Single-Cell RNA-seq — Clustering, Trajectory, DE & GRN (Synthetic HB3.1)¶
- Task ID:
biology.scrna_seq_analysis - Domain:
biology - Subdomain:
transcriptomics - Status:
test - Tags:
scrna_seq,clustering,pseudotime,differential_expression,gene_regulatory_network,pipeline_decisions
Public Summary¶
This page is generated from task metadata and selected public-safe excerpts.
Example B1 Prompt Excerpt¶
# Single-cell RNA-seq pipeline (synthetic counts)
> **Level B1**: Full pipeline specification — HB3.1-v0.5 workflow.
## Problem
You receive a **simulated single-cell RNA-seq count matrix** (`data/counts.npy`) plus minimal metadata (`data/system_info.json`). The data contain:
1. **TF genes** — columns `0 .. n_tf_genes-1` share an underlying sparse directed regulatory structure.
2. **Non-TF genes** — the remaining columns contain marker genes and other gene classes whose column indices are **randomly shuffled**. No biological identities are disclosed.
Your job is to implement a **single coherent pipeline** and export the artifacts listed below.
## Standard workflow (follow this structure)
Notes¶
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- Higher-level prompt details and internal benchmark specifics may remain intentionally undisclosed.