CATO is an AI data analysis and literature review tool for biomedical researchers, built by NYRUS AI. Single-cell analysis is where conversational workflows shine, because the standard pipeline has many steps and each one has defensible defaults worth questioning.
Supported inputs
H5AD and Loom files, 10x MTX matrices, and HDF5 containers load directly; no conversion scripts required.
The pipeline, conversationally
- Quality control: filter cells and genes with stated thresholds.
- Normalization, dimensionality reduction, and clustering with the standard single-cell toolchain.
- Marker genes and cluster annotation, then differential expression between conditions.
- UMAP, feature, and violin figures, each editable and each carrying its code.
Why transparency matters here
Single-cell results are sensitive to thresholds. Because every step's parameters and code are visible, a bioinformatician can audit the pipeline instead of rerunning it from scratch.
You can try CATO at cato.nyrus.ai, or learn more at nyrus.ai/cato.