Visualization of Gene Expression (GE) is a challenging task
since the number of genes and their associations are diffcult to predict in
various set of biological studies. GE could be used to understand tissuegene-
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 graphbased
visualization for GE data with their functional annotation and
associated interaction among the DEGs (Differentially Expressed Genes). |