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Pattern Recognition Letters
Volume 28, Issue 12, 1 September 2007, Pages 1472-1482
 
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doi:10.1016/j.patrec.2007.03.006    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2007 Elsevier B.V. All rights reserved.

A trend pattern assessment approach to microarray gene expression profiling data analysis

Kajia Caoa, Corresponding Author Contact Information, E-mail The Corresponding Author, Qiuming Zhua, Javeed Iqbalb and John W.C. Chanb

aDepartment of Computer Science, University of Nebraska at Omaha, United States bDepartment of Pathology and Microbiology, University of Nebraska Medical Center, United States

Received 27 January 2006; 
revised 7 November 2006. 
Communicated by M. Singh. 
Available online 27 March 2007.

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Abstract

We study the problem of how to assess the reliability of a statistical measurement on data set containing unknown quantity of noises, inconsistencies, and outliers. A practical approach that analyzes the dynamical patterns (trends) of the statistical measurements through a sequential extreme-boundary-points (EBP) weed-out process is explored. We categorize the weed-out trend patterns (WOTP) and examine their relation to the reliability of the measurement. The approach is applied to the processes of extracting genes that are predictive to BCL2 translocations and to clinical survival outcomes of diffuse large B-cell lymphoma (DLBCL) from DNA Microarray gene expression profiling data sets. Fisher’s Discriminate Criterion (FDC) is used as a statistical measurement in the processes. It is found that the weed-out trend analysis (WOTA) approach is effective for qualitatively assessing the statistics-based measurements in the experimentations conducted.

Keywords: Gene expression profiling; Microarray data analysis; Boundary points; Dynamical patterns; Trend evaluations; Fisher’s discriminate criterion

Abbreviations: extreme-boundary-points, EBP; weed-out trend pattern, WOTP; weed-out trend analysis, WOTA; diffuse large B-cell lymphoma, DLBCL; Fisher’s discriminate criterion, FDC

Article Outline

1. Introduction
2. The WOTA approach
2.1. Measurement reliability ρm(X)
2.2. Extreme-boundary-points (EBPs)
2.3. EBP weed-out trend patterns (WOTP)
2.4. WOTA algorithm
3. Application of WOTA to FDC for microarray data analysis
3.1. On the microarray data analysis
3.2. FDC for DLBCL data analysis
3.2.1. The FDC measurement
3.2.2. The measurement reliability of FDC
3.3. Applying WOTA to FDC measurement – algorithm
4. Experiment results and analysis
4.1. BCL2 translocation correlative gene extraction
4.2. Results on clinical outcome prediction
5. Conclusion
Acknowledgements
References






Pattern Recognition Letters
Volume 28, Issue 12, 1 September 2007, Pages 1472-1482
 
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