Linked data cased multi-omics integration and visualization for cancer decision networks
Jha, Alokkumar Khan, Yasar Mehmood, Qaiser Rebholz-Schuhmann, Dietrich Sahay, Ratnesh
Jha, Alokkumar
Khan, Yasar
Mehmood, Qaiser
Rebholz-Schuhmann, Dietrich
Sahay, Ratnesh
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Publication Date
2018-12-30
Type
Conference Paper
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Citation
Jha, Alokkumar, Khan, Yasar, Mehmood, Qaiser, Rebholz-Schuhmann, Dietrich, & Sahay, Ratnesh. (2018). Linked data cased multi-omics integration and visualization for cancer decision networks. Paper presented at the 13th International Conference on Data Integration in the Life Sciences 2018 (DILS2018), Hannover, Germany, 20-21 November. In: Auer S., Vidal ME. (eds) Data Integration in the Life Sciences. DILS 2018. Lecture Notes in Computer Science, vol 11371. Springer, Cham, doi: 10.1007/978-3-030-06016-9_16
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).
Publisher
Springer Verlag
Publisher DOI
10.1007/978-3-030-06016-9
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland