{"title":"CDCA: Community detection in RNA-seq data using centrality-based approach","authors":"Tonmoya Sarmah, Dhruba K Bhattacharyya","doi":"10.1007/s12038-024-00437-8","DOIUrl":null,"url":null,"abstract":"<p>One of the integral part of the network analysis is finding groups of nodes that exhibit similar properties. Community detection techniques are a popular choice to find such groups or communities within a network and it relies on graph-based methods to achieve this goal. Finding communities in biological networks such as gene co-expression networks are particularly important to find groups of genes where we can focus on further downstream analysis and find valuable insights regarding concerned diseases. Here, we present an effective community detection method called community detection using centrality-based approach (CDCA), designed using the graph centrality approach. The method has been tested using four benchmark bulk RNA-seq datasets for schizophrenia and bipolar disorder, and the performance has been proved superior in comparison to several other counterparts. The quality of communities are determined using intrinsic graph properties such as modularity and homogeneity. The biological significance of resultant communities is decided using the pathway enrichment analysis.</p>","PeriodicalId":15171,"journal":{"name":"Journal of Biosciences","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biosciences","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s12038-024-00437-8","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
One of the integral part of the network analysis is finding groups of nodes that exhibit similar properties. Community detection techniques are a popular choice to find such groups or communities within a network and it relies on graph-based methods to achieve this goal. Finding communities in biological networks such as gene co-expression networks are particularly important to find groups of genes where we can focus on further downstream analysis and find valuable insights regarding concerned diseases. Here, we present an effective community detection method called community detection using centrality-based approach (CDCA), designed using the graph centrality approach. The method has been tested using four benchmark bulk RNA-seq datasets for schizophrenia and bipolar disorder, and the performance has been proved superior in comparison to several other counterparts. The quality of communities are determined using intrinsic graph properties such as modularity and homogeneity. The biological significance of resultant communities is decided using the pathway enrichment analysis.
期刊介绍:
The Journal of Biosciences is a quarterly journal published by the Indian Academy of Sciences, Bangalore. It covers all areas of Biology and is the premier journal in the country within its scope. It is indexed in Current Contents and other standard Biological and Medical databases. The Journal of Biosciences began in 1934 as the Proceedings of the Indian Academy of Sciences (Section B). This continued until 1978 when it was split into three parts : Proceedings-Animal Sciences, Proceedings-Plant Sciences and Proceedings-Experimental Biology. Proceedings-Experimental Biology was renamed Journal of Biosciences in 1979; and in 1991, Proceedings-Animal Sciences and Proceedings-Plant Sciences merged with it.