Singular value decomposition for genome-wide expression data processing and modeling
AUTOR(ES)
Alter, Orly
FONTE
The National Academy of Sciences
RESUMO
We describe the use of singular value decomposition in transforming genome-wide expression data from genes × arrays space to reduced diagonalized “eigengenes” × “eigenarrays” space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.
ACESSO AO ARTIGO
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=27718Documentos Relacionados
- Similarities and Differences in Genome-Wide Expression Data of Six Organisms
- Vector algebra in the analysis of genome-wide expression data
- yMGV: a database for visualization and data mining of published genome-wide yeast expression data
- A Classification-Based Machine Learning Approach for the Analysis of Genome-Wide Expression Data
- Integrating regulatory motif discovery and genome-wide expression analysis