Skip to main content
SpringerLink
Log in

NetMix2: Unifying Network Propagation and Altered Subnetworks

  • Conference paper
  • First Online:
Research in Computational Molecular Biology (RECOMB 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13278))

  • 1957 Accesses

Abstract

A standard paradigm in computational biology is to use interaction networks to analyze high-throughput biological data. Two common approaches for leveraging interaction networks are: (1) network ranking, where one ranks vertices in the network according to both vertex scores and network topology; (2) altered subnetwork identification, where one identifies one or more subnetworks in an interaction network using both vertex scores and network topology. The dominant approach in network ranking is network propagation which smooths vertex scores over the network using a random walk or diffusion process, thus utilizing the global structure of the network. For altered subnetwork identification, existing algorithms either restrict solutions to subnetworks in subnetwork families with simple topological constraints, such as connected subnetworks, or utilize ad hoc heuristics that lack a rigorous statistical foundation. In this work, we unify the network propagation and altered subnetwork approaches. We derive a subnetwork family which we call the propagation family that approximates the subnetworks ranked highly by network propagation. We introduce NetMix2, a principled algorithm for identifying altered subnetworks from a wide range of subnetwork families, including the propagation family, thus combining the advantages of the network propagation and altered subnetwork approaches. We show that NetMix2 outperforms network propagation on data simulated using the propagation family. Furthermore, NetMix2 outperforms other methods at recovering known disease genes in pan-cancer somatic mutation data and in genome-wide association data from multiple human diseases. NetMix2 is publicly available at https://github.com/raphael-group/netmix2.

U. Chitra and T. Y. Park—Contributed equally to the manuscript.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    A related problem is the identification of altered subnetworks according to network topology alone. Many of the leading methods for this problem were benchmarked in a recent DREAM competition [18].

References

  1. Addario-Berry, L., Broutin, N., Devroye, L., Lugosi, G.: On combinatorial testing problems. Ann. Stat. 38(5), 3063–3092 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  2. Arias-Castro, E., Candès, E.J., Durand, A.: Detection of an anomalous cluster in a network. Ann. Stat. 39(1), 278–304 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  3. Arias-Castro, E., Candès, E.J., Helgason, H., Zeitouni, O.: Searching for a trail of evidence in a maze. Ann. Stat. 36(4), 1726–1757 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  4. Arias-Castro, E., Donoho, D.L., Huo, X.: Adaptive multiscale detection of filamentary structures in a background of uniform random points. Ann. Stat. 34(1), 326–349 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  5. Azencott, C.A., Grimm, D., Sugiyama, M., Kawahara, Y., Borgwardt, K.M.: Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics 29(13), i171–i179 (2013)

    Article  Google Scholar 

  6. Bailey, M.H., et al.: Comprehensive characterization of cancer driver genes and mutations. Cell 173(2), 371–385 (2018)

    Article  MathSciNet  Google Scholar 

  7. Barel, G., Herwig, R.: NetCore: a network propagation approach using node coreness. Nucleic Acids Res. 48(17), e98–e98 (2020)

    Article  Google Scholar 

  8. Battaglia, S., Maguire, O., Campbell, M.J.: Transcription factor co-repressors in cancer biology: roles and targeting. Int. J. Cancer 126(11), 2511–2519 (2010)

    Google Scholar 

  9. Berger, B., Peng, J., Singh, M.: Computational solutions for omics data. Nature Rev. Genet. 14(5), 333–346 (2013)

    Article  Google Scholar 

  10. Cadena, J., Chen, F., Vullikanti, A.: Near-optimal and practical algorithms for graph scan statistics with connectivity constraints. ACM Trans. Knowl. Discov. Data 13(2), 20:1-20:33 (2019)

    Article  Google Scholar 

  11. Cai, T.T., Jin, J., Low, M.G.: Estimation and confidence sets for sparse normal mixtures. Ann. Stat. 35(6), 2421–2449 (2007)

    MathSciNet  MATH  Google Scholar 

  12. Califano, A., Butte, A.J., Friend, S., Ideker, T., Schadt, E.: Leveraging models of cell regulation and GWAS data in integrative network-based association studies. Nat. Genet. 44(8), 841–847 (2012)

