BioSpace Collaborative

Academic/Biomedical Research
News & Jobs
Biotechnology and Pharmaceutical Channel Medical Device and Diagnostics Channel Clinical Research Channel BioSpace Collaborative    Job Seekers:  Register | Login          Employers:  Register | Login  

Free Newsletters
My Subscriptions

News by Subject
News by Disease
News by Date
Search News
Post Your News

Job Seeker Login
Most Recent Jobs
Search Jobs
Post Resume
Career Fairs
Career Resources
For Employers

Regional News
US & Canada
  Biotech Bay
  Biotech Beach
  Pharm Country
  Bio NC
  Southern Pharm
  BioCanada East
  C2C Services & Suppliers™


Company Profiles

Research Store

Research Events
Post an Event
Real Estate
Business Opportunities

PLoS By Category | Recent PLoS Articles
Immunology - Rheumatology

Importance of Correlation between Gene Expression Levels: Application to the Type I Interferon Signature in Rheumatoid Arthritis
Published: Monday, October 17, 2011
Author: Frédéric Reynier et al.

by Frédéric Reynier, Fabien Petit, Malick Paye, Fanny Turrel-Davin, Pierre-Emmanuel Imbert, Arnaud Hot, Bruno Mougin, Pierre Miossec


The analysis of gene expression data shows that many genes display similarity in their expression profiles suggesting some co-regulation. Here, we investigated the co-expression patterns in gene expression data and proposed a correlation-based research method to stratify individuals.

Methodology/Principal Findings

Using blood from rheumatoid arthritis (RA) patients, we investigated the gene expression profiles from whole blood using Affymetrix microarray technology. Co-expressed genes were analyzed by a biclustering method, followed by gene ontology analysis of the relevant biclusters. Taking the type I interferon (IFN) pathway as an example, a classification algorithm was developed from the 102 RA patients and extended to 10 systemic lupus erythematosus (SLE) patients and 100 healthy volunteers to further characterize individuals. We developed a correlation-based algorithm referred to as Classification Algorithm Based on a Biological Signature (CABS), an alternative to other approaches focused specifically on the expression levels. This algorithm applied to the expression of 35 IFN-related genes showed that the IFN signature presented a heterogeneous expression between RA, SLE and healthy controls which could reflect the level of global IFN signature activation. Moreover, the monitoring of the IFN-related genes during the anti-TNF treatment identified changes in type I IFN gene activity induced in RA patients.


In conclusion, we have proposed an original method to analyze genes sharing an expression pattern and a biological function showing that the activation levels of a biological signature could be characterized by its overall state of correlation.