ArticleBMC bioinformatics2013
Exploring the transcription factor activity in high-throughput gene expression data using RLQ analysis.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Diversity, functional traits and assembly processes of diatom community in the aquatic-terrestrial ecotone: a case study of Danjiangkou Reservoir, China.Frontiers in microbiology · 2025Article
- covRNA: discovering covariate associations in large-scale gene expression data.BMC research notes · 2020Article
- Potential role of the HOXD8 transcription factor in cisplatin resistance and tumour metastasis in advanced epithelial ovarian cancer.Scientific reports · 2018Article
- A high performance prediction of HPV genotypes by Chaos game representation and singular value decomposition.BMC bioinformatics · 2015Article
- Transcriptome-wide signatures of tumor stage in kidney renal clear cell carcinoma: connecting copy number variation, methylation and transcription factor activity.Genome medicine · 2014Article
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6 authors.
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Abstract
backgroundInterpretation 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.
resultsIn 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.
conclusionsRLQ 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.
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