Configuration

scBERT (Ahlmann-Eltze et al. 2025)

Configuration of scBERT: Fine-tuned to predict the perturbation labels of the training data; perturbed embedding computed as for UCE; ridge regression decoder.

4 evaluations · 8 results

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Evaluations and results

4 evaluations · 8 results. Different protocols are not a single leaderboard.

Sorted by L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits) (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	L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits)	Pearson delta (1,000 most highly expressed control genes; mean over held-out perturbations and splits)
scBERT (Ahlmann-Eltze et al. 2025)Evaluation	Adamson: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a), Adamson et al. 2016 Perturb-seq, K562 CRISPRi single perturbations (GEARS distribution, Ahlmann-Eltze et al. 2025)	3.88Computed by Rewire from the authors' source data (best in column)	0.728Computed by Rewire from the authors' source data (best in column)
scBERT (Ahlmann-Eltze et al. 2025)Evaluation	Replogle K562: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a), Replogle et al. 2022 Perturb-seq essential-gene screen, K562 CRISPRi (GEARS distribution, Ahlmann-Eltze et al. 2025)	4.19Computed by Rewire from the authors' source data	0.408Computed by Rewire from the authors' source data
scBERT (Ahlmann-Eltze et al. 2025)Evaluation	Norman double perturbations: expression prediction for held-out double perturbations (Ahlmann-Eltze et al. 2025 Fig. 1a and Extended Data Fig. 2a), Norman et al. 2019 Perturb-seq, K562 CRISPRa single and double perturbations (scFoundation reprocessing, Ahlmann-Eltze et al. 2025)	4.28Computed by Rewire from the authors' source data	0.61Computed by Rewire from the authors' source data
scBERT (Ahlmann-Eltze et al. 2025)Evaluation	Replogle RPE1: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a), Replogle et al. 2022 Perturb-seq essential-gene screen, RPE1 CRISPRi (GEARS distribution, Ahlmann-Eltze et al. 2025)	5.53Computed by Rewire from the authors' source data	0.678Computed by Rewire from the authors' source data

