Identifying Transcriptional Pattern through Clustering Analysis of Gene Expression Data

Abstract

The outbreak of SARS-CoV-1 in 2002 followed by SARS-CoV-2 in 2019 resulted in a global health crisis, emphasizing the need to understand the molecular basis of the viral infection. This study aimed to investigate transcriptional pattern in SARS-CoV-1 through clustering analysis, using the Gene Expression Omnibus microarray data set. By applying integrated hierarchical clustering and k-medoid methods, we sought to elucidate the transcriptional patterns associated with SARS-CoV-1 infection and draw significant conclusions regarding viral pathogenesis. Our analysis revealed distinct clusters of genes with similar expression patterns, providing insights into the host's response to SARS-CoV-1 infection. Notably, key genes involved in viral replication, immune response, and host-pathogen interactions exhibited significant alterations in expression levels. Additionally, the clustering analysis unveiled subgroups within the infected samples, implying potential variations in the host response or viral strain differences. In light of these findings, we conclude that gene expression profiling with clustering analysis, offers valuable insights into the molecular dynamics of the SARS-CoV-1 outbreak. The identified transcriptional patterns enhance our understanding of the virus-host interaction and may pave the way for the identification of potential therapeutic targets. Ultimately, this research contributes to a comprehensive understanding of the pathogenesis of SARS-CoV-1 and provides a basis for future investigations into effective intervention strategies such as gene ranking and gene selection toward SARS-CoV-2.