alexa Quality Weighted Mean and T-test in Microarray Analysis
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
Open Access

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Research Article

Quality Weighted Mean and T-test in Microarray Analysis Lead to Improved Accuracy in Gene Expression Measurements and Reduced Type I and II Errors in Differential Expression Detection

Shouguo Gao1+, Shuang Jia2+, Martin Hessner2 and Xujing Wang1*

1Department of Physics & the Comprehensive Diabetes Center, University of Alabama at Birmingham, 1300 University Blvd, Birmingham, AL 35294, USA

2The Max McGee National Research Center for Juvenile Diabetes & the Human and   Molecular Genetics   Center, The Medical College of Wisconsin and Children’s Hospital of   Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA

+The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First   Authors.

*Corresponding Author:
Dr. Xujing Wang
Phone : 001-205-934-8186,
Fax      : 001-205-934-8042,
Email   : [email protected]

Received Date: December 09, 2008; Accepted Date: December 22, 2008; Published Date: December 26, 2008

Citation: Shouguo G, Shuang J,Martin H, Xujing W (2008) Quality Weighted Mean and T-test in Microarray Analysis Lead to Improved Accuracy in Gene Expression Measurements and Reduced Type I and II Errors in Differential Expression Detection. J Comput Sci Syst Biol 1:041-049. doi: 10.4172/jcsb.1000003

Copyright: © 2008 Shouguo G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Previously we have reported a microarray image processing and data analysis package Matarray, where quality scores are defined for every spot that reflect the reliability and variability of the data acquired from each spot. In this article we present a new development in Matarray, where the quality scores are incorporated as weights in the statistical evaluation and data mining of microarray data. With this approach filtering of poor quality data is automatically achieved through the reduction in their weights, thereby eliminating the need to manually flag or remove bad data points, as well as the problem of missing values. More significantly, utilizing a set of control clones spiked in at known input ratios ranging from 1:30 to 30:1, we find that the quality-weighted statistics leads to more accurate gene expression measurements and more sensitive detection of their changes with significantly lower type II error rates. Further, we have applied the quality-weighted clustering to a time- course microarray data set, and find that the new algorithm improves grouping accuracy. In summary, incorporating quantitative quality measure of microarray data as weight in complex data analysis leads to improved reliability and convenience. In addition it provides a practical way to deal with the missing value issue in establishing automatic statistical tests.

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