Supplemental material for O. Alter, P. O. Brown and D. Botstein, "Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling," Proceedings of the National Academy of Sciences (PNAS) USA 97 (18), pp. 10101–10106 (August 2000); doi: 10.1073/pnas.97.18.10101.
Feature: National Research Council, Mathematics and 21st Century Biology. Washington, DC: National Academies Press (July 2005), 149 pp.
Mention: 9th most cited Proceedings of the National Academy of Sciences (PNAS) USA paper of the year 2000 and 56th most cited PNAS paper of all time, Google Scholar (August 26, 2022).
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.

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Expression Datasets and Classification Lists of Cell Cycle-Regulated Genes

Reproduced from Spellman et al.

Gene Annotation Lists

Singular Value Decomposition-Normalized and Sorted Expression Datasets