Singular value decomposition for genome-wide expression data processing and modeling.

By O. Alter, Patrick O. Brown, David Botstein. Proceedings of the National Academy of Sciences of the United States of America, 2000

2,115 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/36b40e07dbd0fd44e16328154469011b8c1f8b0f

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Citations in the corpus

A Krylov–Schur approach to the truncated SVD (2012, methodology): “The SVD is an increasingly popular tool [6, 1] when analyzing gene expression data.”
Molecular ecological network analyses (2012, background): “, microbial data) from the complexity to the comprehensibility [99].”
Metabolomics technology and bioinformatics (2006, methodology): “This can be best achieved either through significance methods in ANOVA and t-tests, through linear combinations of variables in PCA or by using evolutionary algorithms such as genetic algorithms or genetic programming.”
Model-Based Clustering, Discriminant Analysis, and Density Estimation (2002, methodology): “…al. 1999; Tamayo et al. 1999), graphtheoretic methods (e.g., Ben-Dor, Shamir, and Yakhini 1999), techniques related to principal components (e.g., Alter, Brown, and Botstein 2000; Hastie et al. 2000b; Yeung and Ruzzo 2001), k-means (e.g., Herwig et al. 1999; Tavazoie, Hughes, Campbell, Cho, and…”
The challenges of in silico biology (2000, background): “This important principle of simplicity from complexity is emerging from singular value decomposition of gene expression data that clearly shows that many expressed gene products behave in a highly coordinated fashio”
Functional Adaptive Model Estimation (2005, methodology): “KEY WORDS: Functional predictor; Functional principal components; Generalized additive model; Generalized linear model; Projection pursuit regression.”
The Stanford Microarray Database (2001, methodology): “SMD will in future support k-means clustering (10), and the use of Singular Value Decomposition to find patterns in the microarray data (11).”
Singular value decomposition and principal component analysis (2003, methodology): “SVD and related methods are particularly valuable analysis methods when the distribution of genes is more complicated than the simple distributions in our example data: for instance, SVD has been used to characterize ring-like distributions of genes such as are observed in scatter plots of cell-cycl”
Cleaning large correlation matrices: tools from random matrix theory (2016, methodology): “Y, as the analogue of the Marcenko-Pastur equation (3.9). Moreover, an analysis of the left and right eigenvectors like in Chapter4would certainly be of interest in many real life problems (see e.g. [4,162,163,164] for standard applications). Note that the case of outlier singular values and vectors”
Proteomics: the first decade and beyond (2003, methodology): “Second, the more data the better: in comparison to interpretations of one or a few expression profiles, it has been much more informative to collect several global data sets on the same, differentially perturbed system, and to use mathematical tools such as cluster analysis or singular value decompo”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, neutral): “D atabases with time evolution of gene expression activity are being developed [364, 365].”
Revealing the vectors of cellular identity with single-cell genomics (2016, background): “While several studies have opted to remove these axes of variation entirely 27,66 , this may result in eliminating important aspects of the biological signal.”