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The conventional approach for analyzing gene expression data involvesclustering algorithms. Cluster analyses provide partitioning of the set ofgenes that can predict biological classification based on its similarity inn-dimensional space. In this study, we investigate whether network analysiswill provide an advantage over the traditional approach. We identify theadvantages and disadvantages of using the value-based and the rank-basedconstruction in creating a graph representation of the original gene-expressiondata in a time-series format. We tested four community detection algorithms,namely, the Clauset-Newman-Moore (greedy), Louvain, Leiden, and Girvan-Newmanalgorithms in predicting the 5 functional groups of genes. We used the AdjustedRand Index to assess the quality of the predicted communities with respect tothe biological classifications. We showed that Girvan-Newman outperforms the 3modularity-based algorithms in both value-based and ranked-based constructedgraphs. Moreover, we also show that when compared to the conventionalclustering algorithms such as K-means, Spectral, Birch, and Agglomerativealgorithms, we obtained a higher ARI with Girvan-Newman. This study alsoprovides a tool for graph construction, visualization, and community detectionfor further analysis of gene expression data.

Predicting the Biological Classification of Cell-Cycle Regulated Genes  of Saccharomyces cerevisiae using Community Detection Algorithms on Gene  Co-expression Networks