CATO is an AI data analysis and literature review tool for biomedical researchers, built by NYRUS AI. Differential expression is a natural fit: the methods are standardized, the tooling is in R and Python, and the figures follow conventions reviewers recognize.
The workflow
- Upload a counts matrix and sample metadata (CSV, TSV, or HDF5 family formats).
- Ask for differential expression between conditions; CATO runs established methods from its 200+ bundled scientific libraries and reports fold changes, p-values, and multiple-testing correction.
- Request volcano plots, heatmaps of top genes, and pathway enrichment, each delivered as an editable figure with its code.
Beyond the defaults
Batch effects, paired designs, and covariate adjustment are handled by asking in plain language. The assumptions and model formulas appear next to results, so the bioinformatics core can verify instead of redoing.
You can try CATO at cato.nyrus.ai, or learn more at nyrus.ai/cato.