Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-10-40
Title: NIFTI: An evolutionary approach for finding number of clusters in microarray data
Authors: Jonnalagadda, S.
Srinivasan, R. 
Issue Date: 30-Jan-2009
Source: Jonnalagadda, S., Srinivasan, R. (2009-01-30). NIFTI: An evolutionary approach for finding number of clusters in microarray data. BMC Bioinformatics 10 : -. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-10-40
Abstract: Background: Clustering techniques are routinely used in gene expression data analysis to organize the massive data. Clustering techniques arrange a large number of genes or assays into a few clusters while maximizing the intra-cluster similarity and inter-cluster separation. While clustering of genes facilitates learning the functions of un-characterized genes using their association with known genes, clustering of assays reveals the disease stages and subtypes. Many clustering algorithms require the user to specify the number of clusters a priori. A wrong specification of number of clusters generally leads to either failure to detect novel clusters (disease subtypes) or unnecessary splitting of natural clusters. Results: We have developed anovel method to find the number of clusters in gene expression data. Our procedure evaluates different partitions (each with different number of clusters) from the clustering algorithm and finds the partition that best describes the data. In contrast to the existing methods that evaluate the partitions independently, our procedure considers the dynamic rearrangement of cluster members when a new cluster is added. Partition quality is measured based on a new index called Net InFormation Transfer Index (NIFTI) that measures the information change when an additional cluster is introduced. Information content of a partition increases when clusters do not intersect and decreases if they are not clearly separated. A partition with the highest Total Information Content (TIC) is selected as the optimal one. We illustrate our method using four publicly available microarray datasets. Conclusion: In all four case studies, the proposed method correctly identified the number of clusters and performs better than other well known methods. Our method also showed invariance to the clustering techniques. © 2009 Jonnalagadda and Srinivasan; licensee BioMed Central Ltd.
Source Title: BMC Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/64291
ISSN: 14712105
DOI: 10.1186/1471-2105-10-40
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