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Linked Data Based Multi-omics Integration and Visualization for Cancer Decision Networks

  • Alokkumar JhaEmail author
  • Yasar Khan
  • Qaiser Mehmood
  • Dietrich Rebholz-Schuhmann
  • Ratnesh Sahay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11371)

Abstract

Visualization of Gene Expression (GE) is a challenging task since the number of genes and their associations are difficult to predict in various set of biological studies. GE could be used to understand tissue-gene-protein relationships. Currently, Heatmaps is the standard visualization technique to depict GE data. However, Heatmaps only covers the cluster of highly dense regions. It does not provide the Interaction, Functional Annotation and pooled understanding from higher to lower expression. In the present paper, we propose a graph-based technique - based on color encoding from higher to lower expression map, along with the functional annotation. This visualization technique is highly interactive (HeatMaps are mainly static maps). The visualization system here explains the association between overlapping genes with and without tissues types. Traditional visualization techniques (viz-Heatmaps) generally explain each of the association in distinct maps. For example, overlapping genes and their interactions, based on co-expression and expression cut off are three distinct Heatmaps. We demonstrate the usability using ortholog study of GE and visualize GE using GExpressionMap. We further compare and benchmark our approach with the existing visualization techniques. It also reduces the task to cluster the expressed gene networks further to understand the over/under expression. Further, it provides the interaction based on co-expression network which itself creates co-expression clusters. GExpressionMap provides a unique graph-based visualization for GE data with their functional annotation and associated interaction among the DEGs (Differentially Expressed Genes).

Notes

Acknowledgment

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, co-funded by the European Regional Development Fund.

