Dataset subset

HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)

HepG2 essential-gene screen, normalised to 10,000 counts and log1p; perturbations split 60/40 into train and test.

Evaluation results

17 evaluations · 17 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: 1-NN on GenePT embeddings (Sun et al. 2026)	Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	2.98% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source 1-NN on Sun et al. 2026 within-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-within-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 1, row '1-NN', column 'HepG2'
Configuration: Weighted 10-NN on GenePT embeddings (Sun et al. 2026)	Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	0.86% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source 10-NN on Sun et al. 2026 within-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-within-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 1, row '10-NN', column 'HepG2'
Configuration: Per-gene L2 logistic regression on GenePT embeddings (Sun et al. 2026)	Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	12.9% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source Logistic regression on Sun et al. 2026 within-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-within-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 1, row 'Logistic regression', column 'HepG2'
Configuration: Perturbation majority vote (Sun et al. 2026)	Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	0% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source Majority vote on Sun et al. 2026 within-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-within-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 1, row 'Majority vote', column 'HepG2'
Configuration: Scouter (Sun et al. 2026)	Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	55.1% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source Scouter on Sun et al. 2026 within-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-within-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 1, row 'Scouter', column 'HepG2'
Configuration: scVI perturbation model, mean output (Sun et al. 2026)	Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	67% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source scVI mean on Sun et al. 2026 within-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-within-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 1, row 'scVI mean', column 'HepG2'
Configuration: scVI perturbation model, sampled output (Sun et al. 2026)	Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	3.75% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source scVI sampling on Sun et al. 2026 within-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-within-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 1, row 'scVI sampling', column 'HepG2'
Configuration: VAE adapted from scGen (Sun et al. 2026)	Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	54.4% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source VAE scGen on Sun et al. 2026 within-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-within-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 1, row 'VAE scGen', column 'HepG2'
Configuration: 1-NN on GenePT embeddings (Sun et al. 2026)	Protocol: Sun et al. 2026 cellular-context DE classification (cross cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	2.76% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source 1-NN on Sun et al. 2026 cross-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-cross-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 3, row '1-NN', column 'HepG2'
Configuration: Weighted 10-NN on GenePT embeddings (Sun et al. 2026)	Protocol: Sun et al. 2026 cellular-context DE classification (cross cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	0.47% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source 10-NN on Sun et al. 2026 cross-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-cross-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 3, row '10-NN', column 'HepG2'
Configuration: Per-gene L2 logistic regression on GenePT embeddings (Sun et al. 2026)	Protocol: Sun et al. 2026 cellular-context DE classification (cross cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	18.9% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source Logistic regression on Sun et al. 2026 cross-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-cross-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 3, row 'Logistic regression', column 'HepG2'
Configuration: Perturbation majority vote (Sun et al. 2026)	Protocol: Sun et al. 2026 cellular-context DE classification (cross cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	0% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source Majority vote on Sun et al. 2026 cross-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-cross-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 3, row 'Majority vote', column 'HepG2'
Configuration: scVI perturbation model, mean output (Sun et al. 2026)	Protocol: Sun et al. 2026 cellular-context DE classification (cross cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	81.2% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source scVI mean on Sun et al. 2026 cross-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-cross-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 3, row 'scVI mean', column 'HepG2'
Configuration: scVI perturbation model, sampled output (Sun et al. 2026)	Protocol: Sun et al. 2026 cellular-context DE classification (cross cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	33.4% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source scVI sampling on Sun et al. 2026 cross-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-cross-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 3, row 'scVI sampling', column 'HepG2'
Configuration: STATE (few-shot cross-cell-line) (Sun et al. 2026)	Protocol: Sun et al. 2026 cellular-context DE classification (cross cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	2.68% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source STATE on Sun et al. 2026 cross-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-cross-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 3, row 'STATE', column 'HepG2'
Configuration: TranScouter (Sun et al. 2026)	Protocol: Sun et al. 2026 cellular-context DE classification (cross cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	62.1% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source TranScouter on Sun et al. 2026 cross-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-cross-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 3, row 'TranScouter', column 'HepG2'
Configuration: VAE adapted from scGen (Sun et al. 2026)	Protocol: Sun et al. 2026 cellular-context DE classification (cross cell line) Dataset subset: HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026)	62.6% Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence)) percent · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Methods describe results on both the full data and a PerturbQA-downsampled subset ('We annotate our results to distinguish between these two settings'), but the captions of Tables 1-4 do not say which setting they report. The printed true DE prevalence (1.75-4.41%) matches the full data's 95-98% non-DE share (Table 5), not the downsampled set; this is an inference, not a printed statement.Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Section 3.2 'Full vs. Curated Dataset'; Tables 1-5 Methods, coverage and source VAE scGen on Sun et al. 2026 cross-cell-line DE classification, HepG2 lit-cells-networks-20261011-sun2026de-protocol-cross-cell-line Aggregation: Not reported Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Table 3, row 'VAE scGen', column 'HepG2'

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Claims, original sources and review scope · Release 2026-10-23-5347695500ed
Property and statement	Original source and location	Review and provenance
Source dataset label HepG2 Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Sections 3.1-3.2; Table 5 Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.source_dataset_label` Source artifact SHA-256: `fa1c6a6345c45e14b9c373dfa8aff12633ae646feece1cfa3899c3ed61f6cfdf` Hash scope: Exact bytes of one download; bioRxiv serves slightly different PDF bytes per download, so the gzip copy in artifacts/ is the reference. Inspected artifact
Source location Sections 3.1-3.2; Table 5 Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Sections 3.1-3.2; Table 5 Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.source_locator` Source artifact SHA-256: `fa1c6a6345c45e14b9c373dfa8aff12633ae646feece1cfa3899c3ed61f6cfdf` Hash scope: Exact bytes of one download; bioRxiv serves slightly different PDF bytes per download, so the gzip copy in artifacts/ is the reference. Inspected artifact
Split 60% train / 40% test perturbations Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Sections 3.1-3.2; Table 5 Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.split` Source artifact SHA-256: `fa1c6a6345c45e14b9c373dfa8aff12633ae646feece1cfa3899c3ed61f6cfdf` Hash scope: Exact bytes of one download; bioRxiv serves slightly different PDF bytes per download, so the gzip copy in artifacts/ is the reference. Inspected artifact
Description HepG2 essential-gene screen, normalised to 10,000 counts and log1p; perturbations split 60/40 into train and test. Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Sections 3.1-3.2; Table 5 Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `description` Source artifact SHA-256: `fa1c6a6345c45e14b9c373dfa8aff12633ae646feece1cfa3899c3ed61f6cfdf` Hash scope: Exact bytes of one download; bioRxiv serves slightly different PDF bytes per download, so the gzip copy in artifacts/ is the reference. Inspected artifact
Name HepG2 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026) Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Sections 3.1-3.2; Table 5 Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `name` Source artifact SHA-256: `fa1c6a6345c45e14b9c373dfa8aff12633ae646feece1cfa3899c3ed61f6cfdf` Hash scope: Exact bytes of one download; bioRxiv serves slightly different PDF bytes per download, so the gzip copy in artifacts/ is the reference. Inspected artifact

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Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models · Original source · bioRxiv preprint version 1, posted 2026-07-24

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Stable ID: lit-cells-networks-20261011-sun2026de-data-hepg2

areas: single-cell
contexts: research
split: 60% train / 40% test perturbations
source dataset label: HepG2
source locator: Sections 3.1-3.2; Table 5
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