Configuration

Novae (continual, Chen et al. 2026)

Configuration of Novae: two epochs of self-supervised continual pretraining on the target dataset, then the zero-shot clustering workflow.

3 evaluations · 9 results

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3 evaluations · 9 results. Different protocols are not a single leaderboard.

Sorted by Adjusted rand index (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Protocol and dataset	Adjusted rand index	Normalized mutual information	Silhouette score (against reference labels on the model embedding; range -1 to 1; distance stated as cosine in some model sections of the Methods)
Novae (continual, Chen et al. 2026)Evaluation	Chen et al. 2026 continually pretrained clustering, BC, BC (Chen et al. 2026)	0.31 (best in column)	0.51 (best in column)	0.17
Novae (continual, Chen et al. 2026)Evaluation	Chen et al. 2026 continually pretrained clustering, ME, ME (Chen et al. 2026)	0.18	0.35	0.2
Novae (continual, Chen et al. 2026)Evaluation	Chen et al. 2026 continually pretrained clustering, MH, MH (Chen et al. 2026)	0.07	0.14	0.32 (best in column)

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
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: Novae (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, ME Dataset subset: ME (Chen et al. 2026)	0.18 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, ME lit-cells-networks-20261011-chen2026-protocol-cp-cluster-me Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Novae', dataset 'ME', column 'ARI'
Configuration: Novae (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, ME Dataset subset: ME (Chen et al. 2026)	0.35 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, ME lit-cells-networks-20261011-chen2026-protocol-cp-cluster-me Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Novae', dataset 'ME', column 'NMI'
Configuration: Novae (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, ME Dataset subset: ME (Chen et al. 2026)	0.2 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, ME lit-cells-networks-20261011-chen2026-protocol-cp-cluster-me Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Novae', dataset 'ME', column 'Silhouette'
Configuration: Novae (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, MH Dataset subset: MH (Chen et al. 2026)	0.07 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, MH lit-cells-networks-20261011-chen2026-protocol-cp-cluster-mh Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Novae', dataset 'MH', column 'ARI'
Configuration: Novae (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, MH Dataset subset: MH (Chen et al. 2026)	0.14 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, MH lit-cells-networks-20261011-chen2026-protocol-cp-cluster-mh Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Novae', dataset 'MH', column 'NMI'
Configuration: Novae (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, MH Dataset subset: MH (Chen et al. 2026)	0.32 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, MH lit-cells-networks-20261011-chen2026-protocol-cp-cluster-mh Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Novae', dataset 'MH', column 'Silhouette'

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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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Technical metadata and extraction receipts

Stable ID: lit-cells-networks-20261011-chen2026-config-novae-continual

areas: single-cell; spatial-omics
contexts: research
method types: foundation_model
reported name: Novae
foundation model eligible: true
training setting: two epochs of self-supervised continual pretraining on the target dataset, then the zero-shot clustering workflow
access reuse: Public pretrained checkpoints as released by each model's authors
source locator: Methods: model-specific sections under 'Zero-shot cell type clustering', 'Continually-pretrained cell type clustering', 'Cell type annotation' and 'Perturbation prediction'
missing metadata: version: reason: unreported; note: Checkpoint names are printed for some models only (CellPLM 20231027_85M)
provenance note: Novae as run by Chen et al. 2026: two epochs of self-supervised continual pretraining on the target dataset, then the zero-shot clustering workflow.

Related records

configuration of: Novae
system: Novae on Chen et al. 2026 continually pretrained clustering, BC
system: Novae on Chen et al. 2026 continually pretrained clustering, ME
system: Novae on Chen et al. 2026 continually pretrained clustering, MH

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