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

Bioinformatics
Musa H Asyali, Musa Alci

Abstract

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, the elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from microarray data. In this study, we applied fuzzy c-means (FCM) and normal mixture modeling (NMM) based classification methods to separate microarray data into reliable and unreliable signal intensity populations. We compared the results of FCM classification with those of classification based on NMM. Both approaches were validated against reference sets of biological data consisting of only true positives and true 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 NMM for the reliability analysis of microarray data. The classification approaches described in this paper and sample microarray data are available as Matlab( TM ) (The MathWorks Inc., ...Continue Reading

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Citations

Jul 15, 2006·Nucleic Acids Research·Asim S SiddiquiMarco A Marra
Jun 24, 2009·BMC Bioinformatics·Brian E HowardSteffen Heber
Feb 18, 2006·BMC Bioinformatics·Daniel BerrarWerner Dubitzky
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Aug 23, 2018·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Ali Yousefian-Jazi, Jinwook Choi
Mar 24, 2006·Physiological Genomics·Joseph S VerducciChandan K Sen
Sep 10, 2008·Yi chuan = Hereditas·De-Wu YangJing Wang
Nov 26, 2008·Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine·Lixin HanHong Yan
Mar 1, 2008·Journal of Signal Processing Systems·Yu-Ping WangWei-Wen Cai

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