Protocol

Chen et al. 2026 continually pretrained clustering, BMMC

Two epochs of self-supervised continual pretraining per dataset, then the zero-shot clustering protocol. Dataset BMMC.

Overview

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3 recorded evaluations, 9 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.

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Results

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All evaluations

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

Protocol: Chen et al. 2026 continually pretrained clustering, BMMC · Dataset subset: BMMC (Chen et al. 2026)

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	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)
CellPLM (continual, Chen et al. 2026)Evaluation	0.28 (best in column)	0.66 (best in column)	0.12
scGPT-spatial (continual, Chen et al. 2026)Evaluation	0.28 (best in column)	0.66 (best in column)	0.19 (best in column)
Nicheformer (continual, Chen et al. 2026)Evaluation	0.23	0.59	0.08

All 9 result rows with coverage, uncertainty and sources

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

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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, BMMC Dataset subset: BMMC (Chen et al. 2026)	0.28 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, BMMC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bmmc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'CellPLM', dataset 'BMMC', column 'ARI'
Configuration: CellPLM (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BMMC Dataset subset: BMMC (Chen et al. 2026)	0.66 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, BMMC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bmmc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'CellPLM', dataset 'BMMC', column 'NMI'
Configuration: CellPLM (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BMMC Dataset subset: BMMC (Chen et al. 2026)	0.12 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, BMMC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bmmc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'CellPLM', dataset 'BMMC', column 'Silhouette'
Configuration: Nicheformer (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BMMC Dataset subset: BMMC (Chen et al. 2026)	0.23 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, BMMC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bmmc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Nicheformer', dataset 'BMMC', column 'ARI'
Configuration: Nicheformer (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BMMC Dataset subset: BMMC (Chen et al. 2026)	0.59 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, BMMC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bmmc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Nicheformer', dataset 'BMMC', column 'NMI'
Configuration: Nicheformer (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BMMC Dataset subset: BMMC (Chen et al. 2026)	0.08 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, BMMC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bmmc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'Nicheformer', dataset 'BMMC', column 'Silhouette'
Configuration: scGPT-spatial (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BMMC Dataset subset: BMMC (Chen et al. 2026)	0.28 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, BMMC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bmmc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'scGPT-spatial', dataset 'BMMC', column 'ARI'
Configuration: scGPT-spatial (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BMMC Dataset subset: BMMC (Chen et al. 2026)	0.66 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, BMMC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bmmc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'scGPT-spatial', dataset 'BMMC', column 'NMI'
Configuration: scGPT-spatial (continual, Chen et al. 2026)	Protocol: Chen et al. 2026 continually pretrained clustering, BMMC Dataset subset: BMMC (Chen et al. 2026)	0.19 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, BMMC lit-cells-networks-20261011-chen2026-protocol-cp-cluster-bmmc Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 3, model 'scGPT-spatial', dataset 'BMMC', column 'Silhouette'

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External evaluations: 3

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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-protocol-cp-cluster-bmmc

areas: single-cell; spatial-omics
contexts: research
protocol: Two epochs of self-supervised continual pretraining per dataset, then the zero-shot clustering protocol
version: arXiv 2607.17227v2
metric: adjusted-rand-index
metric direction: higher
unit: unitless
random seeds: Seed 42 for NumPy, Scanpy and PyTorch (Methods)
limitations: Single run; no spread printed.; No non-foundation-model baseline is reported for this task in the tables.
source locator: Table 3; Methods 'Continually-pretrained cell type clustering'

Related records

part of: Harmonised single-cell and spatial foundation model benchmark (Chen et al. 2026)
uses data: BMMC (Chen et al. 2026)
assessment: CellPLM on Chen et al. 2026 continually pretrained clustering, BMMC
assessment: Nicheformer on Chen et al. 2026 continually pretrained clustering, BMMC
assessment: scGPT-spatial on Chen et al. 2026 continually pretrained clustering, BMMC

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