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Proceedings Paper

Visualizing differentially expressed genes
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Paper Abstract

Identification of significantly differentially expressed genes (marker genes) among sample groups is a central issue in microarray analysis. This identification is important to understand the molecular pathway of diseases. Many statistical methods have been proposed to locate marker genes. These methods depend on a cutoff value for selection. A tightfisted cutoff may omit some of the important marker genes, whereas a generous threshold increases the number of false positives. Although robust models for identifying marker genes more accurately is an area of intense research, effective tools for the evaluation of results is often ignored in the literature. Despite the robustness of many of these methods, there is always some probability of false positives. In this paper, we propose a novel approach that exploits parallel coordinates to visualize the gene expression patterns so that one can compare the expression level changes of the marker genes between sample groups and determine whether the selected marker genes are valid. Such visualization is useful to measure the validity of the marker gene selection process as well as to fine tune the parameters of a particular method. A prediction method based on the selected marker genes is used to measure the reliability of our process.

Paper Details

Date Published: 8 September 2006
PDF: 12 pages
Proc. SPIE 6310, Photonic Devices and Algorithms for Computing VIII, 63100O (8 September 2006); doi: 10.1117/12.681433
Show Author Affiliations
Atiq U. Islam, Univ. of Memphis (United States)
Khan M. Iftekharuddin, Univ. of Memphis (United States)
David J. Russomanno, Univ. of Memphis (United States)


Published in SPIE Proceedings Vol. 6310:
Photonic Devices and Algorithms for Computing VIII
Khan M. Iftekharuddin; Abdul Ahad Sami Awwal, Editor(s)

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