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

Bonferroni Test01:10

Bonferroni Test

3.4K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
3.4K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.5K
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...
4.5K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.8K
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:
6.8K
McNemar's Test01:23

McNemar's Test

883
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
883
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.7K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.7K
Test for Homogeneity01:23

Test for Homogeneity

2.4K
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...
2.4K

You might also read

Related Articles

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

Sort by
Same author

[Distribution trends and characteristics analysis of non-motor road traffic injury cases monitored in China, 2006-2013].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2015
Same author

[Analysis on sports and recreation related injuries through data from the Chinese National Injury Surveillance System, 2009-2013].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2015
Same author

[Study on head injuries through data from the National Injury Surveillance System of China, 2013].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2015
Same author

A novel subset of B7-H3<sup>+</sup>CD14<sup>+</sup>HLA-DR<sup>-/low</sup> myeloid-derived suppressor cells are associated with progression of human NSCLC.

Oncoimmunology·2015
Same author

Repair of urethral defects with polylactid acid fibrous membrane seeded with adipose-derived stem cells in a rabbit model.

Connective tissue research·2015
Same author

Percent free prostate-specific antigen is effective to predict prostate biopsy outcome in Chinese men with prostate-specific antigen between 10.1 and 20.0 ng ml(-1).

Asian journal of andrology·2015
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Feb 19, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

6.1K

Construction of null statistics in permutation-based multiple testing for multi-factorial microarray experiments.

Xin Gao1

  • 1Department of Mathematics and Statistics, York University 4700 Keele Street, Toronto, ON M3J 1P3, Canada. xingao@mathstat.yorku.ca

Bioinformatics (Oxford, England)
|April 1, 2006
PubMed
Summary
This summary is machine-generated.

A novel null statistic improves statistical significance testing in microarray experiments. This method offers better control of false discovery rates and increased power for detecting differentially expressed genes compared to standard permutation tests.

More Related Videos

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.7K
Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

19.0K

Related Experiment Videos

Last Updated: Feb 19, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

6.1K
Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.7K
Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

19.0K

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Genomics

Background:

  • The F-test is common for analyzing factorial microarray experiments, but its normality assumption fails with limited data.
  • Permutation methods are used when normality assumptions are violated, but their accuracy can be limited.
  • The F-statistic distribution in microarrays is a mixture, complicating accurate null distribution approximation.

Purpose of the Study:

  • To develop a more accurate method for assessing statistical significance in microarray experiments.
  • To improve the approximation of the true null distribution for F-statistics.
  • To enhance multiple testing procedures in the analysis of gene expression data.

Main Methods:

  • Extended null statistic construction from pairwise differences to multifactorial designs.
  • Proposed a subpartition-based null statistic for approximating F-statistic null distribution.
  • Addressed unbalance in experimental design and corrected for numerator-denominator correlation.

Main Results:

  • The proposed null statistic better approximates the null distribution of the F-statistic.
  • Demonstrated improved control of false discovery rates (FDR) compared to standard permutation methods.
  • Showcased higher statistical power in detecting differentially expressed genes.

Conclusions:

  • The proposed null statistic offers a more robust approach for permutation-based multiple testing in microarray analysis.
  • This method enhances the reliability of identifying differentially expressed genes.
  • The approach is suitable for both balanced and unbalanced multifactorial microarray designs.