Dataset subset

Jiang24 (PerturBench v4 covariate-transfer split)

Jiang24 genetic perturbations across cell lines and cytokine treatments, with 70% of perturbations held out in some states.

Evaluation results

7 evaluations · 48 results. Different protocols are not a single leaderboard.

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: CPA*	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.40 ± 9 × 10 − 3 Perturbench cosine logfc rank fraction · lower Uncertainty: SD 0.009 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source CPA ∗ on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'CPA ∗', column 'Cosine LogFC rank'
Configuration: CPA*	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.60 ± 7 × 10 − 4 Perturbench cosine logfc fraction · higher Uncertainty: SD 0.0007 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source CPA ∗ on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'CPA ∗', column 'Cosine logFC'
Configuration: CPA*	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.004 ± 2 × 10 − 3 Recall (DEG recall: fraction of the top 20 ground-truth DEGs (scanpy t-scores against controls) recalled from predicted cells) fraction · higher Uncertainty: SD 0.002 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source CPA ∗ on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'CPA ∗', column 'DEG recall'
Configuration: CPA*	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	2.5 ± 4 × 10 − 3 Perturbench energy mmd (PCA space: top 256 principal components fitted on the ground-truth test split) unit-unreported · lower Uncertainty: SD 0.004 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source CPA ∗ on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'CPA ∗', column 'MMD PCA'
Configuration: CPA*	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	8.0 ± 2 × 10 − 3 Perturbench energy mmd (gene expression space) unit-unreported · lower Uncertainty: SD 0.002 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source CPA ∗ on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 11, row 'CPA ∗', column 'MMD'
Configuration: CPA* (noAdv)	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.39 ± 1 × 10 − 2 Perturbench cosine logfc rank fraction · lower Uncertainty: SD 0.01 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source CPA ∗ (noAdv) on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'CPA ∗ (noAdv)', column 'Cosine LogFC rank'
Configuration: CPA* (noAdv)	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.60 ± 2 × 10 − 3 Perturbench cosine logfc fraction · higher Uncertainty: SD 0.002 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source CPA ∗ (noAdv) on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'CPA ∗ (noAdv)', column 'Cosine logFC'
Configuration: CPA* (noAdv)	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.005 ± 6 × 10 − 4 Recall (DEG recall: fraction of the top 20 ground-truth DEGs (scanpy t-scores against controls) recalled from predicted cells) fraction · higher Uncertainty: SD 0.0006 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source CPA ∗ (noAdv) on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'CPA ∗ (noAdv)', column 'DEG recall'
Configuration: CPA* (noAdv)	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	2.5 ± 3 × 10 − 3 Perturbench energy mmd (PCA space: top 256 principal components fitted on the ground-truth test split) unit-unreported · lower Uncertainty: SD 0.003 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source CPA ∗ (noAdv) on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'CPA ∗ (noAdv)', column 'MMD PCA'
Configuration: CPA* (noAdv)	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	8.0 ± 2 × 10 − 3 Perturbench energy mmd (gene expression space) unit-unreported · lower Uncertainty: SD 0.002 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source CPA ∗ (noAdv) on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 11, row 'CPA ∗ (noAdv)', column 'MMD'
Configuration: CPA* (noAdv)	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.40 ± 1 × 10 − 2 Perturbench RMSE rank fraction · lower Uncertainty: SD 0.01 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source CPA ∗ (noAdv) on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 11, row 'CPA ∗ (noAdv)', column 'RMSE mean rank'
Configuration: CPA* (noAdv)	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.015 ± 4 × 10 − 5 Perturbench RMSE of mean unit-unreported · lower Uncertainty: SD 0.00004 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source CPA ∗ (noAdv) on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 11, row 'CPA ∗ (noAdv)', column 'RMSE mean'
Configuration: CPA*	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.42 ± 1 × 10 − 2 Perturbench RMSE rank fraction · lower Uncertainty: SD 0.01 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source CPA ∗ on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 11, row 'CPA ∗', column 'RMSE mean rank'
Configuration: CPA*	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.015 ± 5 × 10 − 5 Perturbench RMSE of mean unit-unreported · lower Uncertainty: SD 0.00005 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source CPA ∗ on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 11, row 'CPA ∗', column 'RMSE mean'
Configuration: Decoder	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.32 ± 8 × 10 − 3 Perturbench cosine logfc rank fraction · lower Uncertainty: SD 0.008 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Decoder on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'Decoder', column 'Cosine LogFC rank'
Configuration: Decoder	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.64 ± 8 × 10 − 4 Perturbench cosine logfc fraction · higher Uncertainty: SD 0.0008 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Decoder on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'Decoder', column 'Cosine logFC'
Configuration: Decoder	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	2.6 ± 2 × 10 − 3 Perturbench energy mmd (PCA space: top 256 principal components fitted on the ground-truth test split) unit-unreported · lower Uncertainty: SD 0.002 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Decoder on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'Decoder', column 'MMD PCA'
Configuration: Decoder	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	8.0 ± 2 × 10 − 3 Perturbench energy mmd (gene expression space) unit-unreported · lower Uncertainty: SD 0.002 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Decoder on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 11, row 'Decoder', column 'MMD'
Configuration: Decoder	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.32 ± 5 × 10 − 3 Perturbench RMSE rank fraction · lower Uncertainty: SD 0.005 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Decoder on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 11, row 'Decoder', column 'RMSE mean rank'
Configuration: Decoder	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.015 ± 3 × 10 − 5 Perturbench RMSE of mean unit-unreported · lower Uncertainty: SD 0.00003 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Decoder on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 11, row 'Decoder', column 'RMSE mean'
Configuration: LA	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.38 ± 6 × 10 − 3 Perturbench cosine logfc rank fraction · lower Uncertainty: SD 0.006 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source LA on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'LA', column 'Cosine LogFC rank'
Configuration: LA	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.47 ± 1 × 10 − 3 Perturbench cosine logfc fraction · higher Uncertainty: SD 0.001 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source LA on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'LA', column 'Cosine logFC'
Configuration: LA	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	0.001 ± 5 × 10 − 4 Recall (DEG recall: fraction of the top 20 ground-truth DEGs (scanpy t-scores against controls) recalled from predicted cells) fraction · higher Uncertainty: SD 0.0005 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source LA on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'LA', column 'DEG recall'
Configuration: LA	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	2.6 ± 2 × 10 − 3 Perturbench energy mmd (PCA space: top 256 principal components fitted on the ground-truth test split) unit-unreported · lower Uncertainty: SD 0.002 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source LA on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 4, row 'LA', column 'MMD PCA'
Configuration: LA	Protocol: PerturBench v4 covariate transfer to held-out perturbations in held-out biological states on Jiang24 Dataset subset: Jiang24 (PerturBench v4 covariate-transfer split)	8.0 ± 9 × 10 − 4 Perturbench energy mmd (gene expression space) unit-unreported · lower Uncertainty: SD 0.0009 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source LA on PerturBench v4 jiang24 lit-cells-networks-20261011-perturbench-v4-protocol-jiang24 Aggregation: Not reported PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) · Table 11, row 'LA', column 'MMD'

