ArticleGenome research2013
Analysis and correction of crosstalk effects in pathway analysis.
Article in Genome research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers.
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Who cites it
107 citing papers in PubMed.
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- From Home to Transcriptome: Comparing the Transcriptomic Profile of Induced Immune Response via Lipopolysaccharide Stimulation in homeRNA and Venous Blood.Analytical chemistry · 2026Article
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- Retinoic acid signaling guides the efficiency of inner ear organoid-genesis and governs sensory-nonsensory fate specification.Stem cell reports · 2025Article
- From Home to Transcriptome: Comparing the transcriptomic profile of induced immune response via lipopolysaccharide stimulation in homeRNA and venous blood.bioRxiv : the preprint server for biology · 2025Article
- Decoding functional and developmental trajectories of tissue-resident uterine dendritic cells through integrative omics.iScience · 2025Article
- Proteomic discoveries in hypermobile Ehlers-Danlos syndrome reveal insights into disease pathophysiology.ImmunoHorizons · 2025Article
- Temporal transcriptomic changes during neurodevelopment in a mouse model of Smith-Lemli-Opitz syndrome.bioRxiv : the preprint server for biology · 2025Article
- Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data.Nature communications · 2025Article
- Low-Glucose Culture Conditions Bias Neuronal Energetics Towards Oxidative Phosphorylation.Journal of neurochemistry · 2025Article
- The oxygen level in air directs airway epithelial cell differentiation by controlling mitochondrial citrate export.Science advances · 2025Article
47 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Identifying the pathways that are significantly impacted in a given condition is a crucial step in understanding the underlying biological phenomena. All approaches currently available for this purpose calculate a P-value that aims to quantify the significance of the involvement of each pathway in the given phenotype. These P-values were previously thought to be independent. Here we show that this is not the case, and that many pathways can considerably affect each other's P-values through a "crosstalk" phenomenon. Although it is intuitive that various pathways could influence each other, the presence and extent of this phenomenon have not been rigorously studied and, most importantly, there is no currently available technique able to quantify the amount of such crosstalk. Here, we show that all three major categories of pathway analysis methods (enrichment analysis, functional class scoring, and topology-based methods) are severely influenced by crosstalk phenomena. Using real pathways and data, we show that in some cases pathways with significant P-values are not biologically meaningful, and that some biologically meaningful pathways with nonsignificant P-values become statistically significant when the crosstalk effects of other pathways are removed. We describe a technique able to detect, quantify, and correct crosstalk effects, as well as identify independent functional modules. We assessed this novel approach on data from four experiments involving three phenotypes and two species. This method is expected to allow a better understanding of individual experiment results, as well as a more refined definition of the existing signaling pathways for specific phenotypes.
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