Transcriptome Data Visualization in Pathways with application to Zebrafish Embryo Datasets

Gene expression data analysis from high throughput approaches is complicated due to its abundant scales. It is especially difficult to analyze and categorize temporal differential expression of gene groups and discover interactive relationship among genes from single gene observation. Hence, integration of temporal gene expression data and biological pathway information may provide a powerful approach for understanding dynamic gene regulatory networks. In this study, we adopted the expression data from a series of zebrafish embryo developmental stages, and integrated with KEGG biological pathways to display dynamic gene expression through both heat map and 3D visualization methods in a temporal version. Through filtering analysis, all drastically varied gene expression could be identified at different time and it reflected how these key genes play important roles during embryogenic development. The designed system could retrieve important information for temporal gene expression constrained to a specific group of genes.