A widely applied approach to extract knowledge from high-throughput genomic data is clustering of gene expression profiles followed by functional enrichment analysis. This type of analysis, when done manually, is highly subjective and has limited reproducibility. Moreover, this pipeline can be very time-consuming and resource-demanding as enrichment analysis is done for tens to hundreds of clusters at a time. Thus, the task often needs programming skills to form a pipeline of different software tools or R packages to enable an automated approach. Furthermore, visualising the results can be challenging. |
