High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics - clustering gene expression data - to the operations research community.
Persistent homology, a topological data analysis (TDA) method, is applied to microarray data sets. Although there are a few papers referring to TDA methods in microarray analysis, the usage of persistent homology in the comparison of several weighted gene coexpression networks (WGCN) was not employed before to the very best of our knowledge. We calculate the persistent homology of weighted networks constructed from 38 Arabidopsis microarray data sets to test the relevance and the success of this approach in distinguishing the stress factors. We quantify multiscale topological features of each network using persistent homology and apply a hierarchical clustering algorithm to the distance matrix whose entries are pairwise bottleneck distance between the networks. The immunoresponses to different stress factors are distinguishable by our method. The networks of similar immunoresponses are found to be close with respect to bottleneck distance indicating the similar topological features of WGCNs. This computationally efficient technique analyzing networks provides a quick test for advanced studies.
Biological networks, social networks, and the World Wide Web are some examples of real world networks exhibiting community structure. We present a concise review of community structure finding (CSF) algorithms and applications. We apply a CSF algorithm and various other algorithms on three different microarray data sets. We calculate modularity and C-rand indices as an indication of the quality of each clustering of the three data sets. We compare the performance of the CSF algorithm with the performance of three other algorithms: hierarchical clustering (HC) algorithm, K-means, dynamic tree cut (DTC) algorithm and Naive Bayes Clustering (NBC) using both C-rand and modularity values.We report that the CSF algorithm detects clusters resulting in high modularity; however the CSF does not result in clusters with high C-rand values compared to the other methods.
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