ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
Statistical Significance of Clustering using Soft Thresholding.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
23 citing papers in PubMed.
- Statistical significance of clustering for count data.Biometrics · 2025Article
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- Powerful significance testing for unbalanced clusters.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Technical Optimization of SyntheticMR for the Head and Neck on a 3T MR-Simulator and 1.5T MR-Linac: A Prospective R-IDEAL Stage 2a Technology Innovation Report.medRxiv : the preprint server for health sciences · 2025Article
- Multi-omics insights into the molecular signature and prognosis of hypopharyngeal squamous cell carcinoma.Communications biology · 2025Article
- Beyond the Spectrum: Subtype-Specific Molecular Insights into Autism Spectrum Disorder Via Multimodal Data Integration.medRxiv : the preprint server for health sciences · 2024Article
- Statistical Significance of Clustering with Multidimensional Scaling.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Article
- RNA-Based Classification of Homologous Recombination Deficiency in Racially Diverse Patients with Breast Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Article
- Article
- Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021Article
- MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data.Genome biology · 2021Article
- Dysregulated BMP2 in the Placenta May Contribute to Early-Onset Preeclampsia by Regulating Human Trophoblast Expression of Extracellular Matrix and Adhesion Molecules.Frontiers in cell and developmental biology · 2021Article
- Multi-Omics Data Fusion for Cancer Molecular Subtyping Using Sparse Canonical Correlation Analysis.Frontiers in genetics · 2021Article
- Identification of Immune Subtypes of Lung Squamous Cell Carcinoma by Integrative Genome-Scale Analysis.Frontiers in oncology · 2021Article
- Molecular profiling predicts meningioma recurrence and reveals loss of DREAM complex repression in aggressive tumors.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Multi-omics Analysis of Microenvironment Characteristics and Immune Escape Mechanisms of Hepatocellular Carcinoma.Frontiers in oncology · 2019Article
- diceR: an R package for class discovery using an ensemble driven approach.BMC bioinformatics · 2018Article
- Statistical significance for hierarchical clustering.Biometrics · 2017Article
- Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma.Cancer cell · 2017Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Clustering methods have led to a number of important discoveries in bioinformatics and beyond. A major challenge in their use is determining which clusters represent important underlying structure, as opposed to spurious sampling artifacts. This challenge is especially serious, and very few methods are available, when the data are very high in dimension. Statistical Significance of Clustering (SigClust) is a recently developed cluster evaluation tool for high dimensional low sample size data. An important component of the SigClust approach is the very definition of a single cluster as a subset of data sampled from a multivariate Gaussian distribution. The implementation of SigClust requires the estimation of the eigenvalues of the covariance matrix for the null multivariate Gaussian distribution. We show that the original eigenvalue estimation can lead to a test that suffers from severe inflation of type-I error, in the important case where there are a few very large eigenvalues. This paper addresses this critical challenge using a novel likelihood based soft thresholding approach to estimate these eigenvalues, which leads to a much improved SigClust. Major improvements in SigClust performance are shown by both mathematical analysis, based on the new notion of Theoretical Cluster Index, and extensive simulation studies. Applications to some cancer genomic data further demonstrate the usefulness of these improvements.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.