Protocol

Chen et al. 2026 genetic perturbation prediction, Adamson

scGPT-style perturbation workflow with a shared GEARS decoder; all models scored on the same intersected gene set per dataset. Dataset Adamson.

4 evaluations · 24 results

Overview

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Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

4 recorded evaluations, 24 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

Results are available, but no reviewed comparison panel is linked in this release.

All evaluations

4 evaluations · 24 results. Different protocols are not a single leaderboard.

Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson · Dataset subset: Adamson (Chen et al. 2026)

Sorted by MSE (all genes, predicted versus observed post-perturbation expression) (lower 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	MSE (all genes, predicted versus observed post-perturbation expression)	MSE (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression)	Pearson (all genes, predicted versus observed post-perturbation expression)	Pearson (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression)	Pearson delta (all genes in the intersected gene set)	Pearson delta (top 20 differentially expressed genes per perturbation condition)
GenePT-w (gears-decoder, Chen et al. 2026)Evaluation	0.0068 (best in column)	0.244	0.99 (best in column)	0.958 (best in column)	0.614	0.624
scELMo (gears-decoder, Chen et al. 2026)Evaluation	0.0073	0.21 (best in column)	0.988	0.952	0.618	0.745 (best in column)
scGPT-spatial (gears-decoder, Chen et al. 2026)Evaluation	0.008	0.255	0.987	0.937	0.508	0.71
CellPLM (gears-decoder, Chen et al. 2026)Evaluation	0.0091	0.278	0.988	0.924	0.649 (best in column)	0.711

All 24 result rows with coverage, uncertainty and sources

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.0091 MSE (all genes, predicted versus observed post-perturbation expression) unitless · lower 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'MSE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.278 MSE (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) unitless · lower 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'MSE DE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.988 Pearson (all genes, predicted versus observed post-perturbation expression) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'Pearson'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.924 Pearson (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'Pearson DE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.649 Pearson delta (all genes in the intersected gene set) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'Pearson Delta'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.711 Pearson delta (top 20 differentially expressed genes per perturbation condition) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'Pearson Delta DE'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.0068 MSE (all genes, predicted versus observed post-perturbation expression) unitless · lower 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Adamson', column 'MSE'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.244 MSE (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) unitless · lower 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Adamson', column 'MSE DE'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.99 Pearson (all genes, predicted versus observed post-perturbation expression) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Adamson', column 'Pearson'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.958 Pearson (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Adamson', column 'Pearson DE'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.614 Pearson delta (all genes in the intersected gene set) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Adamson', column 'Pearson Delta'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.624 Pearson delta (top 20 differentially expressed genes per perturbation condition) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Adamson', column 'Pearson Delta DE'
Configuration: scELMo (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.0073 MSE (all genes, predicted versus observed post-perturbation expression) unitless · lower 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 scELMo on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scELMo', dataset 'Adamson', column 'MSE'
Configuration: scELMo (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.21 MSE (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) unitless · lower 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 scELMo on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scELMo', dataset 'Adamson', column 'MSE DE'
Configuration: scELMo (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.988 Pearson (all genes, predicted versus observed post-perturbation expression) 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 scELMo on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scELMo', dataset 'Adamson', column 'Pearson'
Configuration: scELMo (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.952 Pearson (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) 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 scELMo on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scELMo', dataset 'Adamson', column 'Pearson DE'
Configuration: scELMo (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.618 Pearson delta (all genes in the intersected gene set) 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 scELMo on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scELMo', dataset 'Adamson', column 'Pearson Delta'
Configuration: scELMo (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.745 Pearson delta (top 20 differentially expressed genes per perturbation condition) 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 scELMo on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scELMo', dataset 'Adamson', column 'Pearson Delta DE'
Configuration: scGPT-spatial (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.008 MSE (all genes, predicted versus observed post-perturbation expression) unitless · lower 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scGPT-spatial', dataset 'Adamson', column 'MSE'
Configuration: scGPT-spatial (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.255 MSE (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) unitless · lower 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scGPT-spatial', dataset 'Adamson', column 'MSE DE'
Configuration: scGPT-spatial (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.987 Pearson (all genes, predicted versus observed post-perturbation expression) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scGPT-spatial', dataset 'Adamson', column 'Pearson'
Configuration: scGPT-spatial (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.937 Pearson (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scGPT-spatial', dataset 'Adamson', column 'Pearson DE'
Configuration: scGPT-spatial (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.508 Pearson delta (all genes in the intersected gene set) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scGPT-spatial', dataset 'Adamson', column 'Pearson Delta'
Configuration: scGPT-spatial (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.71 Pearson delta (top 20 differentially expressed genes per perturbation condition) 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 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'scGPT-spatial', dataset 'Adamson', column 'Pearson Delta DE'

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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-perturbation-adamson

areas: single-cell; spatial-omics
contexts: research
protocol: scGPT-style perturbation workflow with a shared GEARS decoder; all models scored on the same intersected gene set per dataset
version: arXiv 2607.17227v2
metric: pearson-delta
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 5; Results 'Perturbation prediction'; Methods

Related records

part of: Harmonised single-cell and spatial foundation model benchmark (Chen et al. 2026)
uses data: Adamson (Chen et al. 2026)
assessment: CellPLM on Chen et al. 2026 genetic perturbation prediction, Adamson
assessment: GenePT-w on Chen et al. 2026 genetic perturbation prediction, Adamson
assessment: scELMo on Chen et al. 2026 genetic perturbation prediction, Adamson
assessment: scGPT-spatial on Chen et al. 2026 genetic perturbation prediction, Adamson
assessed by: Assess models for genetic perturbation experiments

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