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Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: scBERT (Ahlmann-Eltze et al. 2025)	Protocol: Norman double perturbations: expression prediction for held-out double perturbations (Ahlmann-Eltze et al. 2025 Fig. 1a and Extended Data Fig. 2a) Dataset: Norman et al. 2019 Perturb-seq, K562 CRISPRa single and double perturbations (scFoundation reprocessing, Ahlmann-Eltze et al. 2025)	0.61 Pearson delta (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unitless · higher Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 310/310 perturbation-split rows scored	Independent external evaluation · Source checked Methods, coverage and source scBERT on Norman doubles (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-norman-double Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Extended Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Extended Data Fig. 2 (MOESM6), sheet "Panel A", rows with dataset_name "norman_from_scfoundation" and method "scbert", column "r2_delta"; plotted as the red line in Extended Data Fig. 2a
Configuration: scBERT (Ahlmann-Eltze et al. 2025)	Protocol: Adamson: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a) Dataset: Adamson et al. 2016 Perturb-seq, K562 CRISPRi single perturbations (GEARS distribution, Ahlmann-Eltze et al. 2025)	0.728 Pearson delta (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unitless · higher Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 48/48 perturbation-split rows scored	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Panel A repeats Source Data Fig. 2 Panel A row for row (48, 262 and 413 rows per method over two splits). Methods restricts the analysis to 73, 398 and 629 perturbations predicted by all models; the sheet does not have these counts.Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 · Methods 'Single perturbation benchmark setup' paragraph 4 versus Source Data Extended Data Fig. 8, sheet "Panel A" The Fig. 2 legend gives "134, 210 and 24 unseen single perturbations across two test-training splits". In Panel A these are the split 1 counts only (Replogle K562 134, Replogle RPE1 210, Adamson 24 rows per method); split 2 has 128, 203 and 24, so the two-split totals are 262, 413 and 48. The Fig. 2a means recorded here use all Panel A rows; whether the plotted red lines use both splits is not stated beyond the sheet itself. Reviewer check 2026-10-11: using split 1 rows only would change the per-model means by up to 0.61 (L2 distance) and 0.06 (Pearson delta), and would change the model with the lowest mean L2 distance on Adamson and Replogle RPE1, so the choice is material.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Fig. 2 legend versus Source Data Fig. 2, sheet "Panel A" Methods restricts the single-perturbation analysis to perturbations predicted by all models: 73 for Adamson, 398 for Replogle K562 and 629 for Replogle RPE1. No sheet of Source Data Fig. 2 has these counts (Panel A: 48, 262 and 413 per method over two splits; Panel C: 20, 214 and 224 test or val rows per method).Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Methods 'Single perturbation benchmark setup' paragraph 4 versus Source Data Fig. 2, sheets "Panel A" and "Panel C" The test and val rows in Panel C (20, 214 and 224 per method for Adamson, Replogle K562 and Replogle RPE1) match neither the Fig. 2 legend counts nor the Methods counts. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Fig. 2, sheet "Panel C" versus the Fig. 2 legend and Methods The main text says CPA was not included in the single-perturbation benchmark, but method "cpa" has rows in Panel C. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Main text paragraph 16 versus Source Data Fig. 2, sheet "Panel C" Methods, coverage and source scBERT on Adamson (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-adamson-single Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 · Source Data Extended Data Fig. 8 (MOESM10), sheet "Panel A", rows with dataset_name "adamson" and method "scbert", column "r2_delta"; plotted as the red line in Extended Data Fig. 8a
Configuration: scBERT (Ahlmann-Eltze et al. 2025)	Protocol: Replogle K562: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a) Dataset: Replogle et al. 2022 Perturb-seq essential-gene screen, K562 CRISPRi (GEARS distribution, Ahlmann-Eltze et al. 2025)	0.408 Pearson delta (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unitless · higher Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 262/262 perturbation-split rows scored	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Panel A repeats Source Data Fig. 2 Panel A row for row (48, 262 and 413 rows per method over two splits). Methods restricts the analysis to 73, 398 and 629 perturbations predicted by all models; the sheet does not have these counts.Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 · Methods 'Single perturbation benchmark setup' paragraph 4 versus Source Data Extended Data Fig. 8, sheet "Panel A" The Fig. 2 legend gives "134, 210 and 24 unseen single perturbations across two test-training splits". In Panel A these are the split 1 counts only (Replogle K562 134, Replogle RPE1 210, Adamson 24 rows per method); split 2 has 128, 203 and 24, so the two-split totals are 262, 413 and 48. The Fig. 2a means recorded here use all Panel A rows; whether the plotted red lines use both splits is not stated beyond the sheet itself. Reviewer check 2026-10-11: using split 1 rows only would change the per-model means by up to 0.61 (L2 distance) and 0.06 (Pearson delta), and would change the model with the lowest mean L2 distance on Adamson and Replogle RPE1, so the choice is material.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Fig. 2 legend versus Source Data Fig. 2, sheet "Panel A" Methods restricts the single-perturbation analysis to perturbations predicted by all models: 73 for Adamson, 398 for Replogle K562 and 629 for Replogle RPE1. No sheet of Source Data Fig. 2 has these counts (Panel A: 48, 262 and 413 per method over two splits; Panel C: 20, 214 and 224 test or val rows per method).Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Methods 'Single perturbation benchmark setup' paragraph 4 versus Source Data Fig. 2, sheets "Panel A" and "Panel C" The test and val rows in Panel C (20, 214 and 224 per method for Adamson, Replogle K562 and Replogle RPE1) match neither the Fig. 2 legend counts nor the Methods counts. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Fig. 2, sheet "Panel C" versus the Fig. 2 legend and Methods The main text says CPA was not included in the single-perturbation benchmark, but method "cpa" has rows in Panel C. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Main text paragraph 16 versus Source Data Fig. 2, sheet "Panel C" Methods, coverage and source scBERT on Replogle K562 (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-replogle-k562-single Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 · Source Data Extended Data Fig. 8 (MOESM10), sheet "Panel A", rows with dataset_name "replogle_k562_essential" and method "scbert", column "r2_delta"; plotted as the red line in Extended Data Fig. 8a
Configuration: scBERT (Ahlmann-Eltze et al. 2025)	Protocol: Replogle RPE1: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a) Dataset: Replogle et al. 2022 Perturb-seq essential-gene screen, RPE1 CRISPRi (GEARS distribution, Ahlmann-Eltze et al. 2025)	0.678 Pearson delta (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unitless · higher Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 413/413 perturbation-split rows scored	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Panel A repeats Source Data Fig. 2 Panel A row for row (48, 262 and 413 rows per method over two splits). Methods restricts the analysis to 73, 398 and 629 perturbations predicted by all models; the sheet does not have these counts.Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 · Methods 'Single perturbation benchmark setup' paragraph 4 versus Source Data Extended Data Fig. 8, sheet "Panel A" The Fig. 2 legend gives "134, 210 and 24 unseen single perturbations across two test-training splits". In Panel A these are the split 1 counts only (Replogle K562 134, Replogle RPE1 210, Adamson 24 rows per method); split 2 has 128, 203 and 24, so the two-split totals are 262, 413 and 48. The Fig. 2a means recorded here use all Panel A rows; whether the plotted red lines use both splits is not stated beyond the sheet itself. Reviewer check 2026-10-11: using split 1 rows only would change the per-model means by up to 0.61 (L2 distance) and 0.06 (Pearson delta), and would change the model with the lowest mean L2 distance on Adamson and Replogle RPE1, so the choice is material.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Fig. 2 legend versus Source Data Fig. 2, sheet "Panel A" Methods restricts the single-perturbation analysis to perturbations predicted by all models: 73 for Adamson, 398 for Replogle K562 and 629 for Replogle RPE1. No sheet of Source Data Fig. 2 has these counts (Panel A: 48, 262 and 413 per method over two splits; Panel C: 20, 214 and 224 test or val rows per method).Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Methods 'Single perturbation benchmark setup' paragraph 4 versus Source Data Fig. 2, sheets "Panel A" and "Panel C" The test and val rows in Panel C (20, 214 and 224 per method for Adamson, Replogle K562 and Replogle RPE1) match neither the Fig. 2 legend counts nor the Methods counts. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Fig. 2, sheet "Panel C" versus the Fig. 2 legend and Methods The main text says CPA was not included in the single-perturbation benchmark, but method "cpa" has rows in Panel C. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Main text paragraph 16 versus Source Data Fig. 2, sheet "Panel C" Methods, coverage and source scBERT on Replogle RPE1 (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-replogle-rpe1-single Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Extended Data Fig. 8, Ahlmann-Eltze et al. 2025 · Source Data Extended Data Fig. 8 (MOESM10), sheet "Panel A", rows with dataset_name "replogle_rpe1_essential" and method "scbert", column "r2_delta"; plotted as the red line in Extended Data Fig. 8a
