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Open Access Methodology article

Exploring the transcription factor activity in high-throughput gene expression data using RLQ analysis

Florent Baty1*, Jochen Rüdiger1, Nicola Miglino2, Lukas Kern3, Peter Borger2 and Martin Brutsche1

Author Affiliations

1 Division of Pulmonary Medicine, Cantonal Hospital St. Gallen, Rorschacherstrasse 95, CH-9007 St. Gallen, Switzerland

2 Department of Biomedicine, University Hospital Basel, Petersgraben 4, CH-4001 Basel, Switzerland

3 Pulmonary Medicine, Cantonal Hospital Zug, Landhausstrasse 11, CH-6340 Baar, Switzerland

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BMC Bioinformatics 2013, 14:178  doi:10.1186/1471-2105-14-178

Published: 6 June 2013

Abstract

Background

Interpretation of gene expression microarray data in the light of external information on both columns and rows (experimental variables and gene annotations) facilitates the extraction of pertinent information hidden in these complex data. Biologists classically interpret genes of interest after retrieving functional information from a subset of genes of interest. Transcription factors play an important role in orchestrating the regulation of gene expression. Their activity can be deduced by examining the presence of putative transcription factors binding sites in the gene promoter regions.

Results

In this paper we present the multivariate statistical method RLQ which aims to analyze microarray data where additional information is available on both genes and samples. As an illustrative example, we applied RLQ methodology to analyze transcription factor activity associated with the time-course effect of steroids on the growth of primary human lung fibroblasts. RLQ could successfully predict transcription factor activity, and could integrate various other sources of external information in the main frame of the analysis. The approach was validated by means of alternative statistical methods and biological validation.

Conclusions

RLQ provides an efficient way of extracting and visualizing structures present in a gene expression dataset by directly modeling the link between experimental variables and gene annotations.