Spatial Correlation of Gene Expression Measures in Tissue Microarray Core Analysis
| Autor: | Mathieu Emily, Didier Morel, Raphael Marcelpoil, Olivier François |
|---|---|
| Přispěvatelé: | TIMB, Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525 (TIMC-IMAG), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF) |
| Rok vydání: | 2005 |
| Předmět: |
Spatial correlation
Pathology medicine.medical_specialty Random geometry Computational biology Biology 01 natural sciences General Biochemistry Genetics and Molecular Biology 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Breast cancer Gene expression Covariate medicine 0101 mathematics Tissue microarrays Spatial analysis Cancer prognosis [STAT.AP]Statistics [stat]/Applications [stat.AP] Tissue microarray medicine.disease Spatially marked point process Voronoi tesselation 030220 oncology & carcinogenesis Breast carcinoma |
| Zdroj: | Computational and Mathematical Methods in Medicine Computational and Mathematical Methods in Medicine, Hindawi Publishing Corporation, 2005, 6 (1), pp.33-39. ⟨10.1080/10273660500035795⟩ |
| ISSN: | 1607-8578 1027-3662 1748-670X 1748-6718 |
| DOI: | 10.1080/10273660500035795 |
| Popis: | International audience; Tissue microarrays (TMAs) make possible the screening of hundreds of different tumour samples for the expression of a specific protein. Automatic features extraction procedures lead to a series of covariates corresponding to the averaged stained scores. In this article, we model the random geometry of TMA cores using voronoi tesselations. This formalism enables the computation of indices of spatial correlation of stained scores using both classical and novel approaches. The potential of these spatial statistics to correctly discriminate between diseased and non-diseased cases is evaluated through the analysis of a TMA containing samples of breast carcinoma data. The results indicate a significant improvement in the breast cancer prognosis. |
| Databáze: | OpenAIRE |
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