Evaluation

Logistic regression on Sun et al. 2026 within-cell-line DE classification, four cell lines

Published comparison transcribed from the pinned source; not reproduced.

Evaluation results

1 evaluation · 6 results. Different protocols are not a single leaderboard.

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)

Sorted by Accuracy (3-class (non-DE, up, down) accuracy within true |log2 FC| quantile Q1 (0–0.016); quantile bins per cell line, averaged over four cell lines) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Accuracy (3-class (non-DE, up, down) accuracy within true \|log2 FC\| quantile Q1 (0–0.016); quantile bins per cell line, averaged over four cell lines)	Accuracy (3-class (non-DE, up, down) accuracy within true \|log2 FC\| quantile Q2 (0.016–0.034); quantile bins per cell line, averaged over four cell lines)	Accuracy (3-class (non-DE, up, down) accuracy within true \|log2 FC\| quantile Q3 (0.034–0.060); quantile bins per cell line, averaged over four cell lines)	Accuracy (3-class (non-DE, up, down) accuracy within true \|log2 FC\| quantile Q4 (0.060–0.108); quantile bins per cell line, averaged over four cell lines)	Accuracy (3-class (non-DE, up, down) accuracy within true \|log2 FC\| quantile Q5 (>0.108); quantile bins per cell line, averaged over four cell lines)	Proportion (share of perturbation-gene pairs predicted as differentially expressed (compare with the printed true DE prevalence))
Per-gene L2 logistic regression on GenePT embeddings (Sun et al. 2026)Evaluation	92.4%	91.5%	89.6%	85%	70.6%	14.7%

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
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: 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)	92.4% Accuracy (3-class (non-DE, up, down) accuracy within true \|log2 FC\| quantile Q1 (0–0.016); quantile bins per cell line, averaged over four cell lines) percent · higher 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 2, row 'Logistic regression', column 'Q1 (0–0.016)'
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)	91.5% Accuracy (3-class (non-DE, up, down) accuracy within true \|log2 FC\| quantile Q2 (0.016–0.034); quantile bins per cell line, averaged over four cell lines) percent · higher 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 2, row 'Logistic regression', column 'Q2 (0.016–0.034)'
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)	89.6% Accuracy (3-class (non-DE, up, down) accuracy within true \|log2 FC\| quantile Q3 (0.034–0.060); quantile bins per cell line, averaged over four cell lines) percent · higher 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 2, row 'Logistic regression', column 'Q3 (0.034–0.060)'
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)	85% Accuracy (3-class (non-DE, up, down) accuracy within true \|log2 FC\| quantile Q4 (0.060–0.108); quantile bins per cell line, averaged over four cell lines) percent · higher 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 2, row 'Logistic regression', column 'Q4 (0.060–0.108)'
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)	70.6% Accuracy (3-class (non-DE, up, down) accuracy within true \|log2 FC\| quantile Q5 (>0.108); quantile bins per cell line, averaged over four cell lines) percent · higher 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 2, row 'Logistic regression', column 'Q5 (>0.108)'

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Evaluation procedure

Sun et al. 2026 unseen-perturbation DE classification (within cell line)

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)

origin: Author-reported evaluation
configuration: Primary source as retrieved 2026-10-11
protocol id: lit-cells-networks-20261011-sun2026de-protocol-within-cell-line
dataset version: Not reported
split: 60/40 split by perturbation
population: Not reported
inputs: Training perturbations with observed expression or DE labels, control-cell expression, and a perturbation representation (GenePT, LLM-derived or learned embedding) where the method uses one
adaptation: Trained per split
metric implementation: Wilcoxon rank-sum DE calls, BH-adjusted p < 0.01, on predicted and observed expression
aggregation: Average across four cell lines
budget: Not reported

Metadata review: source checked. Unreported conditions prevent automatic comparisons.

