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Challenge

Single-Cell Organoid Batch Remediation Agent with CrewAI

NIH discoveries on brain organoid developmental clocks emphasize that single-cell RNA transcriptomic datasets are highly sensitive to technical batch effects across organoid differentiation runs. In this challenge, developers will construct a CrewAI agent team (Batch Inspection Agent, Normalization Specialist, Harmonization Evaluator) that automates single-cell batch effect detection, runs Combat/Harmony integration scripts, and verifies dataset alignment.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a CrewAI multi-agent team to automate single-cell RNA-seq batch effect detection and harmonization.

Delivery guide

How work is evaluated

Evaluation

Evaluates batch effect reduction quality and silhouette score improvement.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

check_approval_status

Ensure corrected silhouette < 0.15 leads to APPROVED status

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

silhouette_reduction

Magnitude of batch silhouette score reduction post-harmonization • target: 0.35 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Configure CrewAI multi-agent collaborative workflows for bioinformatics pipelines

  • Execute Scanpy and Harmony integration tools in Python from agent tasks

  • Compute quantitative batch silhouette metrics (kBET, silhouette width)

  • Automate decision workflows for data harmonization approval

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic multi-batch organoid scRNA-seq expression tables exhibiting engineered technical batch variation.

Challenge setup

Evaluates batch effect reduction quality and silhouette score improvement.

Challenge input

JSON containing batch1_id, batch2_id, raw_batch_silhouette

CrewAI

Mandatory CrewAI framework

Evaluated output

JSON containing corrected_silhouette (float), harmonization_status (APPROVED/REJECTED)

Evaluation checks
  • Ensure corrected silhouette < 0.15 leads to APPROVED status
  • Magnitude of batch silhouette score reduction post-harmonization • target: 0.35 • range: 0-1
Configured evaluation evidence
  • Benchmark: scHarmonize-Bench
  • Silhouette Reduction target: 0.35
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • CrewAI · Required
  • crewAI · Optional
  • Optuna · Optional

Evaluation contract

  • check_approval_status · Weight 1
  • silhouette_reduction · Weight 1

Metrics

  • silhouette_reduction · Target 0.35

Benchmarks

  • scHarmonize-Bench
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing batch1_id, batch2_id, raw_batch_silhouette

    Output: JSON containing corrected_silhouette (float), harmonization_status (APPROVED/REJECTED)

    Execution harness: Python

Evaluation tools

  • CrewAI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Single-Cell Organoid Batch Remediation Agent with CrewAI