    Article  Google Scholar 

  13. Cao, M., et al.: Going the distance for protein function prediction: a new distance metric for protein interaction networks. PLoS One 8(10), 1–12 (2013)

    Google Scholar 

  14. Chakravarty, D., et al.: OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 1, 1–16 (2017)

    Google Scholar 

  15. Chasman, D., Siahpirani, A.F., Roy, S.: Network-based approaches for analysis of complex biological systems. Curr. Opin. Biotech. 39, 157–166 (2016)

    Article  Google Scholar 

  16. Chitra, U., Ding, K., Lee, J.C., Raphael, B.J.: Quantifying and reducing bias in maximum likelihood estimation of structured anomalies. In: Proceedings of the 38th International Conference on Machine Learning, pp. 1908–1919. PMLR, 18–24 July 2021

    Google Scholar 

  17. Cho, D.Y., Kim, Y.A., Przytycka, T.M.: Chapter 5: network biology approach to complex diseases. PLoS Comput. Biol. 8(12), 1–11 (2012)

    Article  Google Scholar 

  18. Choobdar, S., et al.: Assessment of network module identification across complex diseases. Nat. Methods 16(9), 843–852 (2019)

    Article  Google Scholar 

  19. Chua, H.N., Sung, W.K., Wong, L.: Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics 22(13), 1623–1630 (2006)

    Article  Google Scholar 

  20. modENCODE Consortium, Roy, S., Ernst, J., Kharchenko, P.V., Kheradpour, P., et al.: Identification of functional elements and regulatory circuits by drosophila modencode. Science 330(6012), 1787–1797 (2010)

    Google Scholar 

  21. Cornish, A.J., Markowetz, F.: SANTA: Quantifying the functional content of molecular networks. PLoS Comput. Biol. 10(9), e1003808 (2014)

    Google Scholar 

  22. Cowen, L., Devkota, K., Hu, X., Murphy, J.M., Wu, K.: Diffusion state distances: Multitemporal analysis, fast algorithms, and applications to biological networks. SIAM J. Math. Data Sci. 3(1), 142–170 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  23. Cowen, L., Ideker, T., Raphael, B.J., Sharan, R.: Network propagation: a universal amplifier of genetic associations. Nat. Rev. Genet. 18(9), 551–562 (2017)

    Article  Google Scholar 

  24. Creixell, P., et al.: Pathway and network analysis of cancer genomes. Nat. Methods 12(7), 615–621 (2015)

    Article  Google Scholar 

  25. de la Fuente, A.: From ‘differential expression’ to ‘differential networking’ - identification of dysfunctional regulatory networks in diseases. Trends Genet. 26(7), 326–333 (2010)

    Article  Google Scholar 

  26. Deng, M., Zhang, K., Mehta, S., Chen, T., Sun, F.: Prediction of protein function using protein-protein interaction data. J. Comput. Biol. 10(6), 947–960 (2003)

    Article  Google Scholar 

  27. Dimitrakopoulos, C.M., Beerenwinkel, N.: Computational approaches for the identification of cancer genes and pathways. WIREs Syst. Biol. Med. 9(1), e1364 (2017)

    Article  Google Scholar 

  28. Dittrich, M.T., Klau, G., Rosenwald, A., Dandekar, T., Muller, T.: Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics 24(13), i223–i231 (2008)

    Article  Google Scholar 

  29. Donoho, D., Jin, J.: Higher criticism for detecting sparse heterogeneous mixtures. Ann. Stat. 32(3), 962–994 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  30. Efron, B.: Large-scale simultaneous hypothesis testing: the choice of a null hypothesis. J. Am. Stat. Assoc. 99(465), 96–104 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  31. Efron, B.: Correlation and large-scale simultaneous significance testing. J. Am. Stat. Assoc. 102(477), 93–103 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  32. Efron, B.: Size, power and false discovery rates. Ann. Stat. 35(4), 1351–1377 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  33. Ghiassian, S.D., Menche, J., Barabási, A.L.: A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. PLoS Comput. Biol. 11(4), e1004120 (2015)

