lisaR: An LLM-Inferred Semantic Annotation of biological categories for gene set enrichment analysis

Gene set enrichment analysis (GSEA) turns differential expression results into lists of enriched gene sets. These lists are often long and redundant and span several gene-set collections, which makes their biological interpretation difficult. We present lisaR, an R package that reads previously computed differential expression or differential abundance results, runs GSEA and organises the output with the LISA dictionaries (LLM-Inferred Semantic Annotation). The dictionaries place 13,511 gene sets from the three Gene Ontology branches and from curated pathway collections such as Reactome, WikiPathways, KEGG, BioCarta and PID in 158 biological categories grouped into 42 supercategories within four dictionaries. Four open-weight language models scored the semantic fit of each gene set to the categories, and a consensus rule selected the assignments. The dictionaries are distributed with the package and do not depend on the data: studies analysed at different times with the same dictionaries are interpreted with the same categories, and no language model is queried during an analysis. For each category, lisaR summarises the direction of its significant gene sets and applies closed testing with Hommel's robust procedure, which states whether the category shows evidence of enrichment and gives a simultaneous lower bound on how many of its gene sets are enriched. lisaR results are summarised in a report that opens in a web browser and leads from each category to its gene sets and genes, with every figure saved together with its data and R code. We illustrate the workflow with RNA-seq from melanoma biopsies taken before and during nivolumab treatment and with paired tumour and adjacent-tissue proteomics from clear cell renal cell carcinoma. Availability: lisaR is available at https://github.com/DBM-OlmedaLab/lisaR and archived at https://doi.org/10.5281/zenodo.23044930; documentation at https://olmedalab.org/lisaR/.