Eigenarray

1999, O. Alter et al., ‘‘Singular Value Decomposition for Gene Expression Data Processing and Modeling.’’ In: After the Genome V (December 6–10, 1999, Jackson Hole, WY). We describe the use of singular value decomposition in transforming gene expression data from genes/arrays space to ‘‘eigengenes’’/‘‘eigenarrays’’ space, where the eigengenes and eigenarrays are unique orthonormal superpositions of the genes and arrays, respectively.

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2000, O. Alter et al., ‘‘Singular value decomposition for genome-wide expression data processing and modeling,’’ Proc. Natl. Acad. Sci. USA 97 (18), pp. 10101–10106. doi:10.1073/pnas.97.18.10101. 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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2002, T. O. Nielsen et al., ‘‘Molecular Characterisation of Soft Tissue Tumours: a Gene Expression Study,’’ Lancet 359 (9314), pp. 1301–1307. doi: 10.1016/S0140-6736(02)08270-3. The separation of the calponin-positive leiomyosarcoma subgroup from the calponin-negative tumours was dominant, and resulted in an important eigengene and corresponding eigenarray.

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2006, C. M. Li and R. R. Klevecz, ‘‘A rapid genome-scale response of the transcriptional oscillator to perturbation reveals a period-doubling path to phenotypic change,’’ Proc. Natl. Acad. Sci. USA 103 (44), pp. 16254–16259. doi: 10.1073/pnas.0604860103. A reconstruction of the attractor can be seen in the plot of the principal eigengenes 2, 3, and 4.

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