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

312
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...
312
Test for Homogeneity01:23

Test for Homogeneity

2.1K
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...
2.1K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.0K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.0K
Variability: Analysis01:11

Variability: Analysis

199
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
199
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.5K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.5K
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

774
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
774

You might also read

Related Articles

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

Sort by
Same author

Association of endothelial activation and stress index with all-cause and cardiovascular mortality in patients with diabetic kidney disease: a population-based prospective study.

Diabetology & metabolic syndrome·2026
Same author

Enhancing safety and flavor of traditional Yucha: Autochthonous starters from Hainan fermented foods reduce biogenic amines and promote taste-active metabolites.

Food chemistry·2026
Same author

Aequorea Victoria-Inspired Covalent Anchoring Enables Water-Resistant and Scalable Room-Temperature Phosphorescence in Cellulose.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

An Improved DQN Framework with Dual Residual Horizontal Feature Pyramid for Autonomous Fault Diagnosis in Strong-Noise Scenarios.

Sensors (Basel, Switzerland)·2025
Same author

Analysis of cross-platform health communication with a network approach.

Biometrics·2025
Same author

Integrated alkaline deep eutectic solvent pretreatment and high-solids enzymatic hydrolysis for high-titer sugars production from lignocellulose.

International journal of biological macromolecules·2025
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
Same journal

Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease.

Statistics in medicine·2026
Same journal

Rejoinder to Commentaries on "A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events".

Statistics in medicine·2026
Same journal

A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.

Statistics in medicine·2026
Same journal

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.

Statistics in medicine·2026
See all related articles
  1. Home
  3. Robust Structured Heterogeneity Analysis Approach For High-dimensional Data.
  1. Home
  3. Robust Structured Heterogeneity Analysis Approach For High-dimensional Data.

Related Experiment Video

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

18.8K

Robust structured heterogeneity analysis approach for high-dimensional data.

Yifan Sun1,2, Ziye Luo2, Xinyan Fan1,2

  • 1Center for Applied Statistics, Renmin University of China, Beijing, China.

Statistics in Medicine
|April 23, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a robust method to uncover hidden patient subgroups and identify disease-specific genes, improving our understanding of complex diseases and their genetic underpinnings.

Keywords:
high-dimensional dataoverlapping clustersrobustnesssubgroup identification

More Related Videos

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting
08:13

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting

Published on: April 9, 2019

14.3K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.4K

Related Experiment Videos

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

18.8K
Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting
08:13

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting

Published on: April 9, 2019

14.3K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.4K

Area of Science:

  • Biomedical Informatics
  • Genetics
  • Computational Biology

Background:

  • Disease heterogeneity poses challenges in identifying gene-phenotype relationships.
  • Existing methods struggle with data contamination and gene interconnections.

Purpose of the Study:

  • To develop a robust approach for subgroup discovery and subgroup-specific gene identification.
  • To address limitations of existing heterogeneity analysis methods.

Main Methods:

  • A robust structured heterogeneity analysis approach was developed.
  • Huber loss function to handle data contamination.
  • Sparse overlapping group lasso penalty for regularization and gene identification.

Main Results:

  • The proposed approach outperforms alternatives in revealing heterogeneity and selecting important genes.
  • Analysis of Cancer Cell Line Encyclopedia data yielded biologically meaningful findings.
  • Improved prediction accuracy and grouping stability were achieved.

    • Conclusions:

    • The developed method effectively identifies subgroups and subgroup-specific genes in heterogeneous diseases.
    • This approach enhances understanding of disease mechanisms and facilitates personalized medicine.
    • The method demonstrates robustness against data contamination and accounts for gene interdependencies.