Publication
Exploring the transcription factor activity in high-throughput gene expression data using RLQ analysis
Journal Paper/Review - Jun 6, 2013
Baty Florent, RĂ¼diger Jochen, Miglino Nicola, Kern Lukas, Borger Peter, Brutsche Martin
Units
PubMed
Doi
Citation
Type
Journal
Publication Date
Issn Electronic
Pages
Brief description/objective
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.