Enterprise AI Analysis
An agentic AI framework for ingestion and standardization of single-cell RNA-seq data analysis
This paper introduces CellAtria, an agentic AI system designed to automate the entire lifecycle of single-cell RNA sequencing (scRNA-seq) data reuse, from literature parsing and metadata extraction to standardized downstream analysis. By integrating a large language model (LLM) with a modular toolchain and a companion pipeline called CellExpress, CellAtria aims to make complex biomedical workflows accessible to bench scientists, reduce manual effort, and ensure reproducible, scientifically sound analyses. It demonstrates capabilities in literature-driven data acquisition, PDF parsing, dataset retrieval, and full end-to-end scRNA-seq processing, significantly reducing turnaround time compared to traditional manual methods.
Quantifiable Impact & Efficiency Gains
CellAtria delivers tangible improvements in scientific workflow efficiency and reliability, transforming complex bioinformatics tasks into streamlined, automated processes.
~15h
Reduction in Manual Hours (per study)
<10 min
Analysis Turnaround Time
~0.10
Output Consistency (Gini Coefficient)
Deep Analysis & Enterprise Applications
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Agentic AI Framework
CellExpress Pipeline
Key Achievements
CellAtria is an agentic AI system integrating a large language model (LLM) with a tool-execution framework. It enables dialogue-driven, document-to-analysis automation through a chatbot interface. This architecture orchestrates the full lifecycle of data reuse in single-cell research, moving beyond ad-hoc code generation by leveraging pre-vetted, robust analytical tools for scientific rigor and reproducibility.
CellExpress is a co-developed pipeline integrated with CellAtria, applying state-of-the-art scRNA-seq processing steps. It transforms raw count matrices into analysis-ready single-cell profiles, ensuring standardized, reproducible analyses. The pipeline is fully customizable and includes steps for project setup, quality control, normalization, batch correction, dimensionality reduction, clustering, and cell type annotation.
CellAtria successfully demonstrated literature-driven data acquisition, processing of local PDF files, and retrieval of pre-identified datasets from public databases. It achieved full-lifecycle document-to-analysis execution in under 10 minutes for complex workflows, a stark contrast to the ~15 cumulative hours typically required by manual bioinformatics. The system also supports scalable performance across multi-study compendia, handling diverse datasets with consistent output and computational efficiency.
~15h
Manual Hours Saved Per Study
Enterprise Process Flow
Document Parsing
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Accession Resolution
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Dataset Retrieval
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File & Data Organization
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Pipeline Configuration
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CellExpress Execution
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Standardized Dataset
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Rapid Analysis of Longitudinal scRNA-seq Data
CellAtria was used to analyze a longitudinal scRNA-seq study of immune responses in 2-month-old infants following vaccination. The system autonomously parsed the article URL, extracted metadata, retrieved datasets, and executed the CellExpress pipeline, completing the entire workflow in under 10 minutes. This demonstrated its ability to transform literature-based inputs into fully processed datasets with minimal user intervention and high efficiency.
Calculate Your Potential ROI
Estimate the time and cost savings your organization could achieve by automating single-cell data analysis with Agentic AI.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your Path to Agentic AI Integration
A phased approach to seamlessly integrate CellAtria into your existing research infrastructure and maximize its impact.
Phase 1: Discovery & Integration
Assess current workflows, identify key integration points, and configure CellAtria with existing data sources and repositories. Establish initial metadata schemas.
Phase 2: Customization & Validation
Tailor CellExpress pipeline parameters to specific institutional conventions. Perform pilot runs and validate outputs against known benchmarks to ensure accuracy and consistency.
Phase 3: Training & Rollout
Conduct user training for bench scientists and researchers. Implement the system across target departments, providing ongoing support and performance monitoring.
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