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 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.
Keywords
Normalization (sociology)GenomeComputational biologySingular value decompositionBiologyGeneSortingExpression (computer science)Orthonormal basisGeneticsAlgorithmComputer sciencePhysics
Affiliated Institutions
Related Publications
Cluster analysis and display of genome-wide expression patterns
A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according ...
Transcriptional Regulatory Networks in <i>Saccharomyces cerevisiae</i>
We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as m...
Tensor Decompositions and Applications
This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or N-way array. Decompositions...
Cross-Validatory Choice of the Number of Components From a Principal Component Analysis
A method is described for choosing the number of components to retain in a principal component analysis when the aim is dimensionality reduction. The correspondence between prin...
Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays
Oligonucleotide arrays can provide a broad picture of the state of the cell, by monitoring the expression level of thousands of genes at the same time. It is of interest to deve...
Publication Info
- Year
- 2000
- Type
- article
- Volume
- 97
- Issue
- 18
- Pages
- 10101-10106
- Citations
- 1986
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
1986
OpenAlex
Cite This
Orly Alter,
Patrick O. Brown,
David Botstein
(2000).
Singular value decomposition for genome-wide expression data processing and modeling.
Proceedings of the National Academy of Sciences
, 97
(18)
, 10101-10106.
https://doi.org/10.1073/pnas.97.18.10101
Identifiers
- DOI
- 10.1073/pnas.97.18.10101