Composer Agents for Bioinformatics

Overview

This repository aims to build effective "composer" agents for bioinformatics, particularly in the domains of single-cell and spatial single-cell analysis within neurobiology and cancer research.

The concept of a "composer" agent is to assemble pieces (code snippets, domain knowledge, policies) from a catalogue to generate an integrated bioinformatics agent capable of:

• Loading and preprocessing data (e.g., scRNA-seq, spatial transcriptomics)
• Performing standard QC and filtering
• Running downstream analyses (clustering, dimensional reduction, cell type annotation)
• Executing specialized tasks (like spatial deconvolution, cell-cell interaction analysis, RNA velocity, batch correction, etc.)

By customizing different catalogue entries for specific tasks, species, or data types, the composer can produce a final agent with all the necessary instructions and domain knowledge to tackle a specific user request or workflow.

The Problem

Standardizing bioinformatics workflows for single-cell and spatial single-cell data analysis can be time-consuming and error-prone, especially when integrating multiple datasets, applying new tools, or dealing with specialized biological questions (neurobiology, oncology, etc.).

Typical challenges include:

1. Selecting consistent code snippets or libraries for each analysis step.
2. Incorporating relevant domain knowledge to interpret results (e.g., neuron-specific markers, cancer pathways).
3. Enforcing agent policies (e.g., code-only responses, confidentiality, HPC usage).
4. Handling batch effects, cell type annotations, or multi-sample integration with reproducible pipelines.

Solution: Our composer agent dynamically stitches appropriate catalogue items (policies, domain knowledge, code templates, etc.) to generate a complete and specialized agent. This allows a flexible, modular approach to constructing analysis workflows.

Inputs

1. Agent Catalogue:

   • A JSON-based repository of code snippets, domain knowledge, policies, specialized functions for single-cell/spatial analysis, etc.
   • Each catalogue item contains a title (hinting at its purpose) and text (the actual content injected into the final system prompt).

2. User/Planner Instructions:

   • A higher-level plan or user instructions specifying which pieces to assemble, in what order, and any specialized parameters (e.g., choosing cancer vs. neuro domain, using scRNA-seq vs. scATAC-seq modules).

3. Data Context (optional):

   • File paths or data references (e.g., .h5ad, .mtx, coordinates) to inform the composer how to load and process the dataset.

Outputs

1. Agent Prompt (System Prompt for the Final Agent):

   • A consolidated prompt that merges:
     • The relevant policies (professional conduct, code rules, HPC usage, etc.).
     • Domain-specific knowledge (markers, workflows, interpretive context for neuro or cancer).
     • Step-by-step function definitions or code snippets needed to complete the analysis.
   • This final prompt is automatically generated by the composer, ensuring internal consistency and adherence to the provided plan.

2. Executable Bioinformatics Agent:

   • Once the final prompt is deployed into a code-capable environment (e.g., a Jupyter kernel), it executes each step, loading data, performing QC, clustering, and more.
   • The agent also returns interpretations of each step’s outputs (e.g., images, stats, differential expression results).

How to Use

1. Review the Catalogue: Examine the available pieces. Decide which are needed for your specific single-cell or spatial data task (e.g., Load Data, QC, Spatial Deconvolution, etc.).
2. Assemble: Combine the chosen pieces into a final agent prompt. This is done via the composer logic, which merges the text from each relevant catalogue entry.
3. Execute: Provide the final system prompt to a code execution environment. The resulting agent is now specialized to run your single-cell or spatial workflow.
4. Iterate: If the user needs additional analyses or domain expansions, insert new or updated pieces into the catalogue, then rebuild the agent.

Example Scenario

• A user has Slide-seq data for brain tissue. They want to integrate scRNA-seq data to deconvolute spot-level cell types.
• The composer selects:
  • Core Global Agent Policy
  • Spatial Data Loading
  • Neurobiology Domain Knowledge
  • Tangram Deconvolution
  • Agent Policy: Final Reporting
• The composer merges these items and injects the final text into a system prompt.
• The agent proceeds with data loading, QC, clustering, and Tangram-based mapping, returning interpretative text for each step.

Contributing

• Add new catalogue items (policies, domain knowledge, code templates) by following the existing JSON format.
• Edit or refine existing items for more precise coverage (e.g., new QC methods, domain expansions for immunotherapy, neurodegenerative diseases, etc.).
• Test your changes by assembling a new agent and running an end-to-end analysis pipeline.

License

This project is distributed under the terms of [License Name Here]. Refer to LICENSE for details.