Principal component analysis for clustering gene expression data

By K. Y. Yeung, W. L. Ruzzo. Bioinform., 2001

1,214 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cf256491a4d2560c248e0e80bc728ccb23ec9b8e

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Citations in the corpus

A Practical Approach to Microarray Data Analysis (2003, methodology): “Moreover, many analysis algorithms (including PCA and SVD) require complete matrices to function.”
Singular value decomposition and principal component analysis (2003, methodology): “There is a direct relation between PCA and SVD in the case where principal components are calculated from the covariance matrix 4 .”
Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads (2010, neutral): “We use a conventional similarity measure, the adjusted Rand index ARI defined by the following formula (Hubert and Arabie 1985, Yeung and Ruzzo 2001): ⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ ⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ −⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ +⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ ⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ ⎟⎟ ⎠ ⎞ ⎜⎜ ⎝…”
Variable Selection for Model-Based Clustering (2006, result): “Similar results have been found by other researchers, including Green and Krieger (1995) for market segmentation and Yeung and Ruzzo (2001) for clustering gene expression data.”
A method for generating realistic correlation matrices (2011, methodology): “ditional example, we consider the PAM algorithm [Kaufman and Rousseeuw (1990)] to cluster data which has known structure but diﬀerent levels of noise. We use the adjusted Rand statistic [Rand (1971), Yeung and Ruzzo (2001)] to measure the degree of concordance between the clustering output and the t”
Functional clustering and identifying substructures of longitudinal data (2007, background): “A comprehensive summary of aRand can be found in section 4.4.1 of Jain and Dubes (1988), and in section 2.1 of Yeung and Ruzzo (2001) and their supplementary material.”
Model-Based Clustering, Discriminant Analysis, and Density Estimation (2002, background): “…(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 Church 1999), network models (e.g., Michaels et al.…”
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering (2016, methodology): “ARI is a value within − 1 to 1 , with one being the best clustering performance and minus one the opposite.”
Learning structure in gene expression data using deep architectures, with an application to gene clustering (2015, methodology): “Another initial work is [15], they applied the standard PCA on gene expression data and studied the effectiveness of principal components in capturing cluster structure.”