Thirteen years of clusterProfiler

When the human genome was fully sequenced in 2003, research focus shifted to functional genomics, particularly the spatiotemporal expression of genes, which is crucial for understanding organism development, functional regulation, and disease mechanisms. A key step in this process is uncovering the biological pathways involved. The first bioinformatics tool for analyzing biological pathways using Gene Ontology (GO) was GO::TermFinder, a Perl module published in 2004 that implemented the over-representation analysis method. Shortly thereafter, in 2005, the gene set enrichment analysis (GSEA) method was introduced. Various information content-based methods for measuring semantic similarity were adapted for use with GO, and in 2007, Wang proposed a graph-based approach to measure GO semantic similarity. In 2008, I developed GOSemSim, which implemented multiple GO semantic similarity measures, including information content and graph structure algorithms. These tools, which mine biological knowledge, rely heavily on gene functional information accumulated during the Human Genome Project era.

However, these tools were primarily designed for model organisms. One of the motivations behind developing clusterProfiler was my desire to extend pathway analysis to non-model organisms. Additionally, all tools at that time were created for case-control experimental designs. I wanted to apply pathway analysis to more complex biological experiments with multiple conditions, which is the inspiration for the software’s name—it profiles biological themes across different gene clusters (Figure 1). This comparative approach to biological themes is an innovation of clusterProfiler. In the v.4.0 paper, we applied it to compare the pathways perturbed by different drugs over time. In a protocol published in 2024, we demonstrated its use in comparing microbiome and metabolome data across disease subtypes, characterizing transcription factors and their functions activated under stress at different time points and analyzing cell type enrichment in single-cell clusters.