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

Cancer Survival Analysis01:21

Cancer Survival Analysis

484
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
484
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

361
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...
361
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.1K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.1K
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
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

272
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
272
Fisher's Exact Test01:08

Fisher's Exact Test

899
Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of...
899

You might also read

Related Articles

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

Sort by
Same author

Homogeneity test of several covariance matrices with high-dimensional data.

Journal of biopharmaceutical statistics·2021
Same author

Glycan Profiles of gp120 Protein Vaccines from Four Major HIV-1 Subtypes Produced from Different Host Cell Lines under Non-GMP or GMP Conditions.

Journal of virology·2020
Same author

Effects of a high-fat diet on intracellular calcium (Ca2+) handling and cardiac remodeling in Wistar rats without hyperlipidemia.

Ultrastructural pathology·2020
Same author

A strategy for iron oxide nanoparticles to adhere to the neuronal membrane in the substantia nigra of mice.

Journal of materials chemistry. B·2020
Same author

Ultrasound/Optical Dual-Modality Imaging for Evaluation of Vulnerable Atherosclerotic Plaques with Osteopontin Targeted Nanoparticles.

Macromolecular bioscience·2019
Same author

MicroRNA-23a suppresses the apoptosis of inflammatory macrophages and foam cells in atherogenesis by targeting HSP90.

Gene·2019

Related Experiment Video

Updated: Oct 29, 2025

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

7.7K

High-dimensional covariance matrices tests for analyzing multi-tumor gene expression data.

Abdullah Qayed1, Dong Han1

  • 1School of Mathematical Sciences, Department of Statistics, Shanghai Jiao Tong University, Shanghai, China.

Statistical Methods in Medical Research
|July 7, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces new statistical tests to analyze variations within subjects in gene expression data, crucial for understanding complex diseases like cancer. These methods improve the characterization of intra-subject variability in gene sets analysis.

Keywords:
Gene-sets analysishigh-dimensional dataintra-subject (tumor) variationmulti-identity testmulti-sphericity test

More Related Videos

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
09:01

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

523
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K

Related Experiment Videos

Last Updated: Oct 29, 2025

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

7.7K
Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
09:01

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

523
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Gene set analysis in microarray data often requires characterizing intra-subject variation.
  • Understanding intra-subject (e.g., tumor type) variation is crucial for accurate gene expression profiling.
  • Existing methods may not fully capture the complexities of multi-set data within subjects.

Purpose of the Study:

  • To develop and validate statistical tests for assessing intra-subject variation in gene expression data.
  • To test the assumption of intra-subject variation across different sets (e.g., tumor types) within subjects.
  • To evaluate the properties of these tests in both theoretical and empirical settings.

Main Methods:

  • Development of multi-set sphericity tests.
  • Development of multi-set identity of covariance structure tests.
  • Application and validation using The Cancer Genome Atlas (TCGA) data.

Main Results:

  • The proposed tests for multi-set sphericity and covariance structure identity demonstrate good statistical properties.
  • Theoretical and empirical studies confirm the reliability of the developed tests.
  • Analysis of TCGA data provided insights into covariance structures of gene expression across tumor types.

Conclusions:

  • The novel statistical tests effectively characterize intra-subject variation in gene expression data.
  • These methods enhance the analysis of multi-set microarray data, particularly in cancer genomics.
  • The findings contribute to a better understanding of gene expression patterns within and across different tumor types.