Nature Biotechnology
23, 238 - 243 (2005)
Published online: 16 January 2005; | doi:10.1038/nbt1058
Functional annotation and network reconstruction through cross-platform integration of microarray dataXianghong Jasmine Zhou1, 2, Ming-Chih J Kao2, 3, Haiyan Huang2, 4, Angela Wong1, 5, Juan Nunez-Iglesias1, Michael Primig6, Oscar M Aparicio1, Caleb E Finch1, 5, Todd E Morgan1, 5
& Wing Hung Wong2, 71
Program in Molecular and Computational Biology, University of Southern California, Los Angeles, California 90089-1113, USA. 2
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. 3
School of Medicine, University of Michigan, Ann Arbor, Michigan 48109, USA. 4
Department of Statistics, University of California, Berkeley, California 94720, USA. 5
Andrus Gerontology Center, University of Southern California, Los Angeles, California 90089-0191, USA. 6
Biozentrum & Swiss Institute of Bioinformatics, University of Basel, CH-4056 Basel, Switzerland. 7
Department of Statistics, Harvard University, Boston, Massachusetts 02138-2901, USA.
Correspondence should be addressed to Xianghong Jasmine Zhou xjzhou@usc.edu or Wing Hung Wong whwong@stanford.eduThe rapid accumulation of microarray data translates into a need for methods to effectively integrate data generated with different platforms. Here we introduce an approach, 2nd-order expression analysis, that addresses this challenge by first extracting expression patterns as meta-information from each data set (1st-order expression analysis) and then analyzing them across multiple data sets. Using yeast as a model system, we demonstrate two distinct advantages of our approach: we can identify genes of the same function yet without coexpression patterns and we can elucidate the cooperativities between transcription factors for regulatory network reconstruction by overcoming a key obstacle, namely the quantification of activities of transcription factors. Experiments reported in the literature and performed in our lab support a significant number of our predictions.
MORE ARTICLES LIKE THIS These links to content published by NPG are automatically generated.
|
