Datasets:

hansen7
/

spatialagent_human

id int64 1 8	cell_type_workflow stringclasses 8 values	niche_workflow stringclasses 6 values
1	Annotated based on gene co-expression patterns.	null
2	Leiden clustering with majority voting for consensus-based cell-type annotation; provided 3 tiers of annotation.	Labeled niches from cell annotations with clear distributions (e.g. Atrium, Ventricular) for tier 1; considered spatial left/right position for tier 2.
3	Unknown.	Unknown, likely mis-ordered annotations.
4	Leiden clustering and analyzed predefined marker genes in clusters; typically a single marker gene to differentiate cell types; projected cell types spatially and used position for final annotation.	Used UTAG for spatial clustering; labeled structures based on position and provided anatomical image.
5	Leiden clustering and analyzed expression of predefined marker genes in clusters; multiple genes per cell type; mapped both major cell type and subtypes.	Used UTAG for spatial clustering; labeled structures based on position, provided anatomical image and additional sources.
6	Label transfer using TACCO with an scRNA-seq reference of human heart; projected cell types spatially; Leiden clustering and DEG for marker genes, using key markers for second-tier annotation.	Used UTAG for spatial clustering; labeled structures based on position and provided anatomical image.
7	Leiden clustering with manual annotation using marker gene sets and DEG (per Scanpy tutorial); projected cell types spatially; used spatial position and key marker expression for final annotation; provided 3-tier annotation.	Used UTAG for spatial clustering; labeled structures based on position and anatomical knowledge of heart (e.g. 'chamber wall is thicker on the left ventricle').
8	Combined annotation on Leiden clusters with CellTypist-transferred labels as reference.	Used UTAG for spatial clustering.

SpatialAgent — Human Expert Reference Data

Anonymized reference data produced by human scientists for two spatial-transcriptomics tasks used to benchmark SpatialAgent:

Gene panel design — expert-designed targeted gene panels for the human dorsolateral prefrontal cortex (DLPFC / PFC).
Cell-type & tissue-niche annotation — expert annotations of a developing human heart MERFISH dataset (228,633 cells × 238 genes).

All scientist identities are removed. Each task uses its own independent numbering, so the same person generally has a different id in the two tasks (this is intentional — the two studies were anonymized separately). No real names appear anywhere in this repository. Each expert's methodology is documented (by anonymized id) in the workflows.csv files.

Repository layout

panel_design/
  workflows.csv              # id (1–10) -> free-text description of the panel-design approach
  {1..10}.csv                # one full panel per expert (ranked gene lists)
  split/{id}_top{50,100,150}.csv   # top-N subsets of each panel
annotation/
  workflows.csv              # id (1–8) -> cell-type & niche annotation approach
  combined_annotations_anonymized.h5ad   # all experts (anonymized) + model/baseline predictions
  human_annotations_anonymized.h5ad       # human experts only (anonymized), no model columns
  per_expert_raw/            # the original per-expert annotation files, anonymized
    expert{1,2,5,6,7}.h5ad
    expert{3,4}.csv
    expert7_niche.h5ad

See panel_design/README.md and annotation/README.md for the column-level details of each subset.

Panel design (DLPFC)

10 experts each submitted a ranked panel (typically top 50 / 100 / 150 genes) with a short rationale per gene. Formats are heterogeneous (experts used different tools), so columns differ between files; the common fields are a gene symbol, a ranking/priority, and a free-text reasoning column. split/ holds the top-50/100/150 truncations used for size-matched evaluation. Workflows range from purely algorithmic (Persist, greedy kNN reconstruction) to literature-driven marker curation — see panel_design/workflows.csv.

Annotation (developing human heart, MERFISH)

8 experts annotated the same 228,633 cells. The two combined .h5ad objects share an identical cell index and embeddings:

X — log1p-normalized expression (238 genes); layers['raw_count'] — raw counts.
obsm — X_pca, X_umap, spatial (tissue coordinates).
Per-expert columns: cell_type_tier{1,2,3}_expert{N}, tissue_niche_tier{1,2}_expert{N}, and consolidated cell_type_expert{N} / tissue_niche_expert{N}.
Consensus reference labels: cell_type, tissue_niche.

combined_annotations_anonymized.h5ad additionally contains model / baseline predictions (cell_type_agent, tissue_niche_agent, cell_type_gpt, cell_type_sctab, cell_type_popv, cell_type_biomni_run_{1,2,3}, cell_type_spatialagent_run_4) for direct benchmarking; human_annotations_anonymized.h5ad is the human-only subset (those columns dropped). per_expert_raw/ preserves each expert's original file (with their native, heterogeneous column schema) for full transparency.

Caveats

annotation expert 1 did not produce tissue-niche labels (niche fields are empty/NA).
annotation expert 3's labels are of uncertain origin and are likely mis-ordered — use with care.
annotation expert 8 has no standalone raw file; their annotations exist only inside the combined objects.
panel expert 3 submitted a previously designed panel for the wrong tissue.

License & citation

Released under CC-BY-4.0 (adjust if your venue requires otherwise). If you use this data, please cite the SpatialAgent paper. The two workflows.csv files correspond to the Extended Data tables describing human-scientist workflows.

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