Determination of strongly overlapping signaling activity from microarray data

Ghislain Bidaut, Karsten Suhre, Jean Michel Claverie, Michael F. Ochs

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Background: As numerous diseases involve errors in signal transduction, modern therapeutics, often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups. Results: Here we introduce a method relying on gene annotation coupled to decompositional analysis of global gene expression data that allows us to estimate specific activity on strongly coupled signaling pathways and, in some cases, activity of specific signaling proteins. We demonstrate the technique using the Rosetta yeast deletion mutant data set, decompositional analysis by Bayesian Decomposition, and annotation analysis using ClutrFree. We determined from measurements of gene persistence in patterns across multiple potential dimensionalities that 15 basis vectors provides the correct dimensionality for interpreting the data. Using gene ontology and data on gene regulation in the Saccharomyces Genome Database, we identified the transcriptional signatures of several cellular processes in yeast, including cell wall creation, ribosomal disruption, chemical blocking of protein synthesis, and, criticially, individual signatures of the strongly coupled mating and filamentation pathways. Conclusion: This works demonstrates that microarray data can provide downstream indicators of pathway activity either through use of gene ontology or transcription factor databases. This can be used to investigate the specificity and success of targeted therapeutics as well as to elucidate signaling activity in normal and disease processes.

Original languageEnglish
Article number99
JournalBMC Bioinformatics
Volume7
DOIs
Publication statusPublished - 28 Feb 2006
Externally publishedYes

Fingerprint

Microarrays
Microarray Data
Gene Ontology
Overlapping
Genes
Signaling Pathways
Pathway
Gene
Microarray
Yeast
Yeasts
Ontology
Dimensionality
Databases
Annotation
Molecular Sequence Annotation
Signature
Saccharomyces
Proteins
Bayes Theorem

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Determination of strongly overlapping signaling activity from microarray data. / Bidaut, Ghislain; Suhre, Karsten; Claverie, Jean Michel; Ochs, Michael F.

In: BMC Bioinformatics, Vol. 7, 99, 28.02.2006.

Research output: Contribution to journalArticle

Bidaut, Ghislain ; Suhre, Karsten ; Claverie, Jean Michel ; Ochs, Michael F. / Determination of strongly overlapping signaling activity from microarray data. In: BMC Bioinformatics. 2006 ; Vol. 7.
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