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Home / Catalog / Single-Cell RNA-seq — Clustering, Trajectory, DE & GRN (Synthetic HB3.1)

Single-Cell RNA-seq — Clustering, Trajectory, DE & GRN (Synthetic HB3.1)

  • Task ID: biology.scrna_seq_analysis
  • Domain: biology
  • Subdomain: transcriptomics
  • Status: final
  • Benchmark set: seed42 (60 tasks)
  • Tags: scrna_seq, clustering, pseudotime, differential_expression, gene_regulatory_network, pipeline_decisions

Runtime and requirements

  • Estimated time: 45-90 minutes
  • Python: >=3.11
  • Packages: numpy, scipy
  • GPU required: no
  • Network required: no

Public input and output contract

Inputs

  • system_info.json (data): Dataset shape metadata and TF gene column indices for GRN scoring
  • counts.npy (data): Simulated cell-by-gene count matrix (Poisson-like)

Outputs

  • analysis.py (code): Runnable notebook-style pipeline implementing the requested analyses
  • clusters.npy (data): Per-cell discrete cluster assignments (any consistent labeling)
  • pseudotime.npy (data): Scalar pseudotime per cell (monotone preferred along inferred trajectory)
  • de_genes.json (data): {"gene_indices": [int,...]} ranked marker candidates across clusters
  • grn_scores.npy (data): Directed edge scores among TF genes (columns 0..n_tf_genes-1 of counts)
  • method_choices.json (data): Per-step method selection with justification: normalization, batch correction, clustering, pseudotime, DE, GRN

Public repository files

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Provenance

This task is included in the current seed42 benchmark set at dataset revision f11a199f71fb4b854ac43a1bf548d5df519141a9.

This page is generated only from files tracked in the public ASI-Bench repository at commit f18382f03faf. Submission bundles, run logs, private scoring configuration, and private reference answers are not read by this page generator.