Evidence claim
Evidence summary: genetic perturbation response prediction
Summary of Assess models for genetic perturbation experiments
The five sources whose comparisons are shown on this page score how well models predict expression changes after genetic perturbations they were not trained on. Each uses its own split, gene set and metric, so their values are not pooled. Ahlmann-Eltze et al. 2025, who did not develop the deep models and ran them all themselves, scored held-out two-gene CRISPRa perturbations in K562 cells (Norman), training on every single-gene perturbation and half of the pairs. A baseline that adds the two single-gene effects had a mean L2 distance of 2.766 and a Pearson delta of 0.892 over the 1,000 most highly expressed genes, against 3.478 and 0.773 for Geneformer with a linear decoder, 4.096 and 0.631 for GEARS, 4.279 and 0.610 for both UCE and scBERT, 4.616 and 0.527 for scGPT, 4.722 and 0.537 for scFoundation, and 12.205 and 0.146 for CPA; predicting no change gave an L2 distance of 5.616. Rewire computed these means from the authors' Source Data, no spread is stated, and the UCE and scBERT runs used ground-truth expression of the perturbed genes, a possible test leakage. In PerturBench v4, a preprint whose authors trained every model and built the Linear, Latent Additive and Decoder baselines, the cosine similarity of predicted log fold change on held-out Norman pairs ranged from 0.41 for Biolord and 0.44 for GEARS to 0.79 for the Latent Additive baseline, with CPA at 0.76 and the Linear baseline at 0.60, each a mean over five training seeds. When effects were transferred to melanoma co-culture (Frangieh21) every model scored between 0.01 and 0.22 and every printed DEG recall was 0.000; on held-out cell line and cytokine states (Jiang24) scores ranged from 0.17 for Linear to 0.64 for the Decoder baseline, and the paper describes the held-out states in two different ways. In Csendes et al. 2025, whose authors are employees of Turbine Ltd. and built the baselines themselves, predicting the mean training response gave a higher Pearson delta than scGPT and scFoundation, as re-run by the same authors, on all four Perturb-seq datasets: 0.711, 0.557, 0.373 and 0.628 for Adamson, Norman, Replogle K562 and Replogle RPE1, against 0.641, 0.554, 0.327 and 0.596 for scGPT and 0.552, 0.459, 0.269 and 0.471 for scFoundation. Each dataset there has one split and one run per model with no spread printed, so small gaps such as 0.557 against 0.554 cannot be tested. The GEARS developers report a mean squared error of 0.216 for GEARS on Norman, against 0.354 for CPA and 0.551 for a no-change control, with spreads of unstated type. PertEval-scFM, whose authors trained GEARS from scratch, reports an area under its distribution-shift curve of 0.815 for GEARS against 4.484 for an MLP and 4.612 for a mean baseline, lower being better. All of these scores measure agreement with observed expression in held-out perturbations; they do not show which experiments will succeed.
Plain-language summary of what the reviewed evidence shows. Draft until reviewed.
Evidence
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Evidence table
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Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
56 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field summary Context-only references | GEARS primary supplement, Table6 perturbation-ahlmann-20261011-source-data-fig1: sheet 'Panel A', Norman, column l2; perturbation-ahlmann-20261011-source-data-ed-fig2: sheet 'Panel A', Norman, column r2_delta; perturbation-ahlmann-20261011-source-article: Fig. 1 and Extended Data Fig. 2 legends; Methods; lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3, 4 and 7, columns 'Cosine logFC' and 'DEG recall'; perturbation-response-20261009-source-csendes2025-supp: Supplementary Tables 1 and 2; perturbation-response-20261009-source-csendes2025: Results; Methods; Competing interests; coverage-source-gears-supp: Table 6, MSE; perteval-scfm-2025-source: Table 1, Norman single-gene AUSPC Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 10.1038/s41587-023-01905-6 publisher supplement | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed field summary Context-only references | PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) perturbation-ahlmann-20261011-source-data-fig1: sheet 'Panel A', Norman, column l2; perturbation-ahlmann-20261011-source-data-ed-fig2: sheet 'Panel A', Norman, column r2_delta; perturbation-ahlmann-20261011-source-article: Fig. 1 and Extended Data Fig. 2 legends; Methods; lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3, 4 and 7, columns 'Cosine logFC' and 'DEG recall'; perturbation-response-20261009-source-csendes2025-supp: Supplementary Tables 1 and 2; perturbation-response-20261009-source-csendes2025: Results; Methods; Competing interests; coverage-source-gears-supp: Table 6, MSE; perteval-scfm-2025-source: Table 1, Norman single-gene AUSPC Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. |
