Dataset subset

BC (Chen et al. 2026)

BC spatial dataset (Visium breast cancer HBCA1 per Methods), as preprocessed by Chen et al. 2026.

Evaluation results

11 evaluations · 33 results. Different protocols are not a single leaderboard.

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: CellPLM (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.16 Adjusted rand index unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'CellPLM', dataset 'BC', column 'ARI'
Configuration: CellPLM (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.31 Normalized mutual information unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'CellPLM', dataset 'BC', column 'NMI'
Configuration: CellPLM (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.1 Silhouette score (against reference labels on the model embedding; range -1 to 1; distance stated as cosine in some model sections of the Methods) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'CellPLM', dataset 'BC', column 'Silhouette'
Configuration: Nicheformer (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.26 Adjusted rand index unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source Nicheformer on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Nicheformer', dataset 'BC', column 'ARI'
Configuration: Nicheformer (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.41 Normalized mutual information unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source Nicheformer on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Nicheformer', dataset 'BC', column 'NMI'
Configuration: Nicheformer (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.04 Silhouette score (against reference labels on the model embedding; range -1 to 1; distance stated as cosine in some model sections of the Methods) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source Nicheformer on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Nicheformer', dataset 'BC', column 'Silhouette'
Configuration: Novae (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.31 Adjusted rand index unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source Novae on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Novae', dataset 'BC', column 'ARI'
Configuration: Novae (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.51 Normalized mutual information unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source Novae on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Novae', dataset 'BC', column 'NMI'
Configuration: Novae (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.17 Silhouette score (against reference labels on the model embedding; range -1 to 1; distance stated as cosine in some model sections of the Methods) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source Novae on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Novae', dataset 'BC', column 'Silhouette'
Configuration: scGPT-spatial (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.17 Adjusted rand index unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source scGPT-spatial on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'scGPT-spatial', dataset 'BC', column 'ARI'
Configuration: scGPT-spatial (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.33 Normalized mutual information unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source scGPT-spatial on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'scGPT-spatial', dataset 'BC', column 'NMI'
Configuration: scGPT-spatial (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BC Dataset subset: BC (Chen et al. 2026)	0.1 Silhouette score (against reference labels on the model embedding; range -1 to 1; distance stated as cosine in some model sections of the Methods) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source scGPT-spatial on Chen et al. 2026 continually pretrained clustering, BC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'scGPT-spatial', dataset 'BC', column 'Silhouette'
Configuration: GenePT-w (kmeans, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot clustering with k-means, BC Dataset subset: BC (Chen et al. 2026)	0.14 Adjusted rand index unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source GenePT-w on Chen et al. 2026 zero-shot clustering with k-means, BC lit-cells-networks-20261011-chen2026-protocol-kmeans-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Supplementary Table S2, model 'GenePT-w', dataset 'BC', column 'ARI'
Configuration: GenePT-w (kmeans, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot clustering with k-means, BC Dataset subset: BC (Chen et al. 2026)	0.24 Normalized mutual information unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source GenePT-w on Chen et al. 2026 zero-shot clustering with k-means, BC lit-cells-networks-20261011-chen2026-protocol-kmeans-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Supplementary Table S2, model 'GenePT-w', dataset 'BC', column 'NMI'
Configuration: GenePT-w (kmeans, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot clustering with k-means, BC Dataset subset: BC (Chen et al. 2026)	-0.36 Silhouette score (against reference labels on the model embedding; range -1 to 1; distance stated as cosine in some model sections of the Methods) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source GenePT-w on Chen et al. 2026 zero-shot clustering with k-means, BC lit-cells-networks-20261011-chen2026-protocol-kmeans-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Supplementary Table S2, model 'GenePT-w', dataset 'BC', column 'Silhouette'
Configuration: CellPLM (zero-shot, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC Dataset subset: BC (Chen et al. 2026)	0.17 Adjusted rand index unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC lit-cells-networks-20261011-chen2026-protocol-zs-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 2, model 'CellPLM', dataset 'BC', column 'ARI'
Configuration: CellPLM (zero-shot, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC Dataset subset: BC (Chen et al. 2026)	0.32 Normalized mutual information unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC lit-cells-networks-20261011-chen2026-protocol-zs-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 2, model 'CellPLM', dataset 'BC', column 'NMI'
Configuration: CellPLM (zero-shot, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC Dataset subset: BC (Chen et al. 2026)	0.09 Silhouette score (against reference labels on the model embedding; range -1 to 1; distance stated as cosine in some model sections of the Methods) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC lit-cells-networks-20261011-chen2026-protocol-zs-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 2, model 'CellPLM', dataset 'BC', column 'Silhouette'
Configuration: GenePT-w (zero-shot, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC Dataset subset: BC (Chen et al. 2026)	0.08 Adjusted rand index unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source GenePT-w on Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC lit-cells-networks-20261011-chen2026-protocol-zs-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 2, model 'GenePT-w', dataset 'BC', column 'ARI'
Configuration: GenePT-w (zero-shot, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC Dataset subset: BC (Chen et al. 2026)	0.24 Normalized mutual information unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source GenePT-w on Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC lit-cells-networks-20261011-chen2026-protocol-zs-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 2, model 'GenePT-w', dataset 'BC', column 'NMI'
Configuration: GenePT-w (zero-shot, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC Dataset subset: BC (Chen et al. 2026)	-0.35 Silhouette score (against reference labels on the model embedding; range -1 to 1; distance stated as cosine in some model sections of the Methods) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source GenePT-w on Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC lit-cells-networks-20261011-chen2026-protocol-zs-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 2, model 'GenePT-w', dataset 'BC', column 'Silhouette'
Configuration: Nicheformer (zero-shot, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC Dataset subset: BC (Chen et al. 2026)	0.27 Adjusted rand index unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source Nicheformer on Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC lit-cells-networks-20261011-chen2026-protocol-zs-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 2, model 'Nicheformer', dataset 'BC', column 'ARI'
Configuration: Nicheformer (zero-shot, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC Dataset subset: BC (Chen et al. 2026)	0.42 Normalized mutual information unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source Nicheformer on Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC lit-cells-networks-20261011-chen2026-protocol-zs-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 2, model 'Nicheformer', dataset 'BC', column 'NMI'
Configuration: Nicheformer (zero-shot, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC Dataset subset: BC (Chen et al. 2026)	0.05 Silhouette score (against reference labels on the model embedding; range -1 to 1; distance stated as cosine in some model sections of the Methods) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source Nicheformer on Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC lit-cells-networks-20261011-chen2026-protocol-zs-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 2, model 'Nicheformer', dataset 'BC', column 'Silhouette'
Configuration: Novae (zero-shot, Chen et al. 2026)	Protocol: Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC Dataset subset: BC (Chen et al. 2026)	0.25 Adjusted rand index unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source Novae on Chen et al. 2026 zero-shot cell type and spatial domain clustering, BC lit-cells-networks-20261011-chen2026-protocol-zs-cluster-bc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 2, model 'Novae', dataset 'BC', column 'ARI'

