dorsal/arxiv
View SchemaSingular Value Decomposition and Principal Component Analysis
| Authors | Michael E. Wall, Andreas Rechtsteiner, Luis M. Rocha |
|---|---|
| Categories | |
| ArXiv ID | physics/0208101 |
| URL | https://arxiv.org/abs/physics/0208101 |
| Journal | Wall ME, Rechtsteiner A, Rocha LM. In: A Practical Approach to Microarray Data Analysis. (Berrar DP, Dubitzky W, Granzow M, eds.), pp. 91-109, Kluwer: Norwell, MA (2003) |
Abstract
This chapter describes gene expression analysis by Singular Value Decomposition (SVD), emphasizing initial characterization of the data. We describe SVD methods for visualization of gene expression data, representation of the data using a smaller number of variables, and detection of patterns in noisy gene expression data. In addition, we describe the precise relation between SVD analysis and Principal Component Analysis (PCA) when PCA is calculated using the covariance matrix, enabling our descriptions to apply equally well to either method. Our aim is to provide definitions, interpretations, examples, and references that will serve as resources for understanding and extending the application of SVD and PCA to gene expression analysis.
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"abstract": "This chapter describes gene expression analysis by Singular Value\nDecomposition (SVD), emphasizing initial characterization of the data. We\ndescribe SVD methods for visualization of gene expression data, representation\nof the data using a smaller number of variables, and detection of patterns in\nnoisy gene expression data. In addition, we describe the precise relation\nbetween SVD analysis and Principal Component Analysis (PCA) when PCA is\ncalculated using the covariance matrix, enabling our descriptions to apply\nequally well to either method. Our aim is to provide definitions,\ninterpretations, examples, and references that will serve as resources for\nunderstanding and extending the application of SVD and PCA to gene expression\nanalysis.",
"arxiv_id": "physics/0208101",
"authors": [
"Michael E. Wall",
"Andreas Rechtsteiner",
"Luis M. Rocha"
],
"categories": [
"physics.bio-ph",
"physics.data-an",
"q-bio.QM"
],
"journal_ref": "Wall ME, Rechtsteiner A, Rocha LM. In: A Practical Approach to\n Microarray Data Analysis. (Berrar DP, Dubitzky W, Granzow M, eds.), pp.\n 91-109, Kluwer: Norwell, MA (2003)",
"title": "Singular Value Decomposition and Principal Component Analysis",
"url": "https://arxiv.org/abs/physics/0208101"
},
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