Processing and modeling genome-wide expression data using singular value decomposition

Orly Alter; Patrick O. Brown; David Botstein

doi:10.1117/12.427986

4 June 2001 Processing and modeling genome-wide expression data using singular value decomposition

Orly Alter, Patrick O. Brown, David Botstein

Proceedings Volume 4266, Microarrays: Optical Technologies and Informatics; (2001) https://doi.org/10.1117/12.427986
Event: BiOS 2001 The International Symposium on Biomedical Optics, 2001, San Jose, CA, United States

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 additive or multiplicative noise, experimental artifacts, or even irrelevant biological processes 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.

Citation Download Citation

Orly Alter, Patrick O. Brown, and David Botstein "Processing and modeling genome-wide expression data using singular value decomposition", Proc. SPIE 4266, Microarrays: Optical Technologies and Informatics, (4 June 2001); https://doi.org/10.1117/12.427986

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