From Single-Cell Data
To Scientific Discovery

Isaac doesn't just run the analysis — it tells you what it means. Probabilistic cell type annotation, biological hypothesis generation, and plain-language mechanistic summaries at every decision point.

A complete scRNA-seq analysis pipeline that used to require weeks of iterative coding runs end-to-end in hours. No scripts to debug, no packages to install, no environment conflicts.

Designed by Texas Medical Center scientists who have personally run these pipelines for over 5 years across MGH/Harvard, BCM, and Rice University. Every decision reflects real laboratory workflows.

Every figure exports at journal quality. The mechanism report generates a complete methods section, proposed figure panels, and a statistical summary table ready to drop into a manuscript.

Isaac democratizes single-cell analysis for the entire biomedical research community — from basic science labs to clinical research groups to pharmaceutical R&D teams — without requiring dedicated computational staff.

The only platform that integrates clustering, DEG, pathway, trajectory, and ligand-receptor analysis into a single coherent mechanistic narrative — from healthy state to disease to treatment response.