Reproduction

Split: 60/40 split by perturbation
Adaptation: Trained per split
Scoring implementation: Wilcoxon rank-sum DE calls, BH-adjusted p < 0.01, on predicted and observed expression

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18 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-23-5347695500ed
Property and statement	Original source and location	Review and provenance
Comparison: adaptation Trained per split Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Table 1, row 'Logistic regression' Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded author reported Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.adaptation` 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
Comparison: aggregation Average across four cell lines Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Table 1, row 'Logistic regression' Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded author reported Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.aggregation` 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
Comparison: budget Not reported Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Table 1, row 'Logistic regression' Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	missing or unspecified No individual claim review recorded author reported Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.budget` 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
Comparison: dataset version Not reported Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Table 1, row 'Logistic regression' Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	missing or unspecified No individual claim review recorded author reported Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.dataset_version` 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
Comparison: inputs Training perturbations with observed expression or DE labels, control-cell expression, and a perturbation representation (GenePT, LLM-derived or learned embedding) where the method uses one Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Table 1, row 'Logistic regression' Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded author reported Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.inputs` 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
Comparison: metric implementation Wilcoxon rank-sum DE calls, BH-adjusted p < 0.01, on predicted and observed expression Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Table 1, row 'Logistic regression' Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded author reported Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.metric_implementation` 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
Comparison: population Not reported Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Table 1, row 'Logistic regression' Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	missing or unspecified No individual claim review recorded author reported Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.population` 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
Comparison: protocol id lit-cells-networks-20261011-sun2026de-protocol-within-cell-line Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Table 1, row 'Logistic regression' Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded author reported Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.protocol_id` 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
Comparison: split 60/40 split by perturbation Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Table 1, row 'Logistic regression' Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded author reported Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.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
Origin author_reported Context-only references	Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models Original source ↗ Table 1, row 'Logistic regression' Version: bioRxiv preprint version 1, posted 2026-07-24 Retrieved: 2026-10-11T21:35:10Z	not individually reviewed No individual claim review recorded author reported Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.origin` 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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Release 2026-10-23-5347695500ed · Record review: source checked

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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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Technical metadata and extraction receipts

Stable ID: lit-cells-networks-20261011-sun2026de-eval-within-average-logistic-regression

areas: single-cell
contexts: research
origin: author_reported
protocol: lit-cells-networks-20261011-sun2026de-protocol-within-cell-line
version: Primary source as retrieved 2026-10-11
comparison: protocol id: lit-cells-networks-20261011-sun2026de-protocol-within-cell-line; dataset version: Not reported; split: 60/40 split by perturbation; population: Not reported; inputs: Training perturbations with observed expression or DE labels, control-cell expression, and a perturbation representation (GenePT, LLM-derived or learned embedding) where the method uses one; adaptation: Trained per split; metric implementation: Wilcoxon rank-sum DE calls, BH-adjusted p < 0.01, on predicted and observed expression; aggregation: Average across four cell lines; budget: Not reported
source locator: Table 1, row 'Logistic regression'
missing metadata: comparison.budget: reason: unreported; comparison.dataset version: reason: unreported; comparison.population: reason: unreported

Related records

system: Per-gene L2 logistic regression on GenePT embeddings (Sun et al. 2026)
assessment: Sun et al. 2026 unseen-perturbation DE classification (within cell line)
data: K562, RPE1, HepG2 and Jurkat test sets (Sun et al. 2026)
evaluation: Logistic regression within-cell-line Average (Table 1)
evaluation: Logistic regression within-cell-line Q1 (0–0.016) (Table 2)
evaluation: Logistic regression within-cell-line Q2 (0.016–0.034) (Table 2)
evaluation: Logistic regression within-cell-line Q3 (0.034–0.060) (Table 2)
evaluation: Logistic regression within-cell-line Q4 (0.060–0.108) (Table 2)
evaluation: Logistic regression within-cell-line Q5 (>0.108) (Table 2)

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