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

Multiple Comparison Tests01:13

Multiple Comparison Tests

3.4K
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...
3.4K
Proteomics01:33

Proteomics

7.5K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.5K
DNA Microarrays02:34

DNA Microarrays

16.8K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
16.8K
Methods of Medium Optimization01:28

Methods of Medium Optimization

70
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
70

You might also read

Related Articles

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

Sort by
Same author

AI-guided analysis of human pancreatic islet sociology reveals distinct cell compositional changes in type 1 diabetes.

bioRxiv : the preprint server for biology·2026
Same author

Adaptive Fisher's method using weakly geometric grid for combining <i>p</i>-values with application to COVID-19 surveillance.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same author

Molecular Characterization of the Progressive Landscape of Depression.

bioRxiv : the preprint server for biology·2026
Same author

Re-thinking neuropeptide therapeutics: What Neuropeptide Y and Galanin teach us about stress, resilience, and drug design.

Neuropeptides·2026
Same author

The α5-Containing GABA<sub>A</sub> Receptor as a Target for Disorders of Altered Dendritic Inhibition.

Biological psychiatry·2026
Same author

Impact of sex chromosomes and gonad type in stress susceptibility in corticostriatal brain regions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 4, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K

Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an

Lun-Ching Chang, Hui-Min Lin, Etienne Sibille

  • 1Department of Biostatistics, Graduate school of Public Health, University of Pittsburgh, Pittsburgh, PA, USA. ctseng@pitt.edu.

BMC Bioinformatics
|December 24, 2013
PubMed
Summary
This summary is machine-generated.

This study compares 12 microarray meta-analysis methods for detecting candidate markers. It provides a practical guideline for choosing the best method based on statistical and biological considerations for improved genomic data integration.

More Related Videos

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

16.6K
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

3.8K

Related Experiment Videos

Last Updated: May 4, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

16.6K
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

3.8K

Area of Science:

  • Genomics
  • Bioinformatics
  • Biostatistics

Background:

  • High-throughput genomic technologies generate vast datasets, necessitating data integration and meta-analysis in biomedical research.
  • Microarray meta-analysis combines multiple studies to enhance candidate marker detection, but method performance is minimally understood.
  • Lack of clear guidelines complicates the selection of appropriate meta-analysis methods, requiring both statistical and biological insights.

Purpose of the Study:

  • To investigate and compare the performance of 12 different microarray meta-analysis methods.
  • To categorize methods based on their hypothesis-setting capabilities for detecting differentially expressed (DE) genes.
  • To provide a practical guideline for selecting suitable meta-analysis methods in real-world applications.

Main Methods:

  • Performed 12 microarray meta-analysis methods on simulated expression profiles.
  • Categorized methods into three hypothesis settings: HS(A) (all studies), HS(B) (one or more studies), and HS(r) (majority of studies).
  • Conducted comparative analysis on six real applications using criteria: detection capability, biological association, stability, and robustness; employed multi-dimensional scaling (MDS) and entropy measures.

Main Results:

  • Simulation study categorized the 12 methods into the three defined hypothesis settings (HS(A), HS(B), HS(r)).
  • Comprehensive evaluation on real data revealed method-specific performance characteristics.
  • MDS and entropy analyses provided insights into method behavior and data structure.

Conclusions:

  • The study successfully categorized microarray meta-analysis methods based on hypothesis settings.
  • Evaluation using real data and advanced analytical techniques offers a practical guideline for method selection.
  • Source files for simulations and real data are publicly available to facilitate further research.