De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

Winkler, Sebastian; Winkler, Ivana; Figaschewski, Mirjam; Tiede, Thorsten; Nordheim, Alfred; Kohlbacher, Oliver

doi:10.1186/s12859-022-04670-6

Cited by 3 publications

(1 citation statement)

References 125 publications

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“…Multi-omic integration methods have been developed for diverse applications (Maghsoudi et al ., 2022; Zitnik et al ., 2023), such as embedding single-cell data (Ashuach et al ., 2023; Argelaguet et al ., 2020), clustering cancer samples (Chauvel et al ., 2020; Wang et al ., 2014), and pathway reconstruction (Tuncbag et al ., 2016; Winkler et al ., 2022; Paull et al ., 2013). Multi-omics analyses have been particularly prominent in cancer, with pathway enrichment (Paczkowska et al ., 2020), representation learning (Leng et al ., 2022), supervised prediction of cancer subtypes or patient outcomes (Poirion et al ., 2021; Choi and Chae, 2023), and biologically interpretable neural networks (Wysocka et al ., 2023) as representative areas of study.…”

Section: Discussionmentioning

confidence: 99%

MPAC: a computational framework for inferring cancer pathway activities from multi-omic data

Liu,

Page,

Ahlquist

et al. 2024

Preprint

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Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cancer (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC uses network relationships encoded in pathways using a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to prioritize proteins with potential clinical relevance. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available athttps://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

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Section: Discussionmentioning

confidence: 99%

MPAC: a computational framework for inferring cancer pathway activities from multi-omic data

Liu,

Page,

Ahlquist

et al. 2024

Preprint

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Current and future directions in network biology

Zitnik,

Li,

Wells

et al. 2024

Bioinformatics Advances

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Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation Not applicable.

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SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks

Loers

Vermeirssen

2022

BMC Bioinformatics

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Background Representing the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators. Results SUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein–protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels. Conclusions We developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/CBIGR/SUBATOMIC.

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De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

Cited by 3 publications

References 125 publications

MPAC: a computational framework for inferring cancer pathway activities from multi-omic data

MPAC: a computational framework for inferring cancer pathway activities from multi-omic data

Current and future directions in network biology

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks

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