Validating clustering for gene expression data

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

746 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4a53d64a6d505d991e83c4a1a514b95836d546a4

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Citations in the corpus

Critical limitations of consensus clustering in class discovery (2014, neutral): “that the [6, 11, 16, 21, 26, 31, 36, 41, 46] ranked datasets were chosen.”
Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering (2021, methodology): “Silhouette Index There are several methods to evaluate clustering results, such as the Rand index [13], adjusted Rand index [14], distortion score [11], and Silhouette index.”
Judging the quality of gene expression-based clustering methods using gene annotation. (2002, background): “…there have been some recent attempts to develop data-centric figures of merit that are specific to gene expression data (Lukashin and Fuchs 2001; Yeung et al. 2001; Ben-Hur et al. 2002), generic (Fraley and Raftery 1998), or use information beyond the expression data itself (Jakt et al. 2001),…”
Computational cluster validation in post-genomic data analysis (2005, methodology): “The ﬁrst of these, the ﬁgure of merit (Yeung et al ., 2001a), is motivated by the jack-nife (Efron and Tibshirani, 1993) approach.”
From patterns to pathways: gene expression data analysis comes of age (2002, background): “If clusters are to be used to predict classiﬁcations of other samples, one might choose to evaluate each clustering by its predictive power 50 .”
Transcriptional Profiling of the Human Monocyte-to-Macrophage Differentiation and Polarization: New Molecules and Patterns of Gene Expression1 (2006, methodology): “After removal of redundant genes, an expression matrix (3530 15) was obtained, and figures-of-merit analysis was applied to define the optimal number of clusters (24).”
CLICK and EXPANDER: a system for clustering and visualizing gene expression data (2003, methodology): “Several heuristic procedures are then used to expand the kernels into the full clusters.”
Comparisons and validation of statistical clustering techniques for microarray gene expression data (2003, background): “Yeung et al. (2001) introduces the concept of Figure of Merit (FOM) in selecting between competing clustering algorithms.”
FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data (2007, methodology): “The clustering performance was initially assessed using three different Figures Of Merit (FOM)[22,23]: 1-Norm FOM, 2-Norm FOM and Range FOM (a short description of FOMs and their properties is provided in Methods) .”
Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments (2009, positive): “Among the recently developed methods for assessing clustering reliability [34-38], we adopt a general bootstrap framework proposed by Kerr and Churchill [36], in which the clustering procedure is first applied to the original data and then to a large number of bootstrap samples obtained from perturb”
Cluster Validation by Prediction Strength (2005, methodology): “Yeung, Haynor, and Ruzzo (2001) provided a framework for comparing different clustering algorithms for gene expression data.”
9) TM4 Microarray Software Suite (2006, background): “Other algorithms in this category are figures of merit (Yeung et al., 2001), which iteratively step through different values of k searching for an optimal value based on a comparison of within‐cluster and between‐cluster distances, and K‐means support, which iterates K‐means at a fixed value of k se”
Stability-Based Validation of Clustering Solutions (2004, methodology): “Although not explicitly formulated in a probabilistic way, the general validation methodology proposed in Yeung, Haynor, and Ruzzo (2001) as well as the Gap Statistic (Tibshirani, Walther, & Hastie, 2001; see section 3) are model dependent, since both methods assume compact clusters tightly packed…”
Genomic Analysis of Mouse Retinal Development (2004, methodology): “Discontinuities in the rate of change were taken to indicate that a meaningful cluster number had been obtained, with the lowest number of clusters that showed such a discontinuity being used for analysis (Hartigan 1975; Yeung et al. 2001).”
Analysis of matched mRNA measurements from two different microarray technologies (2002, methodology): “0 10 20 30 40 50 60 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 (a) 1 number of clusters Jaccard all wm variance 0 10 20 30 40 50 60 –0.2 0 0.2 0.4 0.6 0.8 1 (b) 1.2 number of clusters Kappa all wm variance 0 50 100 150 200 250 300 350 400 450 500 –0.2 0 0.2 0.4 0.6 0.8 1 (c) 1.2 number of clusters coeffi”
Survey of clustering algorithms (2005, background): “Some recent results can be accessed in [29], [247], and [291].”