GeneTerrain: visual exploration of differential gene expression profiles organized in native biomolecular interaction networks

Author(s):  
Qian You ◽  
Shiaofen Fang ◽  
Jake Yue Chen
2008 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Qian You ◽  
Shiaofen Fang ◽  
Jake Yue Chen

We propose a new network visualization technique using scattered data interpolation and surface rendering, based upon a foundation layout of a scalar field. Contours of the interpolated surfaces are generated to support multi-scale visual interaction for data exploration. Our framework visualizes quantitative attributes of nodes in a network as a continuous surface by interpolating the scalar field, therefore avoiding scalability issues typical in conventional network visualizations while also maintaining the topological properties of the original network. We applied this technique to the study of a bio-molecular interaction network integrated with gene expression data for Alzheimer's Disease (AD). In this application, differential gene expression profiles obtained from the human brain are rendered for AD patients with differing degrees of severity and compared to healthy individuals. We show that this alternative visualization technique is effective in revealing several types of molecular biomarkers, which are traditionally difficult to detect due to ‘noises’ in data derived from DNA microarray experiments.


2016 ◽  
Vol 6 (1_suppl) ◽  
pp. s-0036-1582635-s-0036-1582635 ◽  
Author(s):  
Sibylle Grad ◽  
Ying Zhang ◽  
Olga Rozhnova ◽  
Elena Schelkunova ◽  
Mikhail Mikhailovsky ◽  
...  

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.


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