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 Video

Updated: Jul 10, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Challenges in projecting clustering results across gene expression-profiling datasets.

Lara Lusa1, Lisa M McShane, James F Reid

  • 1Department of Experimental Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy. lara.lusa@ifom-ieo-campus.it

Journal of the National Cancer Institute
|November 15, 2007
PubMed
Summary

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

Neutrophil-to-lymphocyte ratio is associated with cognitive performance in isolated REM sleep behavior disorder.

Sleep medicine·2026
Same author

TNF inhibitors for the long-term management of juvenile idiopathic arthritis associated uveitis: real-life data from the ITHACA cohort.

Expert opinion on biological therapy·2026
Same author

Can we use MRI for timing estimation of rotator cuff tears?

La Radiologia medica·2026
Same author

Pain prevalence and severity in an Italian university hospital: a cross-sectional study.

Annali dell'Istituto superiore di sanita·2026
Same author

Application of integrated nested Laplace approximation to identify hot spots of methylation heterogeneity in healthy individuals from the MAMELI cohort.

Frontiers in genetics·2026
Same author

Critical appraisal and temporal validation of the Niguarda scoring model: a CT-based diagnostic score for detecting hollow viscus and mesenteric injury in blunt abdominal trauma.

Updates in surgery·2026

Mean centering gene expression data aids breast cancer subtype classification but can lead to misclassification, especially for estrogen receptor-positive samples. Careful consideration of patient population comparability is crucial for accurate subtype assignment.

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Gene expression microarray studies have identified distinct cancer subtypes.
  • A five-subtype molecular classification for breast cancer exists, showing clinical outcome associations.
  • The method of centroids classifies new samples based on similarity to established subtype profiles.

Purpose of the Study:

  • To assess the impact of mean centering gene expression data on breast cancer subtype classification using the method of centroids.
  • To investigate the influence of mean centering and estrogen receptor-positive (ER+) sample prevalence on ER status classification accuracy.

Main Methods:

  • Utilized previously identified centroids to assign 99 breast cancer samples (65 ER+) to five subtypes via microarray data.

More Related Videos

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: Jul 10, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

  • Evaluated the effect of gene mean centering on subtype assignment accuracy.
  • Conducted further studies varying ER+ sample proportion in test sets to assess mean centering's impact on ER status classification.

    • Main Results:

    • Mean centering improved subtype assignment for all 99 samples, aligning with known subtype marker gene expression.
    • Classification of ER+ samples alone revealed misclassifications and unexpected subtype distributions.
    • Gene mean centering for ER status classification showed high dependence on ER+ sample prevalence, an effect absent without mean centering.

    Conclusions:

    • Simple data corrections like gene mean centering can introduce biases, conflating assay effects with patient population differences.
    • Comparability of patient populations must be carefully considered before applying data normalization techniques for subtype assignment.