    Google Scholar 

  34. Glaz, J., Naus, J., Wallenstein, S.: Scan Statistics. Springer-Verlag, New York (2001). https://doi.org/10.1007/978-1-4757-3460-7

    Book  MATH  Google Scholar 

  35. Gligorijević, V., Pržulj, N.: Methods for biological data integration: perspectives and challenges. J. Roy. Soc. Interface 12(112), 20150571 (2015)

    Article  Google Scholar 

  36. Guo, Z., et al.: Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network. Bioinformatics 23(16), 2121–2128 (2007)

    Article  Google Scholar 

  37. Gurobi Optimization, LLC: Gurobi Optimizer Reference Manual (2021)

    Google Scholar 

  38. Halldórsson, B.V., Sharan, R.: Network-based interpretation of genomic variation data. J. Mol. Biol. 425(21), 3964–3969 (2013)

    Article  Google Scholar 

  39. Hofree, M., Shen, J.P., Carter, H., Gross, A., Ideker, T.: Network-based stratification of tumor mutations. Nat. Methods 10(11), 1108–1115 (2013)

    Article  Google Scholar 

  40. Hormozdiari, F., Penn, O., Borenstein, E., Eichler, E.E.: The discovery of integrated gene networks for autism and related disorders. Genome Res. 25(1), 142–154 (2015)

    Article  Google Scholar 

  41. Horn, H., Lawrence, M.S., Chouinard, C.R., Shrestha, Y., Hu, J.X., et al.: NetSig: network-based discovery from cancer genomes. Nat. Methods 15(1), 61–66 (2018)

    Article  Google Scholar 

  42. Huang, J.K., et al.: Systematic evaluation of molecular networks for discovery of disease genes. Cell Syst. 6(4), 484–495 (2018)

    Article  Google Scholar 

  43. Ideker, T., Ozier, O., Schwikowski, B., Siegel, A.F.: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18(suppl 1), S233–S240 (2002)

    Article  Google Scholar 

  44. Ideker, T., et al.: Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292(5518), 929–934 (2001)

    Article  Google Scholar 

  45. Jia, P., Zhao, Z.: Network assisted analysis to prioritize GWAS results: principles, methods and perspectives. Hum. Genet. 133(2), 125–138 (2014). https://doi.org/10.1007/s00439-013-1377-1

    Article  Google Scholar 

  46. Kloumann, I.M., Ugander, J., Kleinberg, J.: Block models and personalized PageRank. Proc. Natl. Acad. Sci. 114(1), 33–38 (2017)

    Article  Google Scholar 

  47. Kulldorff, M.: A spatial scan statistic. Commun. Stat. Theory Methods 26(6), 1481–1496 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  48. Köhler, S., Bauer, S., Horn, D., Robinson, P.N.: Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet. 82(4), 949–958 (2008)

    Article  Google Scholar 

  49. Lawrence, M.S., et al.: Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505(7484), 495–501 (2014)

    Article  Google Scholar 

  50. Lazareva, O., Baumbach, J., List, M., Blumenthal, D.B.: On the limits of active module identification. Briefings Bioinf. 22(5), bbab066 (2021)

    Article  Google Scholar 

  51. Lee, I., Blom, U.M., Wang, P.I., Shim, J.E., Marcotte, E.M.: Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 21(7), 1109–1121 (2011)

    Article  Google Scholar 

  52. Leiserson, M.D.M., Vandin, F., Wu, H.T., Dobson, J.R., et al.: Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genetics 47(2), 106–114 (2015)

    Article  Google Scholar 

  53. Leiserson, M.D., Eldridge, J.V., Ramachandran, S., Raphael, B.J.: Network analysis of GWAS data. Curr. Opin. Genet. Dev. 23(6), 602–610 (2013)

    Article  Google Scholar 

  54. Levi, H., Elkon, R., Shamir, R.: DOMINO: a network-based active module identification algorithm with reduced rate of false calls. Mol. Syst. Biol. 17(1), e9593 (2021)

    Article  Google Scholar 

  55. Liu, Y., et al.: SigMod: an exact and efficient method to identify a strongly interconnected disease-associated module in a gene network. Bioinformatics 33(10), 1536–1544 (2017)

