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.

Gene expression analysis is critical to find out important genes associated with diseases. Computational methods such as differential expression analysis and various gene network analyses are used to find such genes which are expressed differently in a diseased sample compared to a control sample (sample without the disease). Such studies have helped understand the genetic causes of various critical diseases including neuro-psychiatric disorders. We have focused our work on functionally related genes associated with Schizophrenia as there is still much to be unearthed about the genetic causes that leads to this disorder. In this paper, we have used a combination of different computational methods to analyze bulk-RNA Seq data for schizophrenia to find pairs of functionally related genes that contribute to its occurrence. We are able to find pairs of genes that have been associated with the target disease along with other pairs which are yet to be established as important genes for the disease pathogenesis.

The process of community detection in a network uncovers groups of closely connected nodes, known as communities. In the context of gene correlation networks and neuro-degenerative diseases, this study introduces a systematic pipeline for centrality-based community detection using scRNA-Seq data. Comparisons with existing methods demonstrate its superior performance in terms of modularity. Furthermore, the resulting communities undergo biological validation and hub-gene analysis, which reveal disease-specific pathways and gene ontology associated with the genes within these communities.

A number of methods are being developed and used for analysis of gene expression data such as RNA-Seq data. Most of these tools focus on finding genes that are responsible for the disease conditions. Methods such as co-expression network generation, module detection and differential co-expression analysis are used to look into specific changes in the gene expression data among different conditions. In this paper, a comparative study of four differential co-expression analysis tools are presented, namely, WGCNA, DiffCorr, MODA and CEMiTool, for RNA-Seq data. The different methods used by these tools are studied and tested on schizophrenia and bipolar disorder datasets and their effectiveness in finding the related differentially co-expressed genes and pathways are being discussed. The relevancy of the resultant genes and pathways are decided on the basis of whether the genes and pathways are associated with the given disease conditions.