References

  1. 1.
    Battke, F., Symons, S., Nieselt, K.: Mayday-integrative analytics for expression data. BMC Bioinform. 11(1), 121 (2010)CrossRefGoogle Scholar
  2. 2.
    Blake, J.A., Richardson, J.E., Bult, C.J., Kadin, J.A., Eppig, J.T.: MGD: the mouse genome database. Nucleic Acids Res. 31(1), 193–195 (2003)CrossRefGoogle Scholar
  3. 3.
    Chen, T., He, H.L., Church, G.M., et al.: Modeling gene expression with differential equations. In: Pacific Symposium on Biocomputing, vol. 4, p. 4 (1999)Google Scholar
  4. 4.
    Gene Ontology Consortium: Gene ontology consortium: going forward. Nucleic Acids Res. 43(D1), D1049–D1056 (2015)CrossRefGoogle Scholar
  5. 5.
    Delgado, M.D., León, J.: Gene expression regulation and cancer. Clin. Transl. Oncol. 8(11), 780–787 (2006)CrossRefGoogle Scholar
  6. 6.
    Dietzsch, J., Gehlenborg, N., Nieselt, K.: Mayday-a microarray data analysis workbench. Bioinformatics 22(8), 1010–1012 (2006)CrossRefGoogle Scholar
  7. 7.
    Dowell, R.D.: The similarity of gene expression between human and mouse tissues. Genome Biol. 12(1), 101 (2011)CrossRefGoogle Scholar
  8. 8.
    Heinrich, J., Seifert, R., Burch, M., Weiskopf, D.: BiCluster viewer: a visualization tool for analyzing gene expression data. In: Bebis, G., et al. (eds.) ISVC 2011. LNCS, vol. 6938, pp. 641–652. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24028-7_59CrossRefGoogle Scholar
  9. 9.
    Hong, S., Chen, X., Jin, L., Xiong, M.: Canonical correlation analysis for RNA-seq co-expression networks. Nucleic Acids Res. 41(8), e95 (2013)CrossRefGoogle Scholar
  10. 10.
    Jha, A., et al.: Linked functional annotation for differentially expressed gene (DEG) demonstrated using illumina body map 2.0. In: Malone, J., Stevens, R., Forsberg, K., Splendiani, A. (eds.) Proceedings of the 8th Semantic Web Applications and Tools for Life Sciences International Conference, CEUR Workshop Proceedings, Cambridge UK, 7–10 December 2015, vol. 1546, pp. 23–32. CEUR-WS.org (2015)Google Scholar
  11. 11.
    Jha, A., et al.: Towards precision medicine: discovering novel gynecological cancer biomarkers and pathways using linked data. J. Biomed. Semant. 8(1), 40 (2017)CrossRefGoogle Scholar
  12. 12.
    Jha, A., Mehdi, M., Khan, Y., Mehmood, Q., Rebholz-Schuhmann, D., Sahay, R.: Drug dosage balancing using large scale multi-omics datasets. In: Wang, F., Yao, L., Luo, G. (eds.) DMAH 2016. LNCS, vol. 10186, pp. 81–100. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57741-8_6CrossRefGoogle Scholar
  13. 13.
    Katz, Y., Wang, E.T., Airoldi, E.M., Burge, C.B.: Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7(12), 1009–1015 (2010)CrossRefGoogle Scholar
  14. 14.
    Khan, Y., et al.: Safe: policy aware SPARQL query federation over RDF data cubes. In: SWAT4LS (2014)Google Scholar
  15. 15.
    Khomtchouk, B.B., Van Booven, D.J., Wahlestedt, C.: HeatmapGenerator: high performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline. Source Code Biol. Med. 9(1), 1 (2014)CrossRefGoogle Scholar
  16. 16.
    Kommadath, A., et al.: Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding. BMC Genomics 15(1), 1 (2014)CrossRefGoogle Scholar
  17. 17.
    Metsalu, T., Vilo, J.: ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res. 43(W1), W566–W570 (2015)CrossRefGoogle Scholar
  18. 18.
    Mocellin, S., Provenzano, M.: RNA interference: learning gene knock-down from cell physiology. J. Transl. Med. 2(1), 39 (2004)CrossRefGoogle Scholar
  19. 19.
    Monaco, G., van Dam, S., Ribeiro, J.L.C.N., Larbi, A., de Magalhães, J.P.: A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels. BMC Evol. Biol. 15(1), 259 (2015)CrossRefGoogle Scholar
  20. 20.
    Segal, E., et al.: GeneXPress: a visualization and statistical analysis tool for gene expression and sequence data. In: Proceedings of the 11th International Conference on Intelligent Systems for Molecular Biology (ISMB), vol. 18 (2004)Google Scholar
  21. 21.
    Singh, P.K., et al.: Determination of system level alterations in host transcriptome due to Zika virus (ZIKV) Infection in retinal pigment epithelium. Sci. Rep. 8(1), 11209 (2018)CrossRefGoogle Scholar
  22. 22.
    Tang, C., Zhang, L., Zhang, A.: Interactive visualization and analysis for gene expression data. In: Proceedings of the 35th Annual Hawaii International Conference on System Sciences, HICSS 2002, p. 9-pp. IEEE (2002)Google Scholar
  23. 23.
    Weniger, M., Engelmann, J.C., Schultz, J.: Genome Expression Pathway Analysis Tool-analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context. BMC Bioinform. 8(1), 179 (2007)CrossRefGoogle Scholar
  24. 24.
    Wu, C., Zhu, J., Zhang, X.: Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes. BMC Bioinform. 13(1), 182 (2012)CrossRefGoogle Scholar
  25. 25.
    Xia, J., Lyle, N.H., Mayer, M.L., Pena, O.M., Hancock, R.E.: INVEX-a web-based tool for integrative visualization of expression data. Bioinformatics 29(24), 3232–3234 (2013)CrossRefGoogle Scholar
  26. 26.
    Yang, Y., Han, L., Yuan, Y., Li, J., Hei, N., Liang, H.: Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types. Nat. Commun. 5, 3231 (2014)CrossRefGoogle Scholar
  27. 27.
    Yeung, K.Y., Ruzzo, W.L.: Principal component analysis for clustering gene expression data. Bioinformatics 17(9), 763–774 (2001)CrossRefGoogle Scholar
  28. 28.
    Yoshida, R., Higuchi, T., Imoto, S., Miyano, S.: ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles. Bioinformatics 22(12), 1538–1539 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alokkumar Jha
    • 1
    Email author
  • Yasar Khan
    • 1
  • Qaiser Mehmood
    • 1
  • Dietrich Rebholz-Schuhmann
    • 1
  • Ratnesh Sahay
    • 1
  1. 1.Insight Centre for Data AnalyticsNational University of Ireland GalwayGalwayIreland

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