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Claims, original sources and review scope · Release 2026-10-23-5347695500ed
Property and statement	Original source and location	Review and provenance
Source dataset label BC Context-only references	Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation Original source ↗ Methods 'Datasets' Version: arXiv 2607.17227v2, updated 2026-09-17 (v1 posted 2026-07-19) Retrieved: 2026-10-11T21:29:34Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.source_dataset_label` Source artifact SHA-256: `8137338c6146f570a6546f30707d4244baca9dab848b32c51539665a5ba539da` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Source location Methods 'Datasets' Context-only references	Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation Original source ↗ Methods 'Datasets' Version: arXiv 2607.17227v2, updated 2026-09-17 (v1 posted 2026-07-19) Retrieved: 2026-10-11T21:29:34Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.source_locator` Source artifact SHA-256: `8137338c6146f570a6546f30707d4244baca9dab848b32c51539665a5ba539da` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Description BC spatial dataset (Visium breast cancer HBCA1 per Methods), as preprocessed by Chen et al. 2026. Context-only references	Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation Original source ↗ Methods 'Datasets' Version: arXiv 2607.17227v2, updated 2026-09-17 (v1 posted 2026-07-19) Retrieved: 2026-10-11T21:29:34Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `description` Source artifact SHA-256: `8137338c6146f570a6546f30707d4244baca9dab848b32c51539665a5ba539da` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Name BC (Chen et al. 2026) Context-only references	Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation Original source ↗ Methods 'Datasets' Version: arXiv 2607.17227v2, updated 2026-09-17 (v1 posted 2026-07-19) Retrieved: 2026-10-11T21:29:34Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `name` Source artifact SHA-256: `8137338c6146f570a6546f30707d4244baca9dab848b32c51539665a5ba539da` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Original source · arXiv 2607.17227v2, updated 2026-09-17 (v1 posted 2026-07-19)

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Stable ID: lit-cells-networks-20261011-chen2026-data-bc

areas: single-cell; spatial-omics
contexts: research
source dataset label: BC
source locator: Methods 'Datasets'
missing metadata: version: reason: unreported

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