Abstract
Community detection is an important task in social network analysis, and communities in network usually appear interrelated feature. Meanwhile, there exist many communities in biological network and module network inference is an effective and established method to analyze the gene expression data. Hence, exploring and analyzing the interaction between modules or independent modules will improve our understanding of cancer mechanism. To this end, we proposed a novel method, InFun, for reconstructing different module networks. Our method applies two-ways clustering which contains Bayesian approach and overlapping community method to detect gene communities via combing gene omics data and prior information between proteins. On the basis of the cancer genome Atlas breast cancer and colon cancer data, we observe that InFun can recognize module networks which are significantly more enriched in the known pathways than another method like Lemon-tree. These gene communities can serve as bio-markers to estimate the survival time of patients which is critical for cancer therapy. Discovering single function communities can predict breast cancer subtype by using different feature sets, and multiple function communities can communicate with other community which can be used to explain cancer processes. InFun brings new sight for understanding cancer mechanism and novel technique for clustering genes.
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Lu, X., Zhu, Z., Peng, X. et al. InFun: a community detection method to detect overlapping gene communities in biological network. SIViP 15, 681–686 (2021). https://doi.org/10.1007/s11760-020-01638-y
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DOI: https://doi.org/10.1007/s11760-020-01638-y