Skip to main content
Loading navigation
Back to challenges

Implement a Single-Cell Epigenomic Cell-State Triage with Scanpy and AnnData

Identifying drug-resistant tumor sub-populations requires multi-omic integration of single-cell RNA and DNA methylation assays. Construct a single-cell multi-omic clustering engine using Scanpy and AnnData to isolate resistant tumor cell sub-clusters with Adjusted Rand Index (ARI) > 0.82.

Data ScienceHosted by Vera
Sign in free to start

What you are building

Single-cell multi-omic cell state triage engine built with Scanpy and AnnData matrix manipulation toolkit.

How work is evaluated

Evaluation

Evaluates single-cell cluster fidelity using Adjusted Rand Index (ARI) on multi-omic single-cell matrices.

Datasets

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

How submissions are scored

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

resistant_fraction_validity

Ensures fraction is between 0 and 1.

Binary check

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

Dimension 2

adjusted_rand_index

ARI clustering accuracy metric against known cell state labels. • target: 0.82 • 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

  • Incorporate multi-modal single-cell data (scRNA-seq + scMethylation) into AnnData objects.

  • Perform dimensionality reduction (PCA, UMAP) and Leiden community clustering using Scanpy.

  • Isolate drug-resistant single-cell states based on biomarker expression thresholds.

Resources

Dataset notes

Dataset of 1,000 synthetic single-cell multi-omic profiles representing responsive and resistant tumor sub-populations.

Challenge setup

Evaluates single-cell cluster fidelity using Adjusted Rand Index (ARI) on multi-omic single-cell matrices.

Challenge input

Path to AnnData h5ad file or matrix dict

Scanpy

Toolkit for single-cell gene expression analysis.

AnnData

Data structure format for annotated data matrices.

Evaluated output

JSON containing cluster_assignments array and detected_resistant_fraction

Evaluation checks
  • Ensures fraction is between 0 and 1.
  • ARI clustering accuracy metric against known cell state labels. • target: 0.82 • range: 0-1
Configured evaluation evidence
  • Benchmark: scRNA-Bench
  • Adjusted Rand Index target: 0.82
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Exa · Required
  • Anyscale · Optional
Policy Serving
  • GPT-4o · Optional

Evaluation contract

  • resistant_fraction_validity · Weight 1
  • adjusted_rand_index · Weight 1

Metrics

  • adjusted_rand_index · Target 0.82

Benchmarks

  • scRNA-Bench
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: Path to AnnData h5ad file or matrix dict

    Output: JSON containing cluster_assignments array and detected_resistant_fraction

    Execution harness: Python

Evaluation tools

  • Scanpy · data_analysis
  • AnnData · data_structure

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Implement a Single-Cell Epigenomic Cell-State Triage with Scanpy and AnnData