ArticleBiometrics2017
Statistical significance for hierarchical clustering.
Article in Biometrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
85 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of illness representation clusters in chronic conditions.Research in nursing & health · 2020Pooled it
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- HR-pQCT skeletal phenotype clustering associated with muscle function in older men and women: The Study of Muscle, Mobility and Aging (SOMMA).Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2026Article
- Cell type-specific barcoding reveals the single-neuron projectional architecture of the mouse midbrain dopaminergic system.bioRxiv : the preprint server for biology · 2026Article
- Mapping epigenetic gene variant dynamics: comparative analysis of frequency, functional impact and trait associations in African and European populations.Scientific reports · 2026Article
- Electrophysiological classification of human layer 2-3 pyramidal neurons reveals subtype-specific synaptic interactions.Nature neuroscience · 2026Article
- Succession of the yellow tang (PeerJ · 2026Article
- A gene-expression signature defines a subtype of Stomach Adenocarcinomas with low levels of Claudins and a high ratio of NF-YA long/NF-YA short splicing variants.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- Evaluation of the Unintended Effects ofBiomolecules · 2025Article
- Evolution of the Metazoan Protein Domain Repertoire Revealed by a Birth-Death-Gain Model.Journal of molecular evolution · 2025Article
- Clinical phenotypes among patients that underwent cardiac resynchronization therapy using unsupervised learning integrating gated SPECT.Nuclear medicine communications · 2025Article
- Rheumatoid arthritis continues to increase in low-middle SDI and low SDI quintiles based on GBD 1990-2021.BMC rheumatology · 2025Article
- Integrated Omics-Based Discovery of Bioactive Halogenated Metabolites from the Deep-SeaMarine drugs · 2025Article
- Multi-omics profiling of metastatic colorectal cancer reveals the transcriptional network of focal adhesion and immune suppression and the role of p-RPS6.Cancer cell international · 2025Article
- Proteomic Analysis of Asthma Airway Inflammation after Allergen Challenge: A Heterogeneous Response.American journal of respiratory and critical care medicine · 2025Article
- Novel endoplasmic reticulum stress-related gene signature unveils CDKN3 as a prognosticator in neuroblastoma.Translational pediatrics · 2025Article
- Statistical significance of clustering for count data.Biometrics · 2025Article
- 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
- No Evidence for Distinct Transcriptomic Subgroups of Devil Facial Tumor Disease (DFTD).Evolutionary applications · 2025Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high-dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure. A critical and challenging question in cluster analysis is whether the identified clusters represent important underlying structure or are artifacts of natural sampling variation. Few approaches have been proposed for addressing this problem in the context of hierarchical clustering, for which the problem is further complicated by the natural tree structure of the partition, and the multiplicity of tests required to parse the layers of nested clusters. In this article, we propose a Monte Carlo based approach for testing statistical significance in hierarchical clustering which addresses these issues. The approach is implemented as a sequential testing procedure guaranteeing control of the family-wise error rate. Theoretical justification is provided for our approach, and its power to detect true clustering structure is illustrated through several simulation studies and applications to two cancer gene expression datasets.
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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.