Network propagation: a universal amplifier of genetic associations

Journal name:
Nature Reviews Genetics
Year published:
Published online


Biological networks are powerful resources for the discovery of genes and genetic modules that drive disease. Fundamental to network analysis is the concept that genes underlying the same phenotype tend to interact; this principle can be used to combine and to amplify signals from individual genes. Recently, numerous bioinformatic techniques have been proposed for genetic analysis using networks, based on random walks, information diffusion and electrical resistance. These approaches have been applied successfully to identify disease genes, genetic modules and drug targets. In fact, all these approaches are variations of a unifying mathematical machinery — network propagation — suggesting that it is a powerful data transformation method of broad utility in genetic research.

At a glance


  1. Schematic illustration of network propagation.
    Figure 1: Schematic illustration of network propagation.

    a | A step-by-step demonstration of network propagation. The propagation process is depicted at different time points until convergence (steady-state (t=∞)). Arrows depict the direction of the flow or walk. Nodes are colour-coded according to the amount of flow that they receive. D indicates nodes that are known (square node) or that are predicted (circular node) to be associated with a disease phenotype. b | Example network with initial high scores for two of nine nodes (step 0, nodes A and H; score shown by colour bar). These scores are allowed to propagate over stepwise iterations 0–9; note that convergence is reached by approximately step 5 and thus the colours do not change markedly in subsequent steps. c | Illustration of a biological network with gene scores before and after propagation, performed independently for two data sets (profile 1 and profile 2). Propagation results in greater concordance between the data sets, as is evident from the greater number of green nodes (dashed oval). Part c is adapted with permission from Ref. 89, Macmillan Publishers Limited.

  2. Network propagation for discovery and prioritization of disease genes.
    Figure 2: Network propagation for discovery and prioritization of disease genes.

    a | A schematic example in which a single disease gene (orange) is used to identify additional disease-related genes; known disease genes are denoted by D. Predicting the involvement of the direct interactors of this gene (yellow; left panel) leads to many false positives, as well as to a false negative (shown in the two other panels). Looking at more distant neighbours that are up to two steps away (yellow; middle panel) again introduces many false positives. Network propagation overcomes these problems by simultaneously considering all paths between genes (yellow; right panel). b | A real example of a protein interaction network that is associated with bare lymphocyte syndrome type 1. Propagation of the signal from any of the three known disease genes (red) ranks the other known disease genes very highly, owing to the many paths between them. Genes in yellow are ranked highly by alternative network analysis methods (which consider direct neighbours or shortest paths); however, these are false positives. c | Receiver operating characteristic (ROC) curves for recovering known cancer genes defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) glioblastoma pathway101. Performance over a set of 591 glioblastoma samples is shown for four different gene rankings according to differential mRNA expression between the tumour and normal samples (green)102, somatic mutation frequencies in tumours (black)102, network-propagated differential mRNA expression (mean across samples; blue) and network-propagated somatic mutations (mean across samples; red). Both network propagation variants considerably outperform their frequency-based counterparts (compare the blue curve to the green curve, and the red curve to the black curve). Part b is reproduced with permission from Ref. 73, Elsevier. Part c is reproduced from Ref. 31, Elsevier.

  3. Overview of approaches that use network propagation.
    Figure 3: Overview of approaches that use network propagation.

    Network propagation approaches take a vector the entries of which (0 or 1 or real-values) indicate the prior information on each gene or node in the network. Following propagation, the scores on the nodes are examined using different approaches. a | 1D approaches rank or prioritize genes by their propagated scores. b | 2D approaches analyse a similarity matrix defined by the propagation and extract modules, or subnetworks, according to both the propagated scores and the topology of the network. c | Integrative approaches propagate prior information from different data sets, or individuals, across one or more networks, forming integrated scores that are used to rank genes and/or to extract modules.

