Configuration

GenePT-w (gears-decoder, Chen et al. 2026)

GenePT-w: model embeddings (or gene embeddings for GenePT-w and scELMo) used with a shared GEARS perturbation decoder in the scGPT-style perturbation workflow.

3 evaluations · 18 results

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Evaluations and results

3 evaluations · 18 results. Different protocols are not a single leaderboard.

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	Protocol and dataset	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	Chen et al. 2026 genetic perturbation prediction, Norman, Norman (Chen et al. 2026)	0.0043 (best in column)	0.176	0.988	0.925	0.587	0.739 (best in column)
GenePT-w (gears-decoder, Chen et al. 2026)Evaluation	Chen et al. 2026 genetic perturbation prediction, Adamson, Adamson (Chen et al. 2026)	0.0068	0.244	0.99 (best in column)	0.958 (best in column)	0.614 (best in column)	0.624
GenePT-w (gears-decoder, Chen et al. 2026)Evaluation	Chen et al. 2026 genetic perturbation prediction, Replogle, Replogle (Chen et al. 2026)	0.0076	0.137 (best in column)	0.987	0.925	0.3	0.451

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Tested configuration	Protocol and dataset	Finding	Evidence and details
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: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (Chen et al. 2026)	0.0043 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, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Norman', column 'MSE'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (Chen et al. 2026)	0.176 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, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Norman', column 'MSE DE'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (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 GenePT-w on Chen et al. 2026 genetic perturbation prediction, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Norman', column 'Pearson'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (Chen et al. 2026)	0.925 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, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Norman', column 'Pearson DE'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (Chen et al. 2026)	0.587 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, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Norman', column 'Pearson Delta'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (Chen et al. 2026)	0.739 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, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Norman', column 'Pearson Delta DE'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.0076 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, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Replogle', column 'MSE'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.137 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, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Replogle', column 'MSE DE'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (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 GenePT-w on Chen et al. 2026 genetic perturbation prediction, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Replogle', column 'Pearson'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.925 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, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Replogle', column 'Pearson DE'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.3 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, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Replogle', column 'Pearson Delta'
Configuration: GenePT-w (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.451 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, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'GenePT-w', dataset 'Replogle', 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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Stable ID: lit-cells-networks-20261011-chen2026-config-genept-w-gears-decoder

areas: single-cell; spatial-omics
contexts: research
method types: foundation_model
reported name: GenePT-w
foundation model eligible: true
training setting: model embeddings (or gene embeddings for GenePT-w and scELMo) used with a shared GEARS perturbation decoder in the scGPT-style perturbation 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: GenePT-w as run by Chen et al. 2026: model embeddings (or gene embeddings for GenePT-w and scELMo) used with a shared GEARS perturbation decoder in the scGPT-style perturbation workflow.

Related records

configuration of: GenePT
system: GenePT-w on Chen et al. 2026 genetic perturbation prediction, Adamson
system: GenePT-w on Chen et al. 2026 genetic perturbation prediction, Norman
system: GenePT-w on Chen et al. 2026 genetic perturbation prediction, Replogle

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