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Bioinformatics Advance Access published online on September 16, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti036
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received May 11, 2004
Revised August 26, 2004
Accepted September 11, 2004

Article

Reliability analysis of microarray data using fuzzy C-means and normal mixture modeling based classification methods

Musa H. Asyali 1* and Musa Alci 2

1 Department of Biostatistics, Epidemiology, and Scientific Computing, King Faisal Specialist Hospital and Research Center, Riyadh 11211, Saudi Arabia
2 Department of Electrical and Electronics Engineering, Ege University, Bornova, Izmir 35100, Turkey

* To whom correspondence should be addressed. E-mail: asyali{at}kfshrc.edu.sa.


  Abstract

Motivation: A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from the microarray data. In this study, we applied fuzzy c-means and normal mixture modeling based classification methods to separate microarray data into reliable and unreliable signal intensity populations.

Results: We compared the results of fuzzy c-means classification with that of classification based on normal mixture modeling. Both approaches were validated against reference sets of biological data consisting of only true positives and negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. Although a comparison of the computation times indicated that the fuzzy approach is computationally more efficient, other considerations support the use of normal mixture modeling for the reliability analysis of microarray data.

Availability: The classification approaches described in this paper and sample microarray data is available as MatlabTM (The MathWorks Inc., Natick, MA) programs (mfiles) and text files respectively at the following URL: http://rc.kfshrc.edu.sa/bssc/staff/MusaAsyali/Downloads.asp. The programs can be run/tested on many different computer platforms where Matlab is available.


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