Supplemental material for O. Alter, P. O. Brown and D. Botstein, "Processing and Modeling Genome-Wide Expression Data Using Singular Value Decomposition." In: M. L. Bittner, Y. Chen, A. N. Dorsel and E. R. Dougherty, editors, Microarrays: Optical Technologies and Informatics. Bellingham, WA: International Society for Optics and Photonics (SPIE), vol. 4266, pp. 171–186 (January 21, 2001); doi: 10.1117/12.427986.
Abstract:
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 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.



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Expression Datasets and Classification Lists of Cell Cycle-Regulated Genes

Reproduced from Spellman et al.

Gene Annotation Lists

Annotate_Cycle.txt
Reproduced from Spellman et al.

Singular Value Decomposition-Normalized and Sorted Expression Datasets