    Google Scholar 

  56. Luo, Y., et al.: A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun. 8(1), 573 (2017)

    Article  Google Scholar 

  57. McLachlan, G., Bean, R.W., Jones, L.B.T.: A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics 22(13), 1608–1615 (2006)

    Article  Google Scholar 

  58. Menche, J., et al.: Uncovering disease-disease relationships through the incomplete human interactome. Science 347(6224), 1257601 (2015)

    Article  Google Scholar 

  59. Mitra, K., Carvunis, A.R., Ramesh, S.K., Ideker, T.: Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14(10), 719–732 (2013)

    Article  Google Scholar 

  60. Nabieva, E., Jim, K., Agarwal, A., Chazelle, B., Singh, M.: Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21, i302–i310 (2005)

    Article  Google Scholar 

  61. Nibbe, R.K., Koyutürk, M., Chance, M.R.: An integrative-omics approach to identify functional sub-networks in human colorectal cancer. PLoS Comput. Biol. 6(1), e1000639 (2010)

    Article  Google Scholar 

  62. Nikolayeva, I., Pla, O.G., Schwikowski, B.: Network module identification-a widespread theoretical bias and best practices. Methods 132, 19–25 (2018)

    Article  Google Scholar 

  63. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Technical report 1999-66, Stanford InfoLab, November 1999

    Google Scholar 

  64. Pan, W., Lin, J., Le, C.T.: A mixture model approach to detecting differentially expressed genes with microarray data. Funct. Integr. Genomics 3(3), 117–124 (2003). https://doi.org/10.1007/s10142-003-0085-7

    Article  Google Scholar 

  65. Paull, E.O., Carlin, D.E., Niepel, M., Sorger, P.K., Haussler, D., Stuart, J.M.: Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE). Bioinformatics 29(21), 2757–2764 (2013)

    Article  Google Scholar 

  66. Picart-Armada, S., Barrett, S.J., Willé, D.R., Perera-Lluna, A., Gutteridge, A., Dessailly, B.H.: Benchmarking network propagation methods for disease gene identification. PLoS Comput. Biol. 15(9), 1–24 (2019)

    Google Scholar 

  67. Pounds, S., Morris, S.W.: Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics 19(10), 1236–1242 (2003)

    Article  Google Scholar 

  68. Radivojac, P., et al.: A large-scale evaluation of computational protein function prediction. Nat. Methods 10(3), 221–227 (2013)

    Article  Google Scholar 

  69. Reyna, M.A., Chitra, U., Elyanow, R., Raphael, B.J.: NetMix: a network-structured mixture model for reduced-bias estimation of altered subnetworks. J. Computat. Biol. 28(5), 469–484 (2021)

    Article  MathSciNet  Google Scholar 

  70. Reyna, M.A., Leiserson, M.D., Raphael, B.J.: Hierarchical HotNet: identifying hierarchies of altered subnetworks. Bioinformatics 34(17), i972–i980 (2018)

    Article  Google Scholar 

  71. Robinson, S., Nevalainen, J., Pinna, G., Campalans, A., Radicella, J.P., Guyon, L.: Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields. Bioinformatics 33(14), i170–i179 (2017)

    Article  Google Scholar 

  72. Sharan, R., Ulitsky, I., Shamir, R.: Network-based prediction of protein function. Mol. Syst. Biol. 3, 88 (2007)

    Article  Google Scholar 

  73. Sharpnack, J., Krishnamurthy, A., Singh, A.: Near-optimal anomaly detection in graphs using Lovász extended scan statistic. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS 2013, vol. 2. pp. 1959–1967 (2013)

    Google Scholar 

  74. Sharpnack, J., Rinaldo, A., Singh, A.: Detecting anomalous activity on networks with the graph Fourier scan statistic. IEEE Trans. Signal Process. 64(2), 364–379 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  75. Sharpnack, J., Singh, A., Rinaldo, A.: Changepoint detection over graphs with the spectral scan statistic. In: Artificial Intelligence and Statistics, pp. 545–553 (2013)

