Linked Data Based Multi-omics Integration and Visualization for Cancer Decision Networks
Refereed Conference Meeting Proceeding
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 heat map is the standard vi-sualization technique to depict GE data. However, heat map 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 (Heat Maps are mainly static maps). The visualization system here explains the association between overlapping genes with and without tissues types. Traditional visualization techniques (viz-heat maps) generally explain each of the association in distinct maps. For example, overlapping genes and their Interaction, based on co-expression and expression cut off are three distinct heat maps.We demonstrate the usability using the ortholog study of GE and visualize GE using GExpressionMap. We further compare, and benchmark our approach with the existing vi-sualization techniques. It also reduces the task to further cluster the expressed gene networks to understand the over/under expression. Further , it provides the interaction based on co-expression network which it self creates co-expression clusters. GExpressionMap provides a unique graph based visualization for GE data with their functional annotation and associated interaction among the DEGs.
13th International Conference on Data Integration in the Life Sciences 2018 (DILS2018)
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National University of Ireland, Galway (NUIG)
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