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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Battke, F., Symons, S., Nieselt, K.: Mayday-integrative analytics for expression data. BMC Bioinform. 11(1), 121 (2010)
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)
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)
Gene Ontology Consortium: Gene ontology consortium: going forward. Nucleic Acids Res. 43(D1), D1049–D1056 (2015)
Delgado, M.D., León, J.: Gene expression regulation and cancer. Clin. Transl. Oncol. 8(11), 780–787 (2006)
Dietzsch, J., Gehlenborg, N., Nieselt, K.: Mayday-a microarray data analysis workbench. Bioinformatics 22(8), 1010–1012 (2006)
Dowell, R.D.: The similarity of gene expression between human and mouse tissues. Genome Biol. 12(1), 101 (2011)
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_59
Hong, S., Chen, X., Jin, L., Xiong, M.: Canonical correlation analysis for RNA-seq co-expression networks. Nucleic Acids Res. 41(8), e95 (2013)
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)
Jha, A., et al.: Towards precision medicine: discovering novel gynecological cancer biomarkers and pathways using linked data. J. Biomed. Semant. 8(1), 40 (2017)
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_6
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)
Khan, Y., et al.: Safe: policy aware SPARQL query federation over RDF data cubes. In: SWAT4LS (2014)
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)
Kommadath, A., et al.: Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding. BMC Genomics 15(1), 1 (2014)
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)
Mocellin, S., Provenzano, M.: RNA interference: learning gene knock-down from cell physiology. J. Transl. Med. 2(1), 39 (2004)
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)
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)
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)
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)
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)
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)
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)
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)
Yeung, K.Y., Ruzzo, W.L.: Principal component analysis for clustering gene expression data. Bioinformatics 17(9), 763–774 (2001)
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)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jha, A., Khan, Y., Mehmood, Q., Rebholz-Schuhmann, D., Sahay, R. (2019). Linked Data Based Multi-omics Integration and Visualization for Cancer Decision Networks. In: Auer, S., Vidal, ME. (eds) Data Integration in the Life Sciences. DILS 2018. Lecture Notes in Computer Science(), vol 11371. Springer, Cham. https://doi.org/10.1007/978-3-030-06016-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-06016-9_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-06015-2
Online ISBN: 978-3-030-06016-9
eBook Packages: Computer ScienceComputer Science (R0)