Multi-tissue Analysis of Co-expression Networks by Higher-Order Generalized Singular Value Decomposition Identifies Functionally Coherent Transcriptional Modules
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Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed heat shock protein (Hsp) and cardiomyopathy genes (Bag3, Cryab, Kras, Emd, Plec), which was significantly replicated using separate failing heart and liver gene expression datasets in humans, thus revealing a conserved functional role for Hsp genes in cardiovascular disease.
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HO-GSVD outperformed WGCNA and DiffCoEx based on its ability to detect clusters in simulated data.
“HO-GSVD outperformed WGCNA and DiffCoEx based on its ability to detect clusters in simulated data [136].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [136]
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- Authors
- Xiao, X., Moreno-Moral, A., Rotival, M., Bottolo, L., & Petretto, E.
- Affiliations
- Imperial College London (GB)
- Corresponding author
- Petretto, Enrico
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-01-02
- Volume
- 10
- Issue
- 1
- Pages
- e1004006
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC3879165
- OpenAlex
- W2030813272
- Cited by
- 63
- References
- 66
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 3 · 2022: 3 · 2021: 6 · 2020: 3 · 2019: 7 · 2018: 4 · 2017: 7 · 2016: 11 · 2015: 5 · 2014: 5
- FWCI
- 3.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Funders
- British Heart Foundation (FS/11/25/28740)
- European Commission (241504)
- Medical Research Council (FP7/20072013)
- Engineering and Physical Sciences Research Council (Platform grant EP/I019111/1, EP/I019111/1, FP7/2007–2013, Mathematics Platform grant EP/I019111/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- gene co-expression network, transcriptional modules, multi-tissue transcriptomics, network module identification, cell cycle progression, extracellular matrix, neocortex expansion, heat shock proteins, cardiomyopathy genes, cardiovascular disease
- MeSH terms
- Algorithms, Animals, Humans, Cardiomyopathies, Organ Specificity, Transcription, Genetic, Signal Transduction, Gene Expression, Genome, Human, Cell Cycle Proteins, Gene Expression Profiling, Rats, Gene Regulatory Networks
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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