Campaign run · Parkinson's disease
parkinson-foundation-embedding-r3
Do zero-shot Geneformer (95M-cell variant) and scGPT embeddings of Fullard 2024 SNpc dopaminergic-neuron nuclei separate PD from control donors better than a baseline log-CPM-PCA-LR pipeline at the donor level — and which foundation model wins?
Status
- How far it got
- Stopped before planning
Stopped before writing any hypotheses: a dry-run test, a launch superseded by an immediate relaunch, a usage limit or a process failure.
- Why it stopped
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- Recorded status
Stopped without a final status. The run stopped without recording a final status.
- Census class
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Research question
The sentence under the run id is quoted from this brief.
Full research brief given to the run
Do zero-shot Geneformer (95M-cell variant) and scGPT embeddings of Fullard 2024 SNpc dopaminergic-neuron nuclei separate PD from control donors better than a baseline log-CPM-PCA-LR pipeline at the donor level — and which foundation model wins?
Cui et al. 2024 (scGPT, Nat Methods 21:1470) and Theodoris et al. 2023+2024 (Geneformer, Nature 618:616 + 95M update) made strong claims about disease-stratification utility of foundation-model embeddings on cell atlases. Boiarsky 2024 and Kedzierska 2024 have begun to publish benchmarks showing zero-shot foundation models are often worse than baseline log-CPM-PCA-LR in disease contexts. PD has not been placed on that benchmark map. This OmiCairn run does so.
Hypotheses (pre-registered):
H1 (primary — donor-level classification, foundation model vs. baseline): Donor-level leave-one-out (LOO) AUC for PD-vs-control classification using donor-mean foundation-model embedding + logistic regression is > 0.75 for at least one of Geneformer or scGPT, AND that AUC is > the log-CPM-PCA-LR baseline AUC by ΔAUC ≥ 0.05, with DeLong test p < 0.05.
H2 (secondary — AGTR1+ subcluster recovery without supervision): Embedding-derived nearest-neighbour graph (Leiden γ = 0.6) in PD donors recovers an AGTR1+ vs. AGTR1- subcluster partition with Adjusted Rand Index ≥ 0.4 against the v1 parkinson-da-vulnerability workspace labels.
H3 (secondary — transfer-learning benefit): Geneformer fine-tuned for 1 epoch on Kamath A1 PD-vs-ctrl labels (GSE178265) generalises to Wang (GSE184950) and Adams (GSE193688) atlases with held-out AUC ≥ 0.7 in each, vs. ≤ 0.6 for the same architecture trained from scratch on the same fine-tune set.
H4 (secondary — embedding-space robustness): Repeat H1 with three subsampling levels (5K / 25K / 50K nuclei per donor average); the ΔAUC vs. baseline must be stable (within ±0.05) across the three subsamples, otherwise the foundation-model advantage is sample-size dependent and that must be reported.
H5 (secondary — cross-atlas zero-shot generalisation): Foundation-model embeddings trained on Fullard alone achieve PD-vs-ctrl AUC ≥ 0.65 when scored zero-shot on Wang and Adams (no fine-tuning). Below this floor, the embedding is Fullard-specific rather than generalising.
Null payoff. If neither Geneformer nor scGPT beats log-CPM-PCA-LR (ΔAUC < 0.05), the field's optimism about single-cell foundation models for neurodegenerative disease is overstated for PD — matching the Boiarsky 2024 + Kedzierska 2024 findings in other diseases, and placing PD on the benchmark map.
The research question comes from the topic's current research brief. All attempts at a topic share it; earlier attempts may have started from an earlier wording.