Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Kruskal-Wallis Test01:19

Kruskal-Wallis Test

The Kruskal-Wallis test, also known as the Kruskal-Wallis H test, serves as a nonparametric alternative to the one-way ANOVA, offering a solution for analyzing the differences across three or more independent groups based on a single, ordinal-dependent variable. This statistical test is particularly valuable in scenarios where the data does not meet the normal distribution assumption required by its parametric counterparts. Kruskal-Wallis test is designed typically to handle ordinal data or...
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...

You might also read

Related Articles

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

Sort by
Same author

Cell Cycle Sensing Shapes Human T Cell Fate and Exhaustion Programs.

bioRxiv : the preprint server for biology·2026
Same author

Wavelet Decomposition-Based Genomic Analysis of the Human Electrocardiogram.

medRxiv : the preprint server for health sciences·2026
Same author

Structure-preserving multivariate hypothesis testing for mass spectrometry imaging and single-cell data.

Bioinformatics (Oxford, England)·2026
Same author

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition in metastatic TNBC.

Nature cancer·2026
Same author

Prognostic pan-cancer and single-cancer models: A large-scale analysis using a real-world clinico-genomic database.

PloS one·2026
Same author

Glaucoma Classification Through SSVEP-Derived ON- and OFF-Pathway Features.

Translational vision science & technology·2026

Related Experiment Video

Updated: Jul 9, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

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

Published on: February 15, 2017

Complementary hierarchical clustering.

Gen Nowak1, Robert Tibshirani

  • 1Department of Statistics, Stanford University, Stanford, CA 94305, USA. gnowak@stanford.edu

Biostatistics (Oxford, England)
|December 21, 2007
PubMed
Summary

Complementary hierarchical clustering identifies novel gene patterns in microarray data. This method uncovers patient subgroups with distinct distant metastasis-free probabilities, offering new insights beyond known prognostic signatures.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Hierarchical clustering of microarray data often prioritizes highly expressed genes, potentially masking relevant or novel biological signals.
  • Dominant gene expression patterns can obscure the influence of less-expressed but functionally significant genes in patient sample clustering.

Purpose of the Study:

  • To introduce complementary hierarchical clustering, a novel method designed to reveal structures driven by less-expressed, potentially novel genes.
  • To develop a 'relative gene importance' concept for identifying key genes within a clustering analysis.

Main Methods:

  • Developed complementary hierarchical clustering to address limitations of standard algorithms in microarray data analysis.
  • Defined and applied the concept of relative gene importance to assess gene influence.

More Related Videos

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Related Experiment Videos

Last Updated: Jul 9, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

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

Published on: February 15, 2017

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

  • Analyzed a breast cancer microarray dataset from 295 patients using correlation-based distance and complementary clustering.

    • Main Results:

    • Simulation studies demonstrated the effectiveness of complementary hierarchical clustering across various scenarios.
    • The analysis of breast cancer data revealed a patient subgroup distinct from those identified by known prognostic signatures.
    • This novel grouping showed significantly different distant metastasis-free probabilities.

    Conclusions:

    • Complementary hierarchical clustering is effective in uncovering hidden structures in microarray data, particularly those related to novel or less-expressed genes.
    • The method identified a biologically relevant patient stratification in breast cancer, independent of established prognostic markers.
    • This approach enhances the discovery of new biomarkers and therapeutic targets by focusing on underrepresented gene expression patterns.