    Google Scholar 

  76. Shrestha, R., et al.: HIT’nDRIVE: patient-specific multidriver gene prioritization for precision oncology. Genome Res. 27(9), 1573–1588 (2017)

    Article  Google Scholar 

  77. Szklarczyk, D., et al.: STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43(D1), D447–D452 (2015)

    Article  Google Scholar 

  78. Tate, J.G., et al.: COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47(D1), D941–D947 (2019)

    Article  Google Scholar 

  79. Ulitsky, I., Shamir, R.: Identification of functional modules using network topology and high-throughput data. BMC Syst. Biol. 1(1), 8 (2007). https://doi.org/10.1186/1752-0509-1-8

    Article  Google Scholar 

  80. Vandin, F., Clay, P., Upfal, E., Raphael, B.J.: Discovery of mutated subnetworks associated with clinical data in cancer. In: Pacific Symposium on Biocomputing, vol. 17, pp. 55–66 (2012)

    Google Scholar 

  81. Vandin, F., Upfal, E., Raphael, B.J.: Algorithms for detecting significantly mutated pathways in cancer. J. Comput. Biol. 18(3), 507–522 (2011)

    Article  MathSciNet  Google Scholar 

  82. Vandin, F., Upfal, E., Raphael, B.J.: De novo discovery of mutated driver pathways in cancer. Genome Res. 22(2), 375–385 (2012)

    Article  Google Scholar 

  83. Vanunu, O., Magger, O., Ruppin, E., Shlomi, T., Sharan, R.: Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol. 6(1), e1000641 (2010)

    Article  MathSciNet  Google Scholar 

  84. Velghe, A., et al.: PDGFRA alterations in cancer: characterization of a gain-of-function V536E transmembrane mutant as well as loss-of-function and passenger mutations. Oncogene 33(20), 2568–2576 (2014)

    Article  Google Scholar 

  85. Vlaic, S., et al.: ModuleDiscoverer: identification of regulatory modules in protein-protein interaction networks. Sci. Rep. 8(1), 433 (2018)

    Article  Google Scholar 

  86. Wang, X., Terfve, C., Rose, J.C., Markowetz, F.: HTSanalyzeR: an R/Bioconductor package for integrated network analysis of high-throughput screens. Bioinformatics 27(6), 879–880 (2011)

    Article  Google Scholar 

  87. Weston, J., Elisseeff, A., Zhou, D., Leslie, C.S., Noble, W.S.: Protein ranking: from local to global structure in the protein similarity network. Proc. Nat. Acad. Sci. 101(17), 6559–6563 (2004)

    Article  Google Scholar 

  88. Xia, J., Gill, E.E., Hancock, R.E.W.: NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat. Protoc. 10(6), 823–844 (2015)

    Article  Google Scholar 

  89. Zhou, D., Bousquet, O., Lal, T., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems, vol. 16. MIT Press (2004)

    Google Scholar 

Download references

Acknowledgement

The authors would like to thank Jasper C. H. Lee and Christopher Musco for helpful discussions, as well as Matthew A. Myers and Palash Sashittal for reviewing early versions of the manuscript. U.C. is supported by NSF GRFP DGE 2039656. B.J.R. is supported by grant U24CA264027 from the National Cancer Institute (NCI).

Author information

Authors and Affiliations

  1. Department of Computer Science, Princeton University, Princeton, NJ, 08544, USA

    Uthsav Chitra, Tae Yoon Park & Benjamin J. Raphael

  2. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA

    Tae Yoon Park & Benjamin J. Raphael

Authors
  1. Uthsav Chitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tae Yoon Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benjamin J. Raphael
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benjamin J. Raphael .

Editor information

Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Itsik Pe'er

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chitra, U., Park, T.Y., Raphael, B.J. (2022). NetMix2: Unifying Network Propagation and Altered Subnetworks. In: Pe'er, I. (eds) Research in Computational Molecular Biology. RECOMB 2022. Lecture Notes in Computer Science(), vol 13278. Springer, Cham. https://doi.org/10.1007/978-3-031-04749-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-04749-7_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04748-0

  • Online ISBN: 978-3-031-04749-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation