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

Complementation Tests00:49

Complementation Tests

6.0K
A complementation test is a simple cross to identify whether the two mutations are located on the same gene or different genes. It was first performed by Edward Lewis in the 1940s while working on fruit flies. He developed the test to identify the location and arrangement of different mutations on chromosomes.
Organisms heterozygous for different mutations are crossed pairwise in all combinations. If present on different genes, the mutations can complement each other by providing the missing...
6.0K
Combinatorial Gene Control02:33

Combinatorial Gene Control

9.4K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
9.4K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.2K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.2K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.4K
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...
4.4K
Longitudinal Studies01:26

Longitudinal Studies

429
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
429
Test for Homogeneity01:23

Test for Homogeneity

2.3K
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...
2.3K

You might also read

Related Articles

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

Sort by
Same author

Physical function impacts hearing without mediation from systolic blood pressure.

Scientific reports·2026
Same author

Longitudinal multiomic and spatial transcriptomic profiling of lupus nephritis progression in a murine model.

Journal of immunology (Baltimore, Md. : 1950)·2026
Same author

The association of angiostatin and plasminogen plasma levels with Charlson comorbidity burden and mortality in COVID-19.

Journal of thrombosis and thrombolysis·2026
Same author

Integrating Deep Learning of Low-Dose CT Imaging With Clinical Data for Lung Cancer Risk Prediction.

Chest·2026
Same author

A novel system for micron-scale analysis of energy deposition and response to low-dose radiation.

Medical physics·2026
Same author

Convolutional Autoencoder for Automated Pre-Processing of Tumor Cell and Tissue Raman Spectra.

Applied spectroscopy·2026

Related Experiment Video

Updated: Jan 2, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K

Longitudinal linear combination test for gene set analysis.

Elham Khodayari Moez1, Morteza Hajihosseini1, Jeffrey L Andrews2

  • 1School of Public Health, University of Alberta, Edmonton, AB, Canada.

BMC Bioinformatics
|December 12, 2019
PubMed
Summary
This summary is machine-generated.

A new method called longitudinal linear combination test (LLCT) analyzes complex microarray data. LLCT effectively handles high-dimensional data and small sample sizes, improving replication in genetic studies.

More Related Videos

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
10:40

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

10.9K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.9K

Related Experiment Videos

Last Updated: Jan 2, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K
Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
10:40

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

10.9K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.9K

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray studies have advanced genetics but suffer from a lack of replication.
  • Complex study designs are underutilized due to inadequate analysis methods.
  • Analyzing complex microarray data is challenging due to data correlation, high dimensionality, and small sample sizes.

Purpose of the Study:

  • To develop a novel statistical method for analyzing repeatedly measured phenotypic or transcriptomic data.
  • To address the challenges in analyzing complex microarray study designs with high dimensionality and small sample sizes.

Main Methods:

  • Developed the longitudinal linear combination test (LLCT), a two-step method for analyzing multiple longitudinal phenotypes.
  • LLCT calculates within-subjects and between-subjects variations to assess the correlation between time trends and gene expression predictors.
  • The method is generalizable to family-based designs and applicable to time-course microarray data.

Main Results:

  • LLCT demonstrated superior performance compared to pathway analysis via regression in simulation studies.
  • The method is powerful for analyzing large gene sets, even with small sample sizes.
  • LLCT can identify gene sets with significantly different temporal expression patterns.

Conclusions:

  • LLCT is a versatile pathway analysis method for various longitudinal OMICS data (genomics, proteomics, metabolomics).
  • It accommodates time-dependent covariates and performs well with unbalanced and incomplete data.
  • LLCT offers a promising approach for time-course linkage of OMICS data in future genetic research.