Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer.
Bioinformatics
; 22(19): 2396-404, 2006 Oct 01.
Article
in En
| MEDLINE
| ID: mdl-16864591
ABSTRACT
MOTIVATION Biological differences between classes are reflected in transcriptional changes which in turn affect the levels by which essential genes are individually expressed and collectively connected. The purpose of this communication is to introduce an analytical procedure to simultaneously identify genes that are differentially expressed (DE) as well as differentially connected (DC) in two or more classes of interest. RESULTS:
Our procedure is based on a two-stepapproach:
First, mixed-model equations are applied to obtain the normalized expression levels of each gene in each class treatment. These normalized expressions form the basis to compute a measure of (possible) DE as well as the correlation structure existing among genes. Second, a two-component mixture of bi-variate distributions is fitted to identify the component that encapsulates those genes that are DE and/or DC. We demonstrate our approach using three distinct datasets including a human systemic inflammation oligonucleotide data; a spotted cDNA data dealing with bovine in vitro adipogenesis and SAGE database on cancerous and normal tissue samples.
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Collection:
01-internacional
Database:
MEDLINE
Main subject:
Proteins
/
Multigene Family
/
Oligonucleotide Array Sequence Analysis
/
Gene Expression Profiling
/
Adipogenesis
/
Inflammation
/
Neoplasms
Type of study:
Diagnostic_studies
/
Risk_factors_studies
Limits:
Animals
/
Humans
Language:
En
Journal:
Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2006
Type:
Article
Affiliation country:
Australia