A novel signaling pathway impact analysis
Open accessAbstract
Motivation: Gene expression class comparison studies may identify hundreds or thousands of genes as differentially expressed (DE) between sample groups. Gaining biological insight from the result of such experiments can be approached, for instance, by identifying the signaling pathways impacted by the observed changes. Most of the existing pathway analysis methods focus on either the number of DE genes observed in a given pathway (enrichment analysis methods), or on the correlation between the pathway genes and the class of the samples (functional class scoring methods). Both approaches treat the pathways as simple sets of genes, disregarding the complex gene interactions that these pathways are built to describe. Results: We describe a novel signaling pathway impact analysis (SPIA) that combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition. A bootstrap procedure is used to assess the significance of the observed total pathway perturbation. Using simulations we show that the evidence derived from perturbations is independent of the pathway enrichment evidence. This allows us to calculate a global pathway significance P-value, which combines the enrichment and perturbation P-values. We illustrate the capabilities of the novel method on four real datasets. The results obtained on these data show that SPIA has better specificity and more sensitivity than several widely used pathway analysis methods. Availability: SPIA was implemented as an R package available at http://vortex.cs.wayne.edu/ontoexpress/ Contact: sorin@wayne.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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The SASP produced by all Sen cells activated downstream signalling pathways in receiving NSen SCs, including senescence, apoptosis and inflammatory responses.
“A signalling pathway impact analysis (SPIA)40 of transcription factors involved in these interactions revealed that the SASP produced by all Sen cells activated downstream signalling pathways in receiving NSen SCs (senescence, apoptosis and inflammatory responses) and inhibited cell-cycle and proliferative pathways (MAPK and AKT signalling) (Extended Data Fig. 11f).”
From Senescence atlas reveals an aged-like inflamed niche that blunts muscle regeneration (2023) · cites this paper as [40]
The SASP produced by all Sen cells inhibited cell-cycle and proliferative pathways (MAPK and AKT signalling) in receiving NSen SCs.
“A signalling pathway impact analysis (SPIA)40 of transcription factors involved in these interactions revealed that the SASP produced by all Sen cells activated downstream signalling pathways in receiving NSen SCs (senescence, apoptosis and inflammatory responses) and inhibited cell-cycle and proliferative pathways (MAPK and AKT signalling) (Extended Data Fig. 11f).”
From Senescence atlas reveals an aged-like inflamed niche that blunts muscle regeneration (2023) · cites this paper as [40]
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- Authors
- Tarca, A. L., Draghici, S., Khatri, P., Hassan, S. S., Mittal, P., Kim, J.-S., Kim, C. J., Kusanovic, J. P., & Romero, R.
- Affiliations
- Wayne State University (US)
- 1 Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202 and 2Perinatology Research Branch-NIH/NICHD, 4 Brush, 3990 John R, Detroit, MI 48201, USA
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-11-05
- Volume
- 25
- Issue
- 1
- Pages
- 75-82
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2732297
- OpenAlex
- W2110687686
- Cited by
- 1,031
- References
- 27
- Citations by year
- 2026: 20 · 2025: 43 · 2024: 58 · 2023: 63 · 2022: 79 · 2021: 94 · 2020: 84 · 2019: 67 · 2018: 85 · 2017: 68 · 2016: 80 · 2015: 81 · 2014: 65 · 2013: 45 · 2012: 34
- FWCI
- 8.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (0438970, 0234806)
- U.S. Department of Defense
- U.S. Department of Health and Human Services
- National Institutes of Health (CA022453)
- National Human Genome Research Institute (R01HG003491)
- National Cancer Institute (R21CA100740, U01CA117478, P30CA022453)
- National Institute of Neurological Disorders and Stroke (R01NS045207)
- National Institute of Biomedical Imaging and Bioengineering (R21EB000990)
- National Institute of Child Health and Human Development
- Division of Biological Infrastructure
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- pathway perturbation, pathway enrichment, differentially expressed genes, gene interaction networks, R package
- MeSH terms
- False Positive Reactions, Female, Humans, Labor, Obstetric, Models, Genetic, Pregnancy, Colorectal Neoplasms, Signal Transduction, Computational Biology, Databases, Genetic
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