rewirebio.iobenchmarks
Protocol

Adamson: expression prediction for unseen single perturbations (Ahlmann-Eltze et al. 2025 Fig. 2a and Extended Data Fig. 8a)

Models are trained on the perturbations in the training part of a GEARS 'simulation' split and predict the mean expression profile for unseen single perturbations. Error is the L2 distance, and agreement the Pearson delta, between the mean predicted and observed expression over the 1,000 most highly expressed genes in the control condition, per perturbation.

7 evaluations · 14 results

Overview

An explanatory profile has not yet been reviewed for this record.

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

7 recorded evaluations, 14 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.

View coverage and remaining gaps across all benchmarks

Results

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

All evaluations

7 evaluations · 14 results. Different protocols are not a single leaderboard.

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)

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 configurationL2 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)
GEARS (Ahlmann-Eltze et al. 2025)Evaluation3.87Computed by Rewire from the authors' source data (best in column)0.726Computed by Rewire from the authors' source data
scBERT (Ahlmann-Eltze et al. 2025)Evaluation3.88Computed by Rewire from the authors' source data0.728Computed by Rewire from the authors' source data (best in column)
Geneformer (Ahlmann-Eltze et al. 2025)Evaluation3.91Computed by Rewire from the authors' source data0.726Computed by Rewire from the authors' source data
Mean (Ahlmann-Eltze et al. 2025)Evaluation3.94Computed by Rewire from the authors' source data0.724Computed by Rewire from the authors' source data
UCE (Ahlmann-Eltze et al. 2025)Evaluation3.94Computed by Rewire from the authors' source data0.724Computed by Rewire from the authors' source data
Linear model (G and P from training data) (Ahlmann-Eltze et al. 2025)Evaluation4.08Computed by Rewire from the authors' source data0.717Computed by Rewire from the authors' source data
scGPT (Ahlmann-Eltze et al. 2025)Evaluation4.95Computed by Rewire from the authors' source data0.595Computed by Rewire from the authors' source data
All 14 result rows with coverage, uncertainty and sources
Filter evaluations

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configurationProtocol and datasetFindingEvidence and details
Configuration: GEARS (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)
0.726 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
Methods, coverage and source

GEARS 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 "gears", column "r2_delta"; plotted as the red line in Extended Data Fig. 8a
Configuration: Geneformer (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)
0.726 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
Methods, coverage and source

Geneformer 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 "geneformer", column "r2_delta"; plotted as the red line in Extended Data Fig. 8a
Configuration: Linear model (G and P from training data) (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)
0.717 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

Author-reported evaluation · Source checked
Methods, coverage and source

Linear model (G and P from training data) 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 "lpm_selftrained", column "r2_delta"; plotted as the red line in Extended Data Fig. 8a
Configuration: Mean (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)
0.724 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

Author-reported evaluation · Source checked
Methods, coverage and source

Mean 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 "mean", column "r2_delta"; plotted as the red line in Extended Data Fig. 8a
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
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: scGPT (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)
0.595 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
Methods, coverage and source

scGPT 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 "scgpt", column "r2_delta"; plotted as the red line in Extended Data Fig. 8a
Configuration: UCE (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)
0.724 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
Methods, coverage and source

UCE 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 "uce", column "r2_delta"; plotted as the red line in Extended Data Fig. 8a
Configuration: GEARS (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)
3.87 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
Methods, coverage and source

GEARS 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 "gears", column "l2"; plotted as the red line in Fig. 2a
Configuration: Geneformer (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)
3.91 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
Methods, coverage and source

Geneformer 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 "geneformer", column "l2"; plotted as the red line in Fig. 2a
Configuration: Linear model (G and P from training data) (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)
4.08 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

Author-reported evaluation · Source checked
Methods, coverage and source

Linear model (G and P from training data) 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 "lpm_selftrained", column "l2"; plotted as the red line in Fig. 2a
Configuration: Mean (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)
3.94 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

Author-reported evaluation · Source checked
Methods, coverage and source

Mean 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 "mean", column "l2"; plotted as the red line in 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)
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
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: scGPT (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)
4.95 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
Methods, coverage and source

scGPT 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 "scgpt", column "l2"; plotted as the red line in Fig. 2a
Configuration: UCE (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)
3.94 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
Methods, coverage and source

UCE 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 "uce", column "l2"; plotted as the red line in Fig. 2a

Source checking is not independent reproduction. Release 2026-10-22-1b7cf027e4f6.

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Reference methods help show what a model adds beyond simple controls. We track a null control and a conventional method for each protocol.

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Author-reported evaluations
2
External evaluations
5

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Null control

Proposed control: requires review

No-change prediction under matched control conditions

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This is a suggested selection rule, not a validated method or a measured score.

Conventional reference

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Training-only mean-effect or linear prediction

Protocol-specific applicability, permitted inputs, access, split, evaluator and execution requirements need review before implementation or execution.

This is a suggested selection rule, not a validated method or a measured score.

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Technical metadata and extraction receipts

Stable ID: perturbation-ahlmann-20261011-protocol-adamson-single

areas
cells-tissues; single-cell
contexts
research
protocol
Models are trained on the perturbations in the training part of a GEARS 'simulation' split and predict the mean expression profile for unseen single perturbations. Error is the L2 distance, and agreement the Pearson delta, between the mean predicted and observed expression over the 1,000 most highly expressed genes in the control condition, per perturbation.
scope note
Split: GEARS 'simulation' test-training split, run twice (two splits). 48 held-out perturbation-split rows per model in the Source Data.
denominator
48
metric definition
L2 distance: square root of the summed squared differences over the 1,000 most highly expressed genes in the control condition. Pearson delta: correlation of predicted and observed expression after subtracting the control expression (Methods 'Evaluation metrics').
aggregation
Mean per model over held-out perturbations, pooling the rows labelled test and val and all splits ("The horizontal red lines show the mean per model").
source locator
Main text paragraphs 13 and 16; Fig. 2 legend; Extended Data Fig. 8 legend; Methods 'Single perturbation benchmark setup' and 'Evaluation metrics'
limitations
The paper prints no per-model value; the means are computed by Rewire from the authors' Source Data rows and have not been checked against a printed number.; Four datasets, all from cancer cell lines, chosen because the GEARS, scGPT and scFoundation publications used them; the original quality control was not improved, so perturbations that did not affect their own target gene are included (main text paragraph 22).; The authors ran every model themselves with default parameters as far as possible and asked the model developers to review their code (Methods 'Software versions and parameters').; The Fig. 2 legend's perturbation counts are the split 1 counts only, and the Methods counts of perturbations predicted by all models (73, 398 and 629) match no Source Data sheet; see the evidence concerns on the Source Data Fig. 2 and Extended Data Fig. 8 source records.; scFoundation and CPA are not in this benchmark (main text paragraph 16).; UCE and scBERT post-perturbation embeddings use ground-truth expression of the perturbed genes; the authors note for UCE that this could leak test data (Methods 'Software versions and parameters').
missing metadata
uncertainty: reason: unreported; note: The figures show per-perturbation points and the mean; no spread of the mean is stated
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