scPilot enables LLMs to reason over single-cell RNA-seq data using natural language and on-demand tools for annotation, trajectories, and TF targeting. Paired with scBench benchmark, it shows gains like 11% accuracy lift via iterative reasoning. Transparent traces explain biological insights.
Key Points
- 1.LLMs reason over single-cell RNA-seq data via natural language and on-demand tools
- 2.11% accuracy lift on scBench benchmark using iterative reasoning
- 3.Transparent traces explain underlying biological insights
Impact Analysis
Bioinformaticians and biologists benefit from automated single-cell analysis, reducing manual effort in annotation, trajectories, and TF targeting. It lowers barriers for non-experts to derive insights from complex RNA-seq data using LLMs. Paired scBench standardizes LLM evaluation, fostering rapid advancements in AI-driven genomics research.
Technical Details
scPilot equips LLMs with natural language interfaces and specialized tools for tasks like cell annotation, trajectory inference, and transcription factor targeting. Iterative reasoning refines outputs, yielding measurable gains on the scBench benchmark. Execution traces provide interpretable step-by-step explanations of biological conclusions.