approach for categorizing and clustering biologically related histopathological diagnoses to facilitate their visualization, thereby increasing the possibility of identifying associations and facilitating the comparison with other data streams. In this study, we utilize histopathological data generated as part of a standardized toxicogenomics compendium study to generate composite histopathological scores and to develop visualizations that facilitate biological insight. The validity of this approach is illustrated by the identification of transcripts that correlate with the pathology diagnoses that comprise the categories of "response to hepatocellular injury" and "repair." This approach is broadly applicable to studies in which histopathology is used to phenotypically anchor other data, and results in visualizations that facilitate biological interpretation and the identification of associations and relationships within the data.}, address = {Cogenics, A Division of Clinical Data, Morrisville, North Carolina, 27560, USA.}, author = {Lobenhofer, E. K. and Boorman, G. A. and Phillips, K. L. and Heinloth, A. N. and Malarkey, D. E. and Blackshear, P. E. and Houle, C. and Hurban, P. }, citeulike-article-id = {1022428}, doi = {10.1080/01926230601072319}, issn = {0192-6233}, journal = {Toxicol Pathol}, keywords = {analysis, graph, mechanism, toxicogenomics, visualization}, number = {7}, pages = {921--928}, posted-at = {2007-01-02 20:01:24}, priority = {2}, title = {Application of visualization tools to the analysis of histopathological data enhances biological insight and interpretation.}, url = {http://dx.doi.org/10.1080/01926230601072319}, volume = {34}, year = {2006} }

TY - JOUR ID - citeulike:1022428 L3 - citeulike-article-id:1022428 TI - Application of visualization tools to the analysis of histopathological data enhances biological insight and interpretation. JF - Toxicol Pathol VL - 34 IS - 7 SP - 921 EP - 928 AD - Cogenics, A Division of Clinical Data, Morrisville, North Carolina, 27560, USA. SN - 0192-6233 N2 - Gene expression profiling, metabolomic screens, and other high-dimensional methods have become an integral part of many biological investigations. To facilitate interpretation of these data, it is important to have detailed phenotypic data-including histopathology-to which these data can be associated, or anchored. However, as the amount of phenotypic data increases, associations within and across these data can be difficult to visualize and interpret. We have developed an approach for categorizing and clustering biologically related histopathological diagnoses to facilitate their visualization, thereby increasing the possibility of identifying associations and facilitating the comparison with other data streams. In this study, we utilize histopathological data generated as part of a standardized toxicogenomics compendium study to generate composite histopathological scores and to develop visualizations that facilitate biological insight. The validity of this approach is illustrated by the identification of transcripts that correlate with the pathology diagnoses that comprise the categories of "response to hepatocellular injury" and "repair." This approach is broadly applicable to studies in which histopathology is used to phenotypically anchor other data, and results in visualizations that facilitate biological interpretation and the identification of associations and relationships within the data. KW - analysis KW - graph KW - mechanism KW - toxicogenomics KW - visualization AU - Lobenhofer, EK AU - Boorman, GA AU - Phillips, KL AU - Heinloth, AN AU - Malarkey, DE AU - Blackshear, PE AU - Houle, C AU - Hurban, P PY - 2006/// UR - http://dx.doi.org/10.1080/01926230601072319 ER -