Mean baseline (context mean, no perturbation-specific effect)
Per the paper's own text (Section 2.2, 'Mean baseline'): predicted perturbation effect delta-hat = mean(Xbar_c) - Xc, where Xc is a cell's own gene expression and Xbar_c is 'the mean gene expression of all cells in the same context.' The source does not state in this paragraph whether that population is restricted to control cells, to training cells, or to some other defined 'context'; this ambiguity is preserved rather than resolved. Separately, and on a different date: an independently pinned copy of this repository's code (main commit ce48c8b998901c8f8b6275114caac3a4d8543c0b, file src/models/components/predictors.py, class MeanExpression.forward) computes torch.mean(x[:, :x.shape[1]//2], dim=0) over whatever batch of inputs x is passed at call time, then returns that batch mean minus x[:, :x.shape[1]//2] -- a batch-mean of the first half of the input's feature dimension, not an explicitly 'all cells in the same context' population as stated in the paper. This code observation and the paper's stated definition are kept as two separate, dated facts, not merged: the mapping between this code commit and the exact code revision that produced the published Table 1 run is unverified.
Overview
Per the paper's own text (Section 2.2, 'Mean baseline'): predicted perturbation effect delta-hat = mean(Xbar_c) - Xc, where Xc is a cell's own gene expression and Xbar_c is 'the mean gene expression of all cells in the same context.' The source does not state in this paragraph whether that population is restricted to control cells, to training cells, or to some other defined 'context'; this ambiguity is preserved rather than resolved. Separately, and on a different date: an independently pinned copy of this repository's code (main commit ce48c8b998901c8f8b6275114caac3a4d8543c0b, file src/models/components/predictors.py, class MeanExpression.forward) computes torch.mean(x[:, :x.shape[1]//2], dim=0) over whatever batch of inputs x is passed at call time, then returns that batch mean minus x[:, :x.shape[1]//2] -- a batch-mean of the first half of the input's feature dimension, not an explicitly 'all cells in the same context' population as stated in the paper. This code observation and the paper's stated definition are kept as two separate, dated facts, not merged: the mapping between this code commit and the exact code revision that produced the published Table 1 run is unverified.
Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Evaluations and results
1 evaluation · 1 result. Different protocols are not a single leaderboard.
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| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: Mean baseline (context mean, no perturbation-specific effect) | Protocol: PertEval-scFM Norman single-gene (2,000 HVGs) AUSPC across SPECTRA sparsification splits Dataset subset: Norman et al. 2019 single-gene perturbations, K562, top 2,000 HVGs (PertEval-scFM Table 1) | 4.612 ± 0.317 AUSPC 10^-2 (printed column header units) · lower Uncertainty: type: author_reported_propagated_standard_error; reported spread: 0.317; note: The source describes this quantity as a standard error (main-text Figure 2 caption: 'Average AUSPC (down-arrow) across sparsification probabilities for each model with standard error bars') and separately gives its own propagation formula (Appendix F.2, Eqs. F3-F5): AUSPC's uncertainty is derived from each split's own MSE uncertainty via the trapezoidal integral's partial derivatives (sigma^2 = sum_i (d/2)^2 * sigma_phi_i^2, where d=0.1 is the fixed sparsification step size). These two author statements describe the same quantity and are not in conflict: a propagated quantity can correctly be reported as a standard error. This is recorded as the author's own reported, propagated uncertainty; the F3-F5 derivation is the authors' own formula and its mathematical correctness has not been independently validated here. It must not be read as an independently resampled model-seed standard deviation or a confidence interval. The underlying per-split uncertainty is attributed by the source to triplicate experiments per model (Appendix I, Figure I1 caption: 'Experiments were carried out in triplicate for each model'), not to the main-text Figure 2 region. Figure I1's own caption separately states '8 train-test splits of increasing difficulty' for this same Norman single-gene evaluation, while Table 1 prints seven S-columns (S0.1-S0.7) and Appendix F.2 describes the sparsification probabilities as spanning 0.1 to 0.7. This 7-vs-8 discrepancy between the Figure I1 caption and the Table 1 / F.2 grid is preserved exactly as printed, not resolved; it must not be read as establishing an eighth Table 1 column or a confirmed n_runs=7, and no significance claim is made from any overlapping error bars. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceMean baseline on PertEval-scFM Norman single-gene (2,000 HVGs) AUSPC Trapezoidal-rule AUSPC of MSE, scored on the perturbation-effect delta=P-Xc (Eq. 5), across seven SPECTRA sparsification splits (s=0.1..0.7), Norman single-gene, 2,000 HVGs. Aggregation: Not reported PertEval-scFM (Wenteler et al., ICML 2025), full text · Table 1, Norman single-gene section, row Mean baseline, column AUSPC (10^-2). |
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Release 2026-10-07-1448159e6a81 · Record review: source checked
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- PertEval-scFM (Wenteler et al., ICML 2025), full text · Original source · PMLR v267 wenteler25a (as served by the PMLR-affiliated mlresearch/v267 GitHub mirror; ETag "ed9f0fe44cf6edc939ee6950d024f65dcd8dd6f16414bd9f5297cda9395d6e58" at retrieval)
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Stable ID: perteval-scfm-2025-method-mean-baseline
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- cells-spatial-multiomics
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- Table 1, row Mean baseline, Norman single-gene section; Section 2.2 ('Mean baseline').