Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data

By Yufeng Liu, D. Hayes, Andrew B. Nobel, James Stephen Marron. 2008

320 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3b0a39ad28db9c0e434868cf0ed4f438ea637f8c

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Citations in the corpus

Multi-stage differentiation defines melanoma subtypes with differential vulnerability to drug-induced iron-dependent oxidative stress (2018, methodology): “1) (Liu et al., 2008) https://cran.r-project.org/web/packages/sigclust/index.html samr R Package (v2.”
Supervised risk predictor of breast cancer based on intrinsic subtypes. (2009, methodology): “A total of 189 breast tumors across 1,906 “intrinsic” genes were analyzed by hierarchical clustering (median centered by feature/gene, Pearson correlation, average linkage),(19) and the sample dendrogram was analyzed using “SigClust”.(20) A total of 122 breast cancers from 189 individuals profiled b”
Characterization of a naturally occurring breast cancer subset enriched in epithelial-to-mesenchymal transition and stem cell characteristics. (2009, methodology): “To assess the significance of this clustering pattern, we applied ‘‘SigClust’’ to test the null hypothesis that any group of samples contained within a common dendrogram branch constitutes a single group (38).”
Comprehensive molecular characterization of pheochromocytoma and paraganglioma (2017, methodology): “To derive a molecular classification for PCCs/PGLs, we performed unsupervised consensus clustering of tumor mRNA expression profiles (Wilkerson and Hayes, 2010), detecting four statistically significant expression subtypes (SigClust [Liu et al., 2008] p < 0.001; Figures S4A and S4B).”
An integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR and NF1 (2010, methodology): “Cluster significance was evaluated using SigClust (Liu et al., 2008), and all class boundaries were statistically significant (Figure 1C).”
Reduced rank stochastic regression with a sparse singular value decomposition (2012, background): “Recently, as high dimensional data become increasingly common, another approach of dimension reduction, through utilizing sparsity-inducing regularization techniques under a multivariate regression framework, has emerged (Turlach et al., 2005; Obozinski et al., 2008; Peng et al., 2010).”
A Sparse Singular Value Decomposition Method for High-Dimensional Data (2014, methodology): “The cancer data used by Lee et al. (2010) (who in turn have them from Liu et al. 2008) consist of the gene expression levels of 12,625 genes for 56 cases of four different types of cancer.”
PCA CONSISTENCY IN HIGH DIMENSION, LOW SAMPLE SIZE CONTEXT (2009, background): “[15], are from a microarray study of lung cancer.”