Sync stemtocvitro
Browse files- README.md +38 -0
- config.json +38 -0
- stemtocvitro.pt +3 -0
README.md
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---
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license: bsd-3-clause
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library_name: pyaging
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tags:
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- pyaging
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- aging-clock
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- biology
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- dna-methylation
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---
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# stemtocvitro
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In-vitro precursor of stemTOC based on the 95th percentile across 629 population-doubling-associated CpGs.
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|---|---|
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| **Predicts** | mitotic age |
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| **Species** | Homo sapiens |
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| **Tissue** | multi-tissue, cultured human cells |
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| **Data type** | DNA methylation |
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| **Model type** | 95th-percentile methylation aggregation |
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| **Year** | 2024 |
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## Use with pyaging
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```python
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import pyaging as pya
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pya.pred.predict_age(adata, ["stemtocvitro"])
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```
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Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io).
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## Citation
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Zhu, Tianlei, et al. "An improved epigenetic counter to track mitotic age in cells." Nature Communications 15 (2024): 4211.
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https://doi.org/10.1038/s41467-024-48649-8
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config.json
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{
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"approved_by_author": "\u231b",
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"citation": "Zhu, Tianlei, et al. \"An improved epigenetic counter to track mitotic age in cells.\" Nature Communications 15 (2024): 4211.",
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"citations": 24,
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"citations_date": "2026-07-05",
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"clock_name": "stemtocvitro",
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"data_type": "DNA methylation",
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"doi": "https://doi.org/10.1038/s41467-024-48649-8",
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"journal": "Nature Communications",
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"last_author": "Andrew E. Teschendorff",
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"model_type": "95th-percentile methylation aggregation",
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"n_features": 629,
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"notes": "In-vitro precursor of stemTOC based on the 95th percentile across 629 population-doubling-associated CpGs.",
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"platform": [
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"Illumina 450K",
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"Illumina EPIC"
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],
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"population": "prenatal and newborn",
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"predicts": [
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"mitotic age"
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],
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"preprocess": "0.95 quantile",
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"reference_values": true,
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"research_only": null,
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"species": "Homo sapiens",
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"tissue": [
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"multi-tissue",
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"cultured human cells"
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],
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"training_target": [
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"population doublings"
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],
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"unit": [
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"beta value"
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],
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"version": "v0.3.0",
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"year": 2024
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}
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stemtocvitro.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:95540d8140d86e3550174d6716a3fe2b559bd8b335ab64a9b7221fff7bf197b9
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size 20197
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