- clusterProfiler has become a widely used tool across biomedical research due to its ability to translate gene- or protein-level data into functional biological insights. By performing enrichment analysis and providing rich visualizations, it allows researchers to move beyond raw differential expression results and identify the pathways, processes, and molecular functions underlying complex biological phenomena. Its real-world applications span diverse research areas, from basic biology to translational medicine.
- One of the most common applications of clusterProfiler is in cancer research. High-throughput studies such as RNA-seq or proteomics often yield long lists of differentially expressed genes that are difficult to interpret in isolation. clusterProfiler enables oncologists and molecular biologists to identify dysregulated pathways such as cell cycle control, DNA repair, apoptosis, and oncogenic signaling. For instance, it has been used to discover enrichment of Wnt/β-catenin or PI3K-Akt signaling pathways in specific tumor subtypes, shedding light on mechanisms of tumorigenesis and highlighting potential therapeutic targets.
- Another major application is in immunology and infectious disease research. Researchers studying host–pathogen interactions often use clusterProfiler to identify immune-related pathways enriched during infection or immune stimulation. For example, enrichment analysis may reveal activation of Toll-like receptor signaling, antigen presentation, or cytokine–cytokine receptor interactions in response to bacterial or viral infections. Similarly, in vaccine research, clusterProfiler helps track immune pathways modulated by vaccination, providing valuable insights into efficacy and mechanisms of protection.
- In drug discovery and pharmacology, clusterProfiler is increasingly used to explore the molecular effects of treatments. By comparing gene expression profiles before and after drug administration, researchers can identify enriched pathways that indicate drug mechanism of action or off-target effects. For example, in cancer pharmacogenomics, clusterProfiler has helped identify enrichment of apoptotic and metabolic pathways following chemotherapy or targeted drug treatment, guiding drug development and repurposing efforts.
- In developmental biology, clusterProfiler is applied to transcriptomic datasets across developmental stages or cell differentiation processes. It helps uncover the functional programs that drive transitions, such as enrichment of cell adhesion, morphogenesis, or lineage-specific signaling pathways. This has been valuable in stem cell research, where understanding differentiation trajectories is crucial for regenerative medicine applications.
- Another important use case is in comparative and evolutionary genomics. Researchers analyzing data from different species or conditions often use clusterProfiler to compare enrichment patterns, providing insights into conserved and divergent biological processes. For example, cross-species analyses of immune responses or stress tolerance often reveal conserved signaling pathways, while highlighting unique adaptations in specific organisms.
- Overall, clusterProfiler has proven indispensable for functional interpretation of omics data across a wide range of disciplines. Its ability to integrate with diverse annotation databases, combined with user-friendly visualization tools, makes it a cornerstone for studies aiming to move from raw data to biological meaning. Whether in cancer, immunology, pharmacology, or basic biology, clusterProfiler continues to empower discoveries that link molecular data to system-level understanding.