| Claimed field summary Context-only references | PertEval-scFM (Wenteler et al., ICML 2025), full text perturbation-ahlmann-20261011-source-data-fig1: sheet 'Panel A', Norman, column l2; perturbation-ahlmann-20261011-source-data-ed-fig2: sheet 'Panel A', Norman, column r2_delta; perturbation-ahlmann-20261011-source-article: Fig. 1 and Extended Data Fig. 2 legends; Methods; lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3, 4 and 7, columns 'Cosine logFC' and 'DEG recall'; perturbation-response-20261009-source-csendes2025-supp: Supplementary Tables 1 and 2; perturbation-response-20261009-source-csendes2025: Results; Methods; Competing interests; coverage-source-gears-supp: Table 6, MSE; perteval-scfm-2025-source: Table 1, Norman single-gene AUSPC Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: PMLR v267 wenteler25a (as served by the PMLR-affiliated mlresearch/v267 GitHub mirror; ETag "ed9f0fe44cf6edc939ee6950d024f65dcd8dd6f16414bd9f5297cda9395d6e58" at retrieval) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed field summary Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines perturbation-ahlmann-20261011-source-data-fig1: sheet 'Panel A', Norman, column l2; perturbation-ahlmann-20261011-source-data-ed-fig2: sheet 'Panel A', Norman, column r2_delta; perturbation-ahlmann-20261011-source-article: Fig. 1 and Extended Data Fig. 2 legends; Methods; lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3, 4 and 7, columns 'Cosine logFC' and 'DEG recall'; perturbation-response-20261009-source-csendes2025-supp: Supplementary Tables 1 and 2; perturbation-response-20261009-source-csendes2025: Results; Methods; Competing interests; coverage-source-gears-supp: Table 6, MSE; perteval-scfm-2025-source: Table 1, Norman single-gene AUSPC Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed field summary Context-only references | Source Data Extended Data Fig. 2, Ahlmann-Eltze et al. 2025 perturbation-ahlmann-20261011-source-data-fig1: sheet 'Panel A', Norman, column l2; perturbation-ahlmann-20261011-source-data-ed-fig2: sheet 'Panel A', Norman, column r2_delta; perturbation-ahlmann-20261011-source-article: Fig. 1 and Extended Data Fig. 2 legends; Methods; lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3, 4 and 7, columns 'Cosine logFC' and 'DEG recall'; perturbation-response-20261009-source-csendes2025-supp: Supplementary Tables 1 and 2; perturbation-response-20261009-source-csendes2025: Results; Methods; Competing interests; coverage-source-gears-supp: Table 6, MSE; perteval-scfm-2025-source: Table 1, Norman single-gene AUSPC Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Source Data Extended Data Fig. 2 (41592_2025_2772_MOESM6_ESM.xlsx) as published with the article | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed field summary Context-only references | Source Data Fig. 1, Ahlmann-Eltze et al. 2025 perturbation-ahlmann-20261011-source-data-fig1: sheet 'Panel A', Norman, column l2; perturbation-ahlmann-20261011-source-data-ed-fig2: sheet 'Panel A', Norman, column r2_delta; perturbation-ahlmann-20261011-source-article: Fig. 1 and Extended Data Fig. 2 legends; Methods; lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3, 4 and 7, columns 'Cosine logFC' and 'DEG recall'; perturbation-response-20261009-source-csendes2025-supp: Supplementary Tables 1 and 2; perturbation-response-20261009-source-csendes2025: Results; Methods; Competing interests; coverage-source-gears-supp: Table 6, MSE; perteval-scfm-2025-source: Table 1, Norman single-gene AUSPC Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Source Data Fig. 1 (41592_2025_2772_MOESM3_ESM.xlsx) as published with the article | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed field summary Context-only references | Benchmarking foundation cell models for post-perturbation RNA-seq prediction perturbation-ahlmann-20261011-source-data-fig1: sheet 'Panel A', Norman, column l2; perturbation-ahlmann-20261011-source-data-ed-fig2: sheet 'Panel A', Norman, column r2_delta; perturbation-ahlmann-20261011-source-article: Fig. 1 and Extended Data Fig. 2 legends; Methods; lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3, 4 and 7, columns 'Cosine logFC' and 'DEG recall'; perturbation-response-20261009-source-csendes2025-supp: Supplementary Tables 1 and 2; perturbation-response-20261009-source-csendes2025: Results; Methods; Competing interests; coverage-source-gears-supp: Table 6, MSE; perteval-scfm-2025-source: Table 1, Norman single-gene