Short Report
Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations
1 Center for Computational Medicine & Bioinformatics, 2017 Palmer Commons Bldg., 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA
2 Michigan Institute for Clinical & Health Research, 24 Frank Lloyd Wright Dr., Domino's Farm, Ann Arbor, MI 48106-0421. USA
3 Electrical Engineering and Computer Science, University of Michigan, 2260 Hayward, Ann Arbor, MI 48109-2121, USA
4 UM3D Lab, Digital Media Commons, University of Michigan, 2281 Bonisteel Blvd., Ann Arbor, MI 48109-0738, USA
5 Department of Internal Medicine, Division of Nephrology, University of Michigan Medical School, 1150 W. Medical Centre Drive, MSRB2, SPC 5676 Ann Arbor, MI 48109-5676, USA
6 Current address: Institute for Translational Sciences, University of Texas Medical Branch, 301 University Blvd. Galveston, TX 77555-0129, USA
BMC Research Notes 2010, 3:296 doi:10.1186/1756-0500-3-296
Published: 11 November 2010Abstract
Background
In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods.
Findings
The 3D network layout (displayed through the 3D viewing method) revealed that genes implicated in many diseases (non-specific genes) tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes) tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network.
Conclusions
The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.