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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 location Table 1; Table 4 caption; Appendix D.4 'Jiang24 splits' Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ Table 1; Table 4 caption; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.source_locator` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact
Split Jiang24 genetic perturbations across cell lines and cytokine treatments, with 70% of perturbations held out in some states. Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ Table 1; Table 4 caption; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.split` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact
Description Jiang24 genetic perturbations across cell lines and cytokine treatments, with 70% of perturbations held out in some states. Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ Table 1; Table 4 caption; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `description` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact
Name Jiang24 (PerturBench v4 covariate-transfer split) Context-only references	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (arXiv v4) Original source ↗ Table 1; Table 4 caption; Appendix D.4 'Jiang24 splits' Version: arXiv 2408.10609v4, posted 2025-10-24 (NeurIPS 2025 Datasets and Benchmarks Track version) Retrieved: 2026-10-11T21:29:35Z	not individually reviewed No individual claim review recorded Audit details Field: `name` Source artifact SHA-256: `b73f2644b129ca209b69812ad467d595e15c283f6cae5c3c9bbc5a4c5870996a` Hash scope: Exact bytes of the arXiv HTML rendering of v4 as retrieved; arXiv may regenerate HTML, so the hash describes this retrieval. Inspected artifact

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

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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)

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Stable ID: lit-cells-networks-20261011-perturbench-v4-data-jiang24

areas: single-cell
contexts: research
split: Jiang24 genetic perturbations across cell lines and cytokine treatments, with 70% of perturbations held out in some states.
source locator: Table 1; Table 4 caption; Appendix D.4 'Jiang24 splits'
missing metadata: test count: reason: unextracted; note: Per-split perturbation counts are in the authors' split notebooks, not printed in the tables; version: reason: unreported

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