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 Experiment Videos

Multivariate search for differentially expressed gene combinations.

Yuanhui Xiao1, Robert Frisina, Alexander Gordon

  • 1Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA. yxiao@bst.rochester.edu <yxiao@bst.rochester.edu>

BMC Bioinformatics
|October 28, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Enhancing antioxidant capacity and modulating sensory traits by nano‑selenium foliar biofortification on field Leek moss.

Food chemistry. Molecular sciences·2026
Same author

Molecular press annealing enables robust perovskite solar cells.

Science (New York, N.Y.)·2026
Same author

Identification and Functional Analysis of Candidate Genes Influencing Citrus Leaf Size Through Transcriptome and Coexpression Network Approaches.

Genes·2025
Same author

Cognitive function in long-term testicular cancer survivors: impact of modifiable factors.

JNCI cancer spectrum·2024
Same author

Switching anti-CGRP monoclonal antibodies in chronic migraine: real-world observations of erenumab, fremanezumab and galcanezumab.

European journal of hospital pharmacy : science and practice·2024
Same author

Observing π-Au Interaction between Aromatic Molecules and Single Au Nanodimers with a Subnanometer Gap by SERS.

Analytical chemistry·2023
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
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

This study introduces a new algorithm to find combinations of differentially expressed genes, moving beyond single-gene analysis. This method enhances the identification of complex gene expression patterns in biological data.

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Standard gene expression analysis uses univariate tests, ignoring multidimensional data structures.
  • A more comprehensive approach considers the joint distribution of gene expression signals.

Purpose of the Study:

  • To develop a novel algorithm for identifying combinations of differentially expressed genes.
  • To improve upon existing multivariate statistical methods for gene expression analysis.

Main Methods:

  • An improved random search algorithm generates candidate gene combinations.
  • Cross-validation ensures the stability of the search procedure.
  • Permutation tests and a family-wise error rate (FWER) control procedure are used for significance testing.

Related Experiment Videos

Main Results:

  • The algorithm successfully identifies significant combinations of differentially expressed genes.
  • The method demonstrates replication stability through cross-validation.

Conclusions:

  • A new algorithm effectively identifies differentially expressed gene combinations.
  • The procedure was validated through simulations and real-world gene array data analysis.