  4. Applications of network propagation to analyse cancer data.
    Figure 4: Applications of network propagation to analyse cancer data.

    a | Identifying disease modules with network propagation. The cohesin protein complex is identified using HotNet2, a 2D approach, by propagating the frequencies of somatic mutations in 12 cancer types from The Cancer Genome Atlas. The right panel shows a mutation matrix, the rows of which are the genes in the identified module, and the columns of which are the samples (colour-coded by cancer type) that have a mutation in these genes. Each of the genes in the module is mutated at extremely low frequency, but the propagation of individual frequencies across the network amplifies this weak signal, as these genes are connected by many edges across multiple protein–protein interaction (PPI) networks (left panel). b | Patient stratification with network propagation. The network-based stratification (NBS) integrative approach is used to identify a robust cluster of patients with ovarian cancer, suggesting a new disease subtype. A subnetwork of genes that have high propagated mutation scores in this patient cluster (denoted by node size) and that is most responsible for discriminating the somatic mutation profiles of this subtype from others, is shown. Edge width reflects confidence. Filled nodes indicate that somatic mutations were found for the corresponding gene in the examined cohort. c | The TieDIE (tied diffusion through interacting events) integrative approach is used to integrate gene expression, somatic mutations and phosphoproteomic data in castration-resistant prostate cancer (CRPC) to link genomic mutations, kinase regulators and transcription regulators. A 'scaffold' network that was generated by TieDIE and is centred on six cancer hallmark categories is shown. Hallmark genes are colour-coded according to their annotated category. Other network genes that connect two or more of these hallmark genes are shown in grey. BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; COAD, colon adenocarcinoma; READ, rectum adenocarcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; LAML, acute myeloid leukemia; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; OV, ovarian serous cystadenocarcinoma; UCEC, uterine corpus endometrioid carcinoma. Note that data for COAD and READ have been combined. Part a is adapted with permission from Ref. 30, Macmillan Publishers Limited. Part b is adapted with permission from Ref. 89, Macmillan Publishers Limited. Part c is adapted with permission from Ref. 92, Elsevier.


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Author information


  1. Department of Computer Science, Tufts University, Medford, Massachusetts 02155, USA.

    • Lenore Cowen
  2. University of California San Diego, La Jolla 92093, California, USA.

    • Trey Ideker
  3. Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA.

    • Benjamin J. Raphael
  4. Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.

    • Roded Sharan

Competing interests statement

B.J.R. is a founder of Medley Genomics. The other authors declare they have no competing interests.

Corresponding author

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Author details

  • Lenore Cowen

    Lenore J. Cowen is a professor of Computer Science and Mathematics at Tufts University, USA. Trained in algorithms and graph theory, she has recently become keenly interested in graph-based approaches to the analysis of biological networks. Her research group recently won the community science DREAM challenge for detecting disease modules in heterogeneous biological networks, using a method based on network propagation. Lenore J. Cowen's homepage.

  • Trey Ideker

    Trey Ideker is a professor of Medicine and Bioengineering at the University of California San Diego, USA, where he directs the San Diego Center for Systems Biology and the National Resource for Network Biology. Ideker is a pioneer in constructing network models of biological systems and has founded popular bioinformatic tools, including Cytoscape. He was the 2009 recipient of the Overton Prize from the International Society for Computational Biology and is a fellow of the American Association for the Advancement of Science. Trey Ideker's homepage.

  • Benjamin J. Raphael

    Benjamin J. Raphael is a professor of Computer Science at Princeton University, USA. His research focuses on the design of combinatorial and statistical algorithms for the interpretation of genomes, including network analysis of germline and somatic variants in disease. He has led analysis working groups within The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). He is the recipient of the Alfred P. Sloan Research Fellowship, the NSF CAREER award and a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. Benjamin J. Raphael's homepage.

  • Roded Sharan

    Roded Sharan is a professor of Computer Science and a member of the Edmond J. Safra Center for Bioinformatics at Tel Aviv University, Israel. He heads a research group that specializes in the analysis and modelling of biological networks, and their applications to medicine. He is a recipient of the Thomson-Reuters Highly Cited Researcher award, the Naomi Kadar prize and a RECOMB Test of Time award. He is also a member of the Young Israel Academy of Sciences and Humanities. Roded Sharan's homepage.

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