Configuration: 1-NN on GenePT embeddings (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: K562, RPE1, HepG2 and Jurkat test sets (Sun et al. 2026) 3.39% 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, four cell lines
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 'Average' 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: K562, RPE1, HepG2 and Jurkat test sets (Sun et al. 2026) 0.91% 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, four cell lines
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 'Average' 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: K562, RPE1, HepG2 and Jurkat test sets (Sun et al. 2026) 14.7% 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, four cell lines
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 'Average' Configuration: Perturbation majority vote (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: K562, RPE1, HepG2 and Jurkat test sets (Sun et al. 2026) 0.03% 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, four cell lines
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 'Average' Configuration: Scouter (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: K562, RPE1, HepG2 and Jurkat test sets (Sun et al. 2026) 64.1% 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, four cell lines
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 'Average' Configuration: scVI perturbation model, mean output (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: K562, RPE1, HepG2 and Jurkat test sets (Sun et al. 2026) 73.4% 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 mean on Sun et al. 2026 within-cell-line DE classification, four cell lines
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 'Average' Configuration: scVI perturbation model, sampled output (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: K562, RPE1, HepG2 and Jurkat test sets (Sun et al. 2026) 4.28% 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, four cell lines
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 'Average' Configuration: VAE adapted from scGen (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: K562, RPE1, HepG2 and Jurkat test sets (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 within-cell-line DE classification, four cell lines
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 'Average' 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 unseen-perturbation DE classification (within cell line) Dataset subset: Jurkat essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026) 1.69% 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, Jurkat
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 'Jurkat' 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: Jurkat essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026) 0.28% 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, Jurkat
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 'Jurkat' 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: Jurkat essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026) 10.4% 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 Logistic regression on Sun et al. 2026 within-cell-line DE classification, Jurkat
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 'Jurkat' Configuration: Perturbation majority vote (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: Jurkat 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, Jurkat
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 'Jurkat' Configuration: Scouter (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: Jurkat essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026) 67.1% 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, Jurkat
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 'Jurkat' Configuration: scVI perturbation model, mean output (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: Jurkat essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026) 72.9% 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
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, Jurkat
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 'Jurkat' Configuration: scVI perturbation model, sampled output (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: Jurkat essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026) 5.37% 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, Jurkat
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 'Jurkat' Configuration: VAE adapted from scGen (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: Jurkat essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026) 65.7% 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, Jurkat
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 'Jurkat' Configuration: 1-NN on GenePT embeddings (Sun et al. 2026) Protocol: Sun et al. 2026 unseen-perturbation DE classification (within cell line) Dataset subset: K562 essential-gene Perturb-seq, 40% test perturbations (Sun et al. 2026) 4.27% 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, K562
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 'K562'