Singular value decomposition for genome-wide expression data processing and modeling

2000/08/29 by Orly Alter, Patrick O. Brown, David Botstein · 14 citations
Biochemistry, Genetics and Molecular Biology · #Gene expression and cancer classification #Gene Regulatory Network Analysis #Genomics and Chromatin Dynamics #Normalization (sociology) #Genome #Computational biology #Singular value decomposition #Biology #Gene #Sorting #Expression (computer science) #Orthonormal basis #Genetics #Algorithm #Computer science #Physics

paper · doi:10.1073/pnas.97.18.10101

openalex publication_date 2000/08/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

We describe the use of singular value decomposition in transforming genome-wide expression data from genes x arrays space to reduced diagonalized "eigengenes" x "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.

Citations

• Comprehensive Identification of Cell Cycle–regulated Genes of the YeastSaccharomyces cerevisiaeby Microarray Hybridization

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