MOTIVATION: Genomic high-throughput technology generates massive data, providing opportunities to understand countless facets of the functioning genome. It also raises profound issues in identifying data relevant to the biology being studied. RESULTS: We introduce a method for the analysis of pathologic biology that unravels the disease characteristics of high dimensional data. The method, disease-specific genomic analysis (DSGA), is intended to precede standard techniques like clustering or class prediction, and enhance their performance and ability to detect disease. DSGA measures the extent to which the disease deviates from a continuous range of normal phenotypes, and isolates the aberrant component of data. In several microarray cancer datasets, we show that DSGA outperforms standard methods. We then use DSGA to highlight a novel subdivision of an important class of genes in breast cancer, the estrogen receptor (ER) cluster. We also identify new markers distinguishing ductal and lobular breast cancers. Although our examples focus on microarrays, DSGA generalizes to any high dimensional genomic/proteomic data.
MOTIVATION: Genomic high-throughput technology generates massive data, providing opportunities to understand countless facets of the functioning genome. It also raises profound issues in identifying data relevant to the biology being studied. RESULTS: We introduce a method for the analysis of pathologic biology that unravels the disease characteristics of high dimensional data. The method, disease-specific genomic analysis (DSGA), is intended to precede standard techniques like clustering or class prediction, and enhance their performance and ability to detect disease. DSGA measures the extent to which the disease deviates from a continuous range of normal phenotypes, and isolates the aberrant component of data. In several microarray cancer datasets, we show that DSGA outperforms standard methods. We then use DSGA to highlight a novel subdivision of an important class of genes in breast cancer, the estrogen receptor (ER) cluster. We also identify new markers distinguishing ductal and lobular breast cancers. Although our examples focus on microarrays, DSGA generalizes to any high dimensional genomic/proteomic data.
Authors: Geoffrey S Baird; Sally K Nelson; Tracy R Keeney; Alex Stewart; Stephen Williams; Stephan Kraemer; Elaine R Peskind; Thomas J Montine Journal: Am J Pathol Date: 2011-11-26 Impact factor: 4.307
Authors: Vilde D Haakensen; Ole Christian Lingjaerde; Torben Lüders; Margit Riis; Aleix Prat; Melissa A Troester; Marit M Holmen; Jan Ole Frantzen; Linda Romundstad; Dina Navjord; Ida K Bukholm; Tom B Johannesen; Charles M Perou; Giske Ursin; Vessela N Kristensen; Anne-Lise Børresen-Dale; Aslaug Helland Journal: BMC Med Genomics Date: 2011-11-01 Impact factor: 3.063
Authors: Vilde D Haakensen; Margarethe Biong; Ole Christian Lingjærde; Marit Muri Holmen; Jan Ole Frantzen; Ying Chen; Dina Navjord; Linda Romundstad; Torben Lüders; Ida K Bukholm; Hiroko K Solvang; Vessela N Kristensen; Giske Ursin; Anne-Lise Børresen-Dale; Aslaug Helland Journal: Breast Cancer Res Date: 2010-08-27 Impact factor: 6.466
Authors: Olga V Razorenova; Laura Castellini; Renata Colavitti; Laura E Edgington; Monica Nicolau; Xin Huang; Barbara Bedogni; Edward M Mills; Matthew Bogyo; Amato J Giaccia Journal: Mol Cell Biol Date: 2013-12-16 Impact factor: 4.272
Authors: Holly E Barker; Joan Chang; Thomas R Cox; Georgina Lang; Demelza Bird; Monica Nicolau; Holly R Evans; Alison Gartland; Janine T Erler Journal: Cancer Res Date: 2011-01-13 Impact factor: 12.701