2 December 2011 Visualization and analysis for multidimensional gene expressions signature of cigarette smoking
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Proceedings Volume 8004, MIPPR 2011: Pattern Recognition and Computer Vision; 800413 (2011); doi: 10.1117/12.902121
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China
Abstract
Biologists often use gene chip to get massive experimental data in the field of bioscience and chemical sciences. Facing a large amount of experimental data, researchers often need to find out a few interesting data or simple regulations. This paper presents a set of methods to visualize and analyze the data for gene expression signatures of people who smoke. We use the latest research data from National Center for Biotechnology Information. Totally, there are more than 400 thousand expressions data. Using these data, we can use parallel coordinates method to visualize the different gene expressions between smokers and nonsmokers and we can distinguish non-smokers, former smokers and current smokers by using the different colors. It can be easy to find out which gene is more important during the lung cancer angiogenesis in the smoking people. In another way, we can use a hierarchical model to visualize the inner relation of different genes. The location of the nodes shows different expression moment and the distance to the root shows the sequence of the expression. We can use the ring layout to represent all the nodes, and connect the different nodes which are related with color lines. Combined with the parallel coordinates method, the visualization result show the important genes and some inner relation obviously, which is useful for examination and prevention of lung cancer.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Changbo Wang, Zhao Xiao, Tianlun Zhang, Jin Cui, Chenming Pang, "Visualization and analysis for multidimensional gene expressions signature of cigarette smoking", Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 800413 (2 December 2011); doi: 10.1117/12.902121; https://doi.org/10.1117/12.902121
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