ArticlePloS one2012
Pathway correlation profile of gene-gene co-expression for identifying pathway perturbation.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer.Cancer medicine · 2024Article
- Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers.Scientific reports · 2022Article
- A descriptive marker gene approach to single-cell pseudotime inference.Bioinformatics (Oxford, England) · 2019Article
- Metabolite-Centric Reporter Pathway and Tripartite Network Analysis ofFrontiers in bioengineering and biotechnology · 2018Article
- MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways.PLoS computational biology · 2016Article
- Differential Coexpression Analysis Reveals Extensive Rewiring of Arabidopsis Gene Coexpression in Response to Pseudomonas syringae Infection.Scientific reports · 2016Article
- Large-Scale Public Transcriptomic Data Mining Reveals a Tight Connection between the Transport of Nitrogen and Other Transport Processes in Arabidopsis.Frontiers in plant science · 2016Article
- Novel structural co-expression analysis linking the NPM1-associated ribosomal biogenesis network to chronic myelogenous leukemia.Scientific reports · 2015Article
- Article
- New network topology approaches reveal differential correlation patterns in breast cancer.BMC systems biology · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Identifying perturbed or dysregulated pathways is critical to understanding the biological processes that change within an experiment. Previous methods identified important pathways that are significantly enriched among differentially expressed genes; however, these methods cannot account for small, coordinated changes in gene expression that amass across a whole pathway. In order to overcome this limitation, we use microarray gene expression data to identify pathway perturbation based on pathway correlation profiles. By identifying the distribution of gene-gene pair correlations within a pathway, we can rank the pathways based on the level of perturbation and dysregulation. We have shown this successfully for differences between two experimental conditions in Escherichia coli and changes within time series data in Saccharomyces cerevisiae, as well as two estrogen receptor response classes of breast cancer. Overall, our method made significant predictions as to the pathway perturbations that are involved in the experimental conditions.
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Registered trials
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