Abstract
It is well known that microarray printing, hybridization, and washing oftentimes create erroneous measurements, and these errors detrimentally impact machine microarray spot quality classification. Thus, it is crucial to identify and remove these errors if automation is to replace the still common practice of visually assessing spot quality, an extremely expensive and time-consuming procedure. A major problem in microarray spot quality classification methods proposed in the literature is the correlation among the features extracted from the spots. In this paper, we propose using a random subspace ensemble of neural networks and a feature selection algorithm to improve the performance of our microarray spot quality classification method. Our best method obtains an error under the receiver operating characteristic curve (EAUR) of 0.3 outperforming the stand-alone support vector machine EAUR of 1.7. The consistency of our proposed approach makes it a viable alternative to the labour-intensive manual method of spot quality assessment.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Implemented as in the Matlab PRTools 3.1.7.
Cy3 and Cy5 are reactive water-soluble fluorescent dyes of the cyanine dye family. Cy3 dyes are green (~550 nm excitation, ~570 nm emission), while Cy5 is fluorescent in the red region (~650/670 nm). For details, see http://www.jacksonimmuno.com/technical/f-cy3-5.asp.
These parameters are found with a grid search to minimize the EAUR.
Implemented as in OSU SVM Matlab Toolbox.
Radial basis function: exp(−gamma ||a–b||2).
References
Schena M, Shalon D, Davis R, Brown P (1995) Quantitative monitoring of gene expression patterns with complementary DNA microarray. Science 270:467–470
Hautaniemi S, Edgren H, Vesanen P, Wolf M, Järvinen AK, Yli-Harja O, Astola J, Kallioniemi O, Monni O (2003) A novel strategy for microarray quality control using Bayesian networks. Bioinformatics 19(16):2031–2038
Nanni L, Lumini A (2007) Ensemblator: an ensemble of classifiers for reliable classification of Biological Data. Pattern Recogn Lett 28(5):622–630
Bylesjö M, Eriksson D, Sjödin A, Sjöström M, Jansson S, Antti H, Trygg J (2005) MASQOT: a method for cDNA microarray spot quality control. BMC Bioinformatics 6:250. doi:10.1186/1471-2105-6-250
Brown C, Goodwin P, Sorger P (2001) Image metrics in the statistical analysis of DNA microarray data. Proc Natl Acad Sci USA 98:8944–8949
Wang X, Ghosh S, Guo S (2001) Quantitative quality control in microarray image processing and data acquisition. Nucleic Acids Res 29:E75
Model F, König T, Piepenbrock C, Adorján P (2002) Statistical process control for large scale microarray experiments. Bioinformatics 1:1–9
Chen Y, Kamat V, Dougherty E, Bittner M, Meltzer P, Trent J (2002) Ratio statistics of gene expression levels and applications to microarray data analysis. Bioinformatics 18:1207–1215
RuosaariS, Hollmén J (2002) Image analysis for detecting faulty spots from microarray images. In: LangeS, Satoh K, Smith CH (eds) Proceedings of the 5th international conference on discovery science (DS2002). Springer, Berlin, pp 259–266
Bicego M, Del Rosario M, Murino V (2005) A supervised data-driven approach for microarray spot quality classification. Pattern Anal Applic 8:181–187
Nanni L, Lumini A (2006) FuzzyBagging: a novel ensemble of classifiers. Pattern Recogn 39(3):488–490
Nanni L (2006) Cluster-based pattern discrimination: a novel technique for feature selection. Pattern Recogn Lett 27(6):682–687
Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844
Nanni L, Lumini A (2005) Ensemble of Parzen Window Classifiers for on-line signature verification. Neurocomputing 68:217–224
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
Pudil P, Novovicova J, Kittler J (1994) Floating search methods in feature selection. Pattern Recogn Lett 15(11):1119–1125
Kittler J, Hatef M, Duin R, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226–239
Brahnam S, Nanni L, Randall S (2007) Introduction to neonatal facial pain detection using common and advanced face classification techniques. In: Advanced computational intelligence paradigms in healthcare, vol 48, Springer Berlin, pp 225–253
Huang L, Dai Y (2005) A support vector machine approach for prediction of T cell epitopes. In: Proceedings of the third Asia-Pacific bioinformatics conference (APBC2005), Singapore, Jan 17–21, pp 312–328
Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51:181–207
Acknowledgments
The authors would like to thank S. Hautaniemi for sharing the data set.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nanni, L., Lumini, A. & Brahnam, S. Advanced machine learning techniques for microarray spot quality classification. Neural Comput & Applic 19, 471–475 (2010). https://doi.org/10.1007/s00521-010-0342-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-010-0342-3