DINGO: differential network analysis in genomics
Open accessAbstract
Motivation: Cancer progression and development are initiated by aberrations in various molecular networks through coordinated changes across multiple genes and pathways. It is important to understand how these networks change under different stress conditions and/or patient-specific groups to infer differential patterns of activation and inhibition. Existing methods are limited to correlation networks that are independently estimated from separate group-specific data and without due consideration of relationships that are conserved across multiple groups. Method: We propose a pathway-based differential network analysis in genomics (DINGO) model for estimating group-specific networks and making inference on the differential networks. DINGO jointly estimates the group-specific conditional dependencies by decomposing them into global and group-specific components. The delineation of these components allows for a more refined picture of the major driver and passenger events in the elucidation of cancer progression and development. Results: Simulation studies demonstrate that DINGO provides more accurate group-specific conditional dependencies than achieved by using separate estimation approaches. We apply DINGO to key signaling pathways in glioblastoma to build differential networks for long-term survivors and short-term survivors in The Cancer Genome Atlas. The hub genes found by mRNA expression, DNA copy number, methylation and microRNA expression reveal several important roles in glioblastoma progression. Availability and implementation: R Package at: odin.mdacc.tmc.edu/∼vbaladan. Contact: veera@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online.
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DINGO works similarly to DiffCoEx by grouping genes based on how differently they behave in a particular subset of samples from the baseline co-expression determined from all samples.
“DINGO is a more recent tool that works similarly to DiffCoEx by grouping genes based on how differently they behave in a particular subset of samples (representing e.g. a particular condition) from the baseline co-expression determined from all samples [102].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [102]
The DINGO model and Global component sections describe a shared network estimated across groups and group-specific relationships, which supports the baseline-and-condition aspect of the paraphrase. But Section 2.2.3 says DINGO scores differences in gene-pair dependencies between groups and constructs a differential network; it does not describe grouping genes by their deviation from the baseline as the claim says. Page numbers are unavailable in our copy. (LLM, conf=0.87)
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Gene co-expression analysis for functional classification and gene–disease predictions
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Sudhakar, P., Verstockt, B., Cremer, J., Verstockt, S., Sabino, J., Ferrante, M., & Vermeire, S. (2021) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa281 · Cited by 49 · see citing works
- Authors
- Ha, M. J., Baladandayuthapani, V., & Do, K.-A.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- Corresponding author
- Baladandayuthapani, Veerabhadran
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-07-06
- Volume
- 31
- Issue
- 21
- Pages
- 3413-3420
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC4751246
- OpenAlex
- W2108156593
- Cited by
- 120
- References
- 50
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 8 · 2023: 10 · 2022: 15 · 2021: 20 · 2020: 10 · 2019: 15 · 2018: 11 · 2017: 12 · 2016: 8
- FWCI
- 4.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institutes of Health (CA- 016672, CA140388)
- University of Texas MD Anderson Cancer Center ((CA016672), P30-CA-016672, P50 CA140388)
- National Cancer Institute (R01CA160736, CA 016672, P50CA140388, CA140388, P30CA016672)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- differential network analysis, tumor progression, glioblastoma, signaling pathways, hub genes, DNA copy number, DNA methylation, microRNA expression, mRNA expression, survival outcomes, TCGA
- MeSH terms
- Algorithms, Computer Simulation, Glioblastoma, Humans, Models, Theoretical, Regression Analysis, RNA, Messenger, Signal Transduction, DNA Methylation, Gene Expression Profiling, Genomics, MicroRNAs, Gene Regulatory Networks, DNA Copy Number Variations
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