AUSPC Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: BMC Genomics 26:393, published 2025-04-23; PMC12016270 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed field summary Context-only references | Csendes et al. 2025, Supplementary Material 4 (Supplementary Tables 1-3) perturbation-ahlmann-20261011-source-data-fig1: sheet 'Panel A', Norman, column l2; perturbation-ahlmann-20261011-source-data-ed-fig2: sheet 'Panel A', Norman, column r2_delta; perturbation-ahlmann-20261011-source-article: Fig. 1 and Extended Data Fig. 2 legends; Methods; lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3, 4 and 7, columns 'Cosine logFC' and 'DEG recall'; perturbation-response-20261009-source-csendes2025-supp: Supplementary Tables 1 and 2; perturbation-response-20261009-source-csendes2025: Results; Methods; Competing interests; coverage-source-gears-supp: Table 6, MSE; perteval-scfm-2025-source: Table 1, Norman single-gene AUSPC Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 12864_2025_11600_MOESM4_ESM.xlsx from the Europe PMC supplementary bundle (hash is of the workbook; the bundle zip is rebuilt per request) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Pinned records1077 values
| GEARS primary supplement, Table6 perturbation-ahlmann-20261011-source-data-fig1: sheet 'Panel A', Norman, column l2; perturbation-ahlmann-20261011-source-data-ed-fig2: sheet 'Panel A', Norman, column r2_delta; perturbation-ahlmann-20261011-source-article: Fig. 1 and Extended Data Fig. 2 legends; Methods; lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3, 4 and 7, columns 'Cosine logFC' and 'DEG recall'; perturbation-response-20261009-source-csendes2025-supp: Supplementary Tables 1 and 2; perturbation-response-20261009-source-csendes2025: Results; Methods; Competing interests; coverage-source-gears-supp: Table 6, MSE; perteval-scfm-2025-source: Table 1, Norman single-gene AUSPC Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 10.1038/s41587-023-01905-6 publisher supplement | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Pinned records1077 values
| PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) perturbation-ahlmann-20261011-source-data-fig1: sheet 'Panel A', Norman, column l2; perturbation-ahlmann-20261011-source-data-ed-fig2: sheet 'Panel A', Norman, column r2_delta; perturbation-ahlmann-20261011-source-article: Fig. 1 and Extended Data Fig. 2 legends; Methods; lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3, 4 and 7, columns 'Cosine logFC' and 'DEG recall'; perturbation-response-20261009-source-csendes2025-supp: Supplementary Tables 1 and 2; perturbation-response-20261009-source-csendes2025: Results; Methods; Competing interests; coverage-source-gears-supp: Table 6, MSE; perteval-scfm-2025-source: Table 1, Norman single-gene AUSPC Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. |
Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
8 source records and release history
- 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
- Source Data Fig. 1, Ahlmann-Eltze et al. 2025 · Original source · Source Data Fig. 1 (41592_2025_2772_MOESM3_ESM.xlsx) as published with the article
- Source Data Extended Data Fig. 2, Ahlmann-Eltze et al. 2025 · Original source · Source Data Extended Data Fig. 2 (41592_2025_2772_MOESM6_ESM.xlsx) as published with the article
- PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Original source · arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version)
- Benchmarking foundation cell models for post-perturbation RNA-seq prediction · Original source · BMC Genomics 26:393, published 2025-04-23; PMC12016270 full-text XML
- Csendes et al. 2025, Supplementary Material 4 (Supplementary Tables 1-3) · Original source · 12864_2025_11600_MOESM4_ESM.xlsx from the Europe PMC supplementary bundle (hash is of the workbook; the bundle zip is rebuilt per request)
- GEARS primary supplement, Table6 · Original source · 10.1038/s41587-023-01905-6 publisher supplement
- PertEval-scFM (Wenteler et al., ICML 2025), full text · Original source · PMLR v267 wenteler25a (as served by the PMLR-affiliated mlresearch/v267 GitHub mirror; ETag "ed9f0fe44cf6edc939ee6950d024f65dcd8dd6f16414bd9f5297cda9395d6e58" at retrieval)
Technical metadata and extraction receipts
Stable ID: use-case-summary-genetic-perturbation-response
- field
- summary
- value
