Dataset subset

Replogle (Chen et al. 2026)

Replogle K562 genome-wide CRISPRi Perturb-seq, as preprocessed by Chen et al. 2026.

Evaluation results

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

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Applied filters: All linked evaluations

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, Replogle Dataset subset: Replogle (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 CellPLM 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 'CellPLM', dataset 'Replogle', column 'MSE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.184 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, 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 'CellPLM', dataset 'Replogle', column 'MSE DE'
Configuration: CellPLM (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 CellPLM 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 'CellPLM', dataset 'Replogle', column 'Pearson'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.941 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, 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 'CellPLM', dataset 'Replogle', column 'Pearson DE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.38 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, 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 'CellPLM', dataset 'Replogle', column 'Pearson Delta'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.46 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, 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 'CellPLM', dataset 'Replogle', 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'
Configuration: scELMo (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.0077 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, 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 'scELMo', dataset 'Replogle', column 'MSE'
Configuration: scELMo (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.143 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, 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 'scELMo', dataset 'Replogle', column 'MSE DE'
Configuration: scELMo (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 scELMo 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 'scELMo', dataset 'Replogle', column 'Pearson'
Configuration: scELMo (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.921 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, 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 'scELMo', dataset 'Replogle', column 'Pearson DE'
Configuration: scELMo (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.329 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, 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 'scELMo', dataset 'Replogle', column 'Pearson Delta'
Configuration: scELMo (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.458 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, 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 'scELMo', dataset 'Replogle', column 'Pearson Delta DE'
Configuration: scGPT-spatial (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 scGPT-spatial 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 'scGPT-spatial', dataset 'Replogle', column 'MSE'
Configuration: scGPT-spatial (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.18 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, 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 'scGPT-spatial', dataset 'Replogle', column 'MSE DE'
Configuration: scGPT-spatial (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.986 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, 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 'scGPT-spatial', dataset 'Replogle', column 'Pearson'
Configuration: scGPT-spatial (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.944 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, 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 'scGPT-spatial', dataset 'Replogle', column 'Pearson DE'
Configuration: scGPT-spatial (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.306 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, 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 'scGPT-spatial', dataset 'Replogle', column 'Pearson Delta'
Configuration: scGPT-spatial (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.38 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, 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 'scGPT-spatial', dataset 'Replogle', column 'Pearson Delta DE'

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Claims, original sources and review scope · Release 2026-10-23-5347695500ed
Property and statement	Original source and location	Review and provenance
Source dataset label Replogle Context-only references	Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation Original source ↗ Methods 'Datasets' Version: arXiv 2607.17227v2, updated 2026-09-17 (v1 posted 2026-07-19) Retrieved: 2026-10-11T21:29:34Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.source_dataset_label` Source artifact SHA-256: `8137338c6146f570a6546f30707d4244baca9dab848b32c51539665a5ba539da` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Source location Methods 'Datasets' Context-only references	Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation Original source ↗ Methods 'Datasets' Version: arXiv 2607.17227v2, updated 2026-09-17 (v1 posted 2026-07-19) Retrieved: 2026-10-11T21:29:34Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.source_locator` Source artifact SHA-256: `8137338c6146f570a6546f30707d4244baca9dab848b32c51539665a5ba539da` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Description Replogle K562 genome-wide CRISPRi Perturb-seq, as preprocessed by Chen et al. 2026. Context-only references	Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation Original source ↗ Methods 'Datasets' Version: arXiv 2607.17227v2, updated 2026-09-17 (v1 posted 2026-07-19) Retrieved: 2026-10-11T21:29:34Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `description` Source artifact SHA-256: `8137338c6146f570a6546f30707d4244baca9dab848b32c51539665a5ba539da` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Name Replogle (Chen et al. 2026) Context-only references	Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation Original source ↗ Methods 'Datasets' Version: arXiv 2607.17227v2, updated 2026-09-17 (v1 posted 2026-07-19) Retrieved: 2026-10-11T21:29:34Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `name` Source artifact SHA-256: `8137338c6146f570a6546f30707d4244baca9dab848b32c51539665a5ba539da` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Release 2026-10-23-5347695500ed · Record review: source checked

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

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

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

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