Configuration

CellPLM (gears-decoder, Chen et al. 2026)

Configuration of CellPLM: model embeddings (or gene embeddings for GenePT-w and scELMo) used with a shared GEARS perturbation decoder in the scGPT-style perturbation workflow.

3 evaluations · 18 results

Overview

An explanatory profile has not yet been reviewed for this record.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

Evaluations and results

3 evaluations · 18 results. Different protocols are not a single leaderboard.

Sorted by MSE (all genes, predicted versus observed post-perturbation expression) (lower 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	Protocol and dataset	MSE (all genes, predicted versus observed post-perturbation expression)	MSE (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression)	Pearson (all genes, predicted versus observed post-perturbation expression)	Pearson (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression)	Pearson delta (all genes in the intersected gene set)	Pearson delta (top 20 differentially expressed genes per perturbation condition)
CellPLM (gears-decoder, Chen et al. 2026)Evaluation	Chen et al. 2026 genetic perturbation prediction, Norman, Norman (Chen et al. 2026)	0.0064 (best in column)	0.403	0.983	0.872	0.473	0.398
CellPLM (gears-decoder, Chen et al. 2026)Evaluation	Chen et al. 2026 genetic perturbation prediction, Replogle, Replogle (Chen et al. 2026)	0.0068	0.184 (best in column)	0.987	0.941 (best in column)	0.38	0.46
CellPLM (gears-decoder, Chen et al. 2026)Evaluation	Chen et al. 2026 genetic perturbation prediction, Adamson, Adamson (Chen et al. 2026)	0.0091	0.278	0.988 (best in column)	0.924	0.649 (best in column)	0.711 (best in column)

All 18 result rows with coverage, uncertainty and sources

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.0091 MSE (all genes, predicted versus observed post-perturbation expression) unitless · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'MSE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.278 MSE (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) unitless · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'MSE DE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.988 Pearson (all genes, predicted versus observed post-perturbation expression) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'Pearson'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.924 Pearson (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'Pearson DE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.649 Pearson delta (all genes in the intersected gene set) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'Pearson Delta'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Adamson Dataset subset: Adamson (Chen et al. 2026)	0.711 Pearson delta (top 20 differentially expressed genes per perturbation condition) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Adamson lit-cells-networks-20261011-chen2026-protocol-perturbation-adamson Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Adamson', column 'Pearson Delta DE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (Chen et al. 2026)	0.0064 MSE (all genes, predicted versus observed post-perturbation expression) unitless · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Norman', column 'MSE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (Chen et al. 2026)	0.403 MSE (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) unitless · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Norman', column 'MSE DE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (Chen et al. 2026)	0.983 Pearson (all genes, predicted versus observed post-perturbation expression) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Norman', column 'Pearson'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (Chen et al. 2026)	0.872 Pearson (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Norman', column 'Pearson DE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (Chen et al. 2026)	0.473 Pearson delta (all genes in the intersected gene set) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Norman', column 'Pearson Delta'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Norman Dataset subset: Norman (Chen et al. 2026)	0.398 Pearson delta (top 20 differentially expressed genes per perturbation condition) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Norman lit-cells-networks-20261011-chen2026-protocol-perturbation-norman Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Norman', column 'Pearson Delta DE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.0068 MSE (all genes, predicted versus observed post-perturbation expression) unitless · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Replogle', column 'MSE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.184 MSE (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) unitless · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Replogle', column 'MSE DE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.987 Pearson (all genes, predicted versus observed post-perturbation expression) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Replogle', column 'Pearson'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.941 Pearson (top 20 differentially expressed genes per perturbation condition, predicted versus observed post-perturbation expression) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Replogle', column 'Pearson DE'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.38 Pearson delta (all genes in the intersected gene set) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Replogle', column 'Pearson Delta'
Configuration: CellPLM (gears-decoder, Chen et al. 2026)	Protocol: Chen et al. 2026 genetic perturbation prediction, Replogle Dataset subset: Replogle (Chen et al. 2026)	0.46 Pearson delta (top 20 differentially expressed genes per perturbation condition) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Table 4 precision and recall equal accuracy in all eight Nicheformer and CellPLM rows, as micro (global) averaging would give, but recall differs from accuracy in every scGPT-spatial, GenePT-w, scELMo and Novae row. The Methods call precision and recall 'global' without defining the averaging, so they may be averaged differently between models and should not be compared across models.Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 4, columns 'Accuracy', 'Precision' and 'Recall'; Methods 'Cell type annotation' (page 25) Methods, coverage and source CellPLM on Chen et al. 2026 genetic perturbation prediction, Replogle lit-cells-networks-20261011-chen2026-protocol-perturbation-replogle Aggregation: Not reported Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Table 5, model 'CellPLM', dataset 'Replogle', column 'Pearson Delta DE'

Source checking is not independent reproduction. Release 2026-10-23-5347695500ed.

Use this configuration

How it works, versions and access

Additional methodological details, versions and access terms have not yet been extracted into a structured profile for this configuration. Consult the linked sources and evaluation records below.

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

0 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

No evidence rows match these filters. Choose another scope or clear the search.

How to read the evidence

Sources and history

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

1 source record and release history

Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation · Original source · arXiv 2607.17227v2, updated 2026-09-17 (v1 posted 2026-07-19)

Download this release (gzip)

Technical metadata and extraction receipts

Stable ID: lit-cells-networks-20261011-chen2026-config-cellplm-gears-decoder

areas: single-cell; spatial-omics
contexts: research
method types: foundation_model
reported name: CellPLM
foundation model eligible: true
training setting: model embeddings (or gene embeddings for GenePT-w and scELMo) used with a shared GEARS perturbation decoder in the scGPT-style perturbation workflow
access reuse: Public pretrained checkpoints as released by each model's authors
source locator: Methods: model-specific sections under 'Zero-shot cell type clustering', 'Continually-pretrained cell type clustering', 'Cell type annotation' and 'Perturbation prediction'
missing metadata: version: reason: unreported; note: Checkpoint names are printed for some models only (CellPLM 20231027_85M)
provenance note: CellPLM as run by Chen et al. 2026: model embeddings (or gene embeddings for GenePT-w and scELMo) used with a shared GEARS perturbation decoder in the scGPT-style perturbation workflow.

Related records

configuration of: CellPLM
system: CellPLM on Chen et al. 2026 genetic perturbation prediction, Adamson
system: CellPLM on Chen et al. 2026 genetic perturbation prediction, Norman
system: CellPLM on Chen et al. 2026 genetic perturbation prediction, Replogle

Suggest a correction