ArticleBMC bioinformatics2008
Identifying differential correlation in gene/pathway combinations.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases.Briefings in bioinformatics · 2024Article
- PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs.Briefings in bioinformatics · 2023Article
- Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions.bioRxiv : the preprint server for biology · 2023Article
- Identification of pathways associated with chemosensitivity through network embedding.PLoS computational biology · 2019Article
- A descriptive marker gene approach to single-cell pseudotime inference.Bioinformatics (Oxford, England) · 2019Article
- Integrative analysis reveals disrupted pathways regulated by microRNAs in cancer.Nucleic acids research · 2018Article
- Metrics to estimate differential co-expression networks.BioData mining · 2017Article
- MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways.PLoS computational biology · 2016Article
- Tilting the lasso by knowledge-based post-processing.BMC bioinformatics · 2016Article
- Gene Expression Architecture of Mouse Dorsal and Tail Skin Reveals Functional Differences in Inflammation and Cancer.Cell reports · 2016Article
- Systems analysis of high-throughput data.Advances in experimental medicine and biology · 2014Review
- Met kinetic signature derived from the response to HGF/SF in a cellular model predicts breast cancer patient survival.PloS one · 2012Article
- MODELING DEPENDENT GENE EXPRESSION.Annals of statistics · 2012Article
- A hypothesis test for equality of bayesian network models.EURASIP journal on bioinformatics & systems biology · 2010Article
- Age, estrogen, and immune response in breast adenocarcinoma and adjacent normal tissue.OncoimmunologyArticle
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Authors and funding
3 authors.
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Abstract
backgroundAn important emerging trend in the analysis of microarray data is to incorporate known pathway information a priori. Expression level "summaries" for pathways, obtained from the expression data for the genes constituting the pathway, permit the inclusion of pathway information, reduce the high dimensionality of microarray data, and have the power to elucidate gene-interaction dependencies which are not already accounted for through known pathway identification.
resultsWe present a novel method for the analysis of microarray data that identifies joint differential expression in gene-pathway pairs. This method takes advantage of known gene pathway memberships to compute a summary expression level for each pathway as a whole. Correlations between the pathway expression summary and the expression levels of genes not already known to be associated with the pathway provide clues to gene interaction dependencies that are not already accounted for through known pathway identification, and statistically significant differences between gene-pathway correlations in phenotypically different cells (e.g., where the expression level of a single gene and a given pathway summary correlate strongly in normal cells but weakly in tumor cells) may indicate biologically relevant gene-pathway interactions. Here, we detail the methodology and present the results of this method applied to two gene-expression datasets, identifying gene-pathway pairs which exhibit differential joint expression by phenotype.
conclusionThe method described herein provides a means by which interactions between large numbers of genes may be identified by incorporating known pathway information to reduce the dimensionality of gene interactions. The method is efficient and easily applied to data sets of ~102 arrays. Application of this method to two publicly-available cancer data sets yields suggestive and promising results. This method has the potential to complement gene-at-a-time analysis techniques for microarray analysis by indicating relationships between pathways and genes that have not previously been identified and which may play a role in disease.
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