Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Thioredoxin interacting protein (TXNIP), a redox regulator, mediates the RAPGEF3/4 signaling dependency in primary melanoma.

bioRxiv : the preprint server for biology·2026

Same author

SatTCR: a pipeline for performing saturation analysis of the T cell receptor repertoire and a case study of a healthy canine.

MethodsX·2025

Same author

Discovery and Targeted Proteomic Studies Reveal Striatal Markers Validated for Huntington's Disease.

Annals of clinical and translational neurology·2025

Same author

SURROGATE SELECTION OVERSAMPLES EXPANDED T CELL CLONOTYPES.

The annals of applied statistics·2025

Same author

Limited Biomarker Potential for IgG Autoantibodies Reactive to Linear Epitopes in Systemic Lupus Erythematosus or Spondyloarthropathy.

Antibodies (Basel, Switzerland)·2024

Same author

Intratumoral radiation dose heterogeneity augments antitumor immunity in mice and primes responses to checkpoint blockade.

Science translational medicine·2024

Same journal

Towards a Unified Theory for Semiparametric Data Fusion with Individual-Level Data.

Annals of statistics·2026

Same journal

One-Step Estimation of Differentiable Hilbert-Valued Parameters.

Annals of statistics·2026

Same journal

GENERALIZATION ERROR BOUNDS OF DYNAMIC TREATMENT REGIMES IN PENALIZED REGRESSION-BASED LEARNING.

Annals of statistics·2026

Same journal

EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.

Annals of statistics·2026

Same journal

TESTING HIGH-DIMENSIONAL REGRESSION COEFFICIENTS IN LINEAR MODELS.

Annals of statistics·2026

Same journal

COUNTERFACTUAL INFERENCE IN SEQUENTIAL EXPERIMENTS.

Annals of statistics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

GAMMA-BASED CLUSTERING VIA ORDERED MEANS WITH APPLICATION TO GENE-EXPRESSION ANALYSIS.

Michael A Newton¹, Lisa M Chung

¹Department of Statistics, University of Wisconsin, Madison, 1300 University Ave, Madison, Wisconsin 53706-1532, USA.

Annals of Statistics

|November 30, 2010

Summary

This summary is machine-generated.

This study introduces a novel discrete mixture model for gene-expression data clustering. The method efficiently handles constraints on latent means, improving computational properties and yielding promising empirical results.

More Related Videos

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Related Experiment Videos

Last Updated: Jun 6, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Area of Science:

Computational Biology
Statistical Genetics
Machine Learning

Background:

Discrete mixture models are foundational for clustering algorithms.
Technical challenges have previously limited their application scope.
Gene-expression data analysis requires robust clustering techniques.

Purpose of the Study:

To present a novel discrete mixture model for gene-expression data analysis.
To address limitations in existing mixture models for clustering.
To improve computational efficiency and analytical properties.

Main Methods:

The model mixes over a finite catalog of structures with equality/inequality constraints on latent expected values.
Computations leverage the probability of orderings in independent gamma-distributed variables.
A dynamic-programming calculation is guided by the equivalence to negative-binomial random variables.

Main Results:

The model's structure ensures strict concavity of the mixture log-likelihood, enhancing numerical stability.
The proposed clustering method demonstrates beneficial computational properties.
Empirical studies show promising results for gene-expression data clustering.

Conclusions:

The developed discrete mixture model offers an effective approach for gene-expression data clustering.
The method overcomes previous technical limitations by incorporating structural constraints.
The model provides a computationally efficient and numerically stable clustering solution.