- The five sources whose comparisons are shown on this page score how well models predict expression changes after genetic perturbations they were not trained on. Each uses its own split, gene set and metric, so their values are not pooled. Ahlmann-Eltze et al. 2025, who did not develop the deep models and ran them all themselves, scored held-out two-gene CRISPRa perturbations in K562 cells (Norman), training on every single-gene perturbation and half of the pairs. A baseline that adds the two single-gene effects had a mean L2 distance of 2.766 and a Pearson delta of 0.892 over the 1,000 most highly expressed genes, against 3.478 and 0.773 for Geneformer with a linear decoder, 4.096 and 0.631 for GEARS, 4.279 and 0.610 for both UCE and scBERT, 4.616 and 0.527 for scGPT, 4.722 and 0.537 for scFoundation, and 12.205 and 0.146 for CPA; predicting no change gave an L2 distance of 5.616. Rewire computed these means from the authors' Source Data, no spread is stated, and the UCE and scBERT runs used ground-truth expression of the perturbed genes, a possible test leakage. In PerturBench v4, a preprint whose authors trained every model and built the Linear, Latent Additive and Decoder baselines, the cosine similarity of predicted log fold change on held-out Norman pairs ranged from 0.41 for Biolord and 0.44 for GEARS to 0.79 for the Latent Additive baseline, with CPA at 0.76 and the Linear baseline at 0.60, each a mean over five training seeds. When effects were transferred to melanoma co-culture (Frangieh21) every model scored between 0.01 and 0.22 and every printed DEG recall was 0.000; on held-out cell line and cytokine states (Jiang24) scores ranged from 0.17 for Linear to 0.64 for the Decoder baseline, and the paper describes the held-out states in two different ways. In Csendes et al. 2025, whose authors are employees of Turbine Ltd. and built the baselines themselves, predicting the mean training response gave a higher Pearson delta than scGPT and scFoundation, as re-run by the same authors, on all four Perturb-seq datasets: 0.711, 0.557, 0.373 and 0.628 for Adamson, Norman, Replogle K562 and Replogle RPE1, against 0.641, 0.554, 0.327 and 0.596 for scGPT and 0.552, 0.459, 0.269 and 0.471 for scFoundation. Each dataset there has one split and one run per model with no spread printed, so small gaps such as 0.557 against 0.554 cannot be tested. The GEARS developers report a mean squared error of 0.216 for GEARS on Norman, against 0.354 for CPA and 0.551 for a no-change control, with spreads of unstated type. PertEval-scFM, whose authors trained GEARS from scratch, reports an area under its distribution-shift curve of 0.815 for GEARS against 4.484 for an MLP and 4.612 for a mean baseline, lower being better. All of these scores measure agreement with observed expression in held-out perturbations; they do not show which experiments will succeed.
- source locator
- perturbation-ahlmann-20261011-source-data-fig1: sheet 'Panel A', Norman, column l2; perturbation-ahlmann-20261011-source-data-ed-fig2: sheet 'Panel A', Norman, column r2_delta; perturbation-ahlmann-20261011-source-article: Fig. 1 and Extended Data Fig. 2 legends; Methods; lit-cells-networks-20261011-perturbench-v4-source-wu2025: Tables 3, 4 and 7, columns 'Cosine logFC' and 'DEG recall'; perturbation-response-20261009-source-csendes2025-supp: Supplementary Tables 1 and 2; perturbation-response-20261009-source-csendes2025: Results; Methods; Competing interests; coverage-source-gears-supp: Table 6, MSE; perteval-scfm-2025-source: Table 1, Norman single-gene AUSPC
- review
- method: independent-cell-check; scope-review; source-hash-verification; method note: Rebuilt the use case's judgements with deriveUseCaseInputs and compared every number with the printed_value of the stored source_checked result it comes from, on an evaluation of an active judgement, including rounding, tool, dataset and split. Checked each range and ranking against every active evaluation on the protocol, not only the cited rows. Checked origin, publication status, truth-set and leakage limits against the judgement, protocol, dataset and source records, that each cited source is source_checked with no evidence_concerns, that no withheld judgement is described, and that nothing is recommended. Re-downloaded Source Data Fig. 1 and Extended Data Fig. 2, matched both SHA-256 values, and confirmed with a separate reader that the UCE and scBERT Norman rows are distinct and that their means agree to three decimals.; reviewer: claude; reviewer note: Separate Claude review agent, independent of the agent that drafted the summary; the reviewer corrected the text; no human review claimed; reviewed at: 2026-10-12T15:00:00Z; note: Drafted in data/omics/use-case-followups-20261012/summary-drafts.md and corrected in this review: limited the opening to the sources whose comparisons are shown, named the UCE and scBERT ground-truth input as possible test leakage, and added the article and Csendes main-text locators. Descriptive summary only; no execution, human scientific review or clinical validation. See docs/reviews/use-cases/use-case-summaries-2026-10-12.md.
- pins
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