Hi Picasa,

To summarise and visualize your ORA results while highlighting shared pathways across datasets, start with heatmaps that list pathways as rows and datasets as columns, colouring cells by metrics like -log10(p-value) or fold enrichment—this clusters similar pathways and reveals overlaps at a glance, while keeping dataset distinctions clear. For scalability to more datasets, supplement with UpSet plots to depict intersections of enriched pathways, then use faceted dotplots or bubble plots to drill down into per-dataset details like gene counts or adjusted p-values.

The clusterProfiler package in R shines here; its compareCluster function takes multiple gene lists as input and outputs ready-made comparative visuals like dotplots or heatmaps that emphasize consistencies and differences. For integrative analysis across datasets, try the ActivePathways package, which prioritizes shared pathways via data fusion and includes network-based plots.

Helpful papers include Paczkowska et al. (2020) in Nature Communications, which details ActivePathways for multi-omics pathway integration, and Reimand et al. (2019) in Nucleic Acids Research, showcasing enrichment maps in g:Profiler for visualizing overlaps in Cytoscape.

Best,
Kevin