Abstract
Microarray technology is a prominent tool that analyzes many thousands of gene expressions in a single experiment as well as to realize the primary genetic causes of various human diseases. There are abundant applications of this technology and its dataset is of high dimension and it is difficult to analyze the whole gene sets. In this paper, the SAM technique is used in a Golub microarray dataset which helps in identifying significant genes. Then the identified genes are clustered using three clustering techniques, namely, Hierarchical, k-means and Fuzzy C-means clustering algorithms. It helps in forming groups or clusters that share similar characteristics, which are useful when unknown dataset is used for analysis. From the results, it is shown that the hierarchical clustering performs well in exactly forming 27 samples in first cluster (ALL) and 11 samples in the second cluster (AML). They will provide an idea regarding the characteristics of the dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schena, M., Shalon, D., Davis, R.W., Brown, P.O.: Quantitative monitoring of gene expression patterns with a complemenary DNA microarray. Science 270, 467–470 (1995)
Southern, E.M.: DNA microarray history and overview. Methods Mol. Biol. 170, 1–15 (2001)
Chen, T.C., Hsieh, Y.C., You, P.S., Lee, Y.C.: Feature selection and classification by using grid computing based evolutionary approach for the microarray data. In: Third IEEE International Conference on Computer Science and Information Technology, pp. 85–89 (2010)
Hsu, H.H., Lu, M.D.: Feature selection for cancer classification on microarray expression data. In: Eighth International Conference on Intelligent Systems Design and Applications, pp. 153–158 (2008)
Soliman, T.H.A., Sewissy, A.A., AbdelLatif, H.: A gene selection approach for classifying diseases based on microarray datasets. In: Second International Conference on Computer Technology and Development, pp. 626–631(2010)
Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. PNAS 98(9), 5116–5121 (2001)
Schwender, H.: Siggenes: multiple testing using SAM and Efron’s empirical Bayes approaches. R package version 1.32.0
Damle, T., Kshirsagar, M.: Role of permutations in significance analysis of microarray and clustering of significant microarray gene list. IJCSI Int. J. Comput. Sci. 9, 342–344 (2012)
Maguluri, L.P., Rajapanthula, K., Srinivasu, P.N.: A comparative analysis of clustering based segmentation algorithms in microarray images. Int. J. Emerg. Sci. Eng. 1, 27–32 (2013)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Chu, G., Li, J., Narasimhan, B., Tibshirani, R., Tusher, V.: SAM significance analysis of microarrays. Users guide and technical document, Department of Biochemistry, Stanford University, Stanford CA 94305 (2002)
Bosio, M., Pujalte, P.B., Salembier, P., Oliveras-Verges, A.: Feature set enhancement via hierarchical clustering for microarray classification. In: International Workshop on Genomic Signal Processing and Statistics, pp. 226–229 (2011)
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. In: Proceeding of the National Academy of Sciences, pp. 14863–14868 (1998)
Maji, P.: Mutual information-based supervised attribute clustering for microarray sample classification. IEEE Trans. Knowl. Data Eng. 24, 127–140 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Nirmalakumari, K., Harikumar, R., Rajkumar, P. (2016). Clustering Techniques from Significance Analysis of Microarrays. In: Senthilkumar, M., Ramasamy, V., Sheen, S., Veeramani, C., Bonato, A., Batten, L. (eds) Computational Intelligence, Cyber Security and Computational Models. Advances in Intelligent Systems and Computing, vol 412. Springer, Singapore. https://doi.org/10.1007/978-981-10-0251-9_19
Download citation
DOI: https://doi.org/10.1007/978-981-10-0251-9_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0250-2
Online ISBN: 978-981-10-0251-9
eBook Packages: EngineeringEngineering (R0)