Configuration: scBERT (Ahlmann-Eltze et al. 2025)	Protocol: Norman double perturbations: expression prediction for held-out double perturbations (Ahlmann-Eltze et al. 2025 Fig. 1a and Extended Data Fig. 2a) Dataset: Norman et al. 2019 Perturb-seq, K562 CRISPRa single and double perturbations (scFoundation reprocessing, Ahlmann-Eltze et al. 2025)	4.28 L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unit-unreported · lower Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 310/310 perturbation-split rows scored	Independent external evaluation · Source checked Methods, coverage and source scBERT on Norman doubles (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-norman-double Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Fig. 1, Ahlmann-Eltze et al. 2025 · Source Data Fig. 1 (MOESM3), sheet "Panel A", rows with dataset_name "norman_from_scfoundation" and method "scbert", column "l2"; plotted as the red line in Fig. 1a
Configuration: scBERT (Ahlmann-Eltze et al. 2025)	Protocol: Adamson: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a) Dataset: Adamson et al. 2016 Perturb-seq, K562 CRISPRi single perturbations (GEARS distribution, Ahlmann-Eltze et al. 2025)	3.88 L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unit-unreported · lower Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 48/48 perturbation-split rows scored	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The Fig. 2 legend gives "134, 210 and 24 unseen single perturbations across two test-training splits". In Panel A these are the split 1 counts only (Replogle K562 134, Replogle RPE1 210, Adamson 24 rows per method); split 2 has 128, 203 and 24, so the two-split totals are 262, 413 and 48. The Fig. 2a means recorded here use all Panel A rows; whether the plotted red lines use both splits is not stated beyond the sheet itself. Reviewer check 2026-10-11: using split 1 rows only would change the per-model means by up to 0.61 (L2 distance) and 0.06 (Pearson delta), and would change the model with the lowest mean L2 distance on Adamson and Replogle RPE1, so the choice is material.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Fig. 2 legend versus Source Data Fig. 2, sheet "Panel A" Methods restricts the single-perturbation analysis to perturbations predicted by all models: 73 for Adamson, 398 for Replogle K562 and 629 for Replogle RPE1. No sheet of Source Data Fig. 2 has these counts (Panel A: 48, 262 and 413 per method over two splits; Panel C: 20, 214 and 224 test or val rows per method).Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Methods 'Single perturbation benchmark setup' paragraph 4 versus Source Data Fig. 2, sheets "Panel A" and "Panel C" The test and val rows in Panel C (20, 214 and 224 per method for Adamson, Replogle K562 and Replogle RPE1) match neither the Fig. 2 legend counts nor the Methods counts. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Fig. 2, sheet "Panel C" versus the Fig. 2 legend and Methods The main text says CPA was not included in the single-perturbation benchmark, but method "cpa" has rows in Panel C. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Main text paragraph 16 versus Source Data Fig. 2, sheet "Panel C" Methods, coverage and source scBERT on Adamson (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-adamson-single Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Fig. 2 (MOESM4), sheet "Panel A", rows with dataset_name "adamson" and method "scbert", column "l2"; plotted as the red line in Fig. 2a
Configuration: scBERT (Ahlmann-Eltze et al. 2025)	Protocol: Replogle K562: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a) Dataset: Replogle et al. 2022 Perturb-seq essential-gene screen, K562 CRISPRi (GEARS distribution, Ahlmann-Eltze et al. 2025)	4.19 L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unit-unreported · lower Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 262/262 perturbation-split rows scored	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The Fig. 2 legend gives "134, 210 and 24 unseen single perturbations across two test-training splits". In Panel A these are the split 1 counts only (Replogle K562 134, Replogle RPE1 210, Adamson 24 rows per method); split 2 has 128, 203 and 24, so the two-split totals are 262, 413 and 48. The Fig. 2a means recorded here use all Panel A rows; whether the plotted red lines use both splits is not stated beyond the sheet itself. Reviewer check 2026-10-11: using split 1 rows only would change the per-model means by up to 0.61 (L2 distance) and 0.06 (Pearson delta), and would change the model with the lowest mean L2 distance on Adamson and Replogle RPE1, so the choice is material.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Fig. 2 legend versus Source Data Fig. 2, sheet "Panel A" Methods restricts the single-perturbation analysis to perturbations predicted by all models: 73 for Adamson, 398 for Replogle K562 and 629 for Replogle RPE1. No sheet of Source Data Fig. 2 has these counts (Panel A: 48, 262 and 413 per method over two splits; Panel C: 20, 214 and 224 test or val rows per method).Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Methods 'Single perturbation benchmark setup' paragraph 4 versus Source Data Fig. 2, sheets "Panel A" and "Panel C" The test and val rows in Panel C (20, 214 and 224 per method for Adamson, Replogle K562 and Replogle RPE1) match neither the Fig. 2 legend counts nor the Methods counts. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Fig. 2, sheet "Panel C" versus the Fig. 2 legend and Methods The main text says CPA was not included in the single-perturbation benchmark, but method "cpa" has rows in Panel C. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Main text paragraph 16 versus Source Data Fig. 2, sheet "Panel C" Methods, coverage and source scBERT on Replogle K562 (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-replogle-k562-single Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Fig. 2 (MOESM4), sheet "Panel A", rows with dataset_name "replogle_k562_essential" and method "scbert", column "l2"; plotted as the red line in Fig. 2a
Configuration: scBERT (Ahlmann-Eltze et al. 2025)	Protocol: Replogle RPE1: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a) Dataset: Replogle et al. 2022 Perturb-seq essential-gene screen, RPE1 CRISPRi (GEARS distribution, Ahlmann-Eltze et al. 2025)	5.53 L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unit-unreported · lower Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 413/413 perturbation-split rows scored	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The Fig. 2 legend gives "134, 210 and 24 unseen single perturbations across two test-training splits". In Panel A these are the split 1 counts only (Replogle K562 134, Replogle RPE1 210, Adamson 24 rows per method); split 2 has 128, 203 and 24, so the two-split totals are 262, 413 and 48. The Fig. 2a means recorded here use all Panel A rows; whether the plotted red lines use both splits is not stated beyond the sheet itself. Reviewer check 2026-10-11: using split 1 rows only would change the per-model means by up to 0.61 (L2 distance) and 0.06 (Pearson delta), and would change the model with the lowest mean L2 distance on Adamson and Replogle RPE1, so the choice is material.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Fig. 2 legend versus Source Data Fig. 2, sheet "Panel A" Methods restricts the single-perturbation analysis to perturbations predicted by all models: 73 for Adamson, 398 for Replogle K562 and 629 for Replogle RPE1. No sheet of Source Data Fig. 2 has these counts (Panel A: 48, 262 and 413 per method over two splits; Panel C: 20, 214 and 224 test or val rows per method).Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Methods 'Single perturbation benchmark setup' paragraph 4 versus Source Data Fig. 2, sheets "Panel A" and "Panel C" The test and val rows in Panel C (20, 214 and 224 per method for Adamson, Replogle K562 and Replogle RPE1) match neither the Fig. 2 legend counts nor the Methods counts. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Fig. 2, sheet "Panel C" versus the Fig. 2 legend and Methods The main text says CPA was not included in the single-perturbation benchmark, but method "cpa" has rows in Panel C. Fig. 2c was not extracted.Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Main text paragraph 16 versus Source Data Fig. 2, sheet "Panel C" Methods, coverage and source scBERT on Replogle RPE1 (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-replogle-rpe1-single Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Fig. 2 (MOESM4), sheet "Panel A", rows with dataset_name "replogle_rpe1_essential" and method "scbert", column "l2"; plotted as the red line in Fig. 2a

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Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines · Original source · Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML

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Stable ID: perturbation-ahlmann-20261011-config-scbert

areas: cells-tissues; single-cell
contexts: research
method types: foundation_model
reported name: scBERT (Source Data method "scbert")
source locator: Methods 'Software versions and parameters'; Source Data column method "scbert"
foundation model eligible: true
parameters: Fine-tuned to predict the perturbation labels of the training data; perturbed embedding computed as for UCE; ridge regression decoder.
access reuse: Prediction scripts for every model: github.com/const-ae/linear_perturbation_prediction-Paper (benchmark/src), archived on Zenodo; branch link, not pinned to a commit in the article.
version: scBERT commit 262fd4b9, model weights provided by the scBERT authors
provenance note: scBERT as run by Ahlmann-Eltze et al. 2025 for perturbation-effect prediction.

Related records

configuration of: scBERT
system: scBERT on Adamson (Ahlmann-Eltze et al. 2025)
system: scBERT on Norman doubles (Ahlmann-Eltze et al. 2025)
system: scBERT on Replogle K562 (Ahlmann-Eltze et al. 2025)
system: scBERT on Replogle RPE1 (Ahlmann-Eltze et al. 2025)

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