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license: agpl-3.0
library_name: methscope
tags:
- dna-methylation
- methylation
- wgbs
- single-cell
- imputation
- deconvolution
- cell-type-annotation
---
# MethScope — pretrained models
Pretrained models for [**methscope-cli**](https://github.com/zhou-lab/methscope-cli):
ultra-fast analysis of sparse DNA methylomes via MRMP (Most Recurrent Methylation
Pattern) encoding. Each model is a **single self-contained bundle** that already
carries its MRMP feature definition (and labels / signature), so a query `.cg`
runs directly — no separate annotation files.
## Fetch & run
```sh
# fetch one model (or --local-dir models to grab all)
hf download zhou-lab/methscope hg38_celltype.ubjx --local-dir .
methscope predict query.cg hg38_celltype.ubjx # cell type / label
methscope deconv mixture.cg hg38_65celltypes.refx # cell-type proportions (NNLS)
methscope upscale -o out.cg hg38_wg.updecx query.cg # genome-wide CpG upscaling
methscope inspect hg38_sex.ubjx # framework, labels, features
```
Small query `.cg` test fixtures (typed cells, a deconvolution mixture, an
upscale input + truth) live in the GitHub repo
[`zhou-lab/methscope_data`](https://github.com/zhou-lab/methscope_data) under
`test/`, alongside this catalog and the MRMP-construction reproducibility archive.
## Models
| file | task | framework | labels / notes |
|------|------|-----------|----------------|
| `hg38_wg.updecx` | whole-genome CpG upscaling | `UPDEC2` | all 29,401,795 hg38 CpGs; the primary upscaler (2.8 GB) |
| `hg38_10k1.updecx` | CpG upscaling (single block) | MLP decoder | block 10k1 (10,000 CpGs); small demo model |
| `hg38_celltype.ubjx` | cell-type annotation | xgboost | 62 human cell types |
| `mm10_celltype.ubjx` | cell-type annotation | xgboost | 41 mouse-brain cell types |
| `hg38_sex.ubjx` | sex prediction | logistic | Female, Male (XCI `Xa_hi`/`Xa_lo` markers) |
| `hg38_65celltypes.refx` | cell-type deconvolution (NNLS) | refx | 65 types = 58 Zhou + 7 Loyfer organ/blood; split MRMP, 15,300 patterns |
`methscope inspect <model>` prints the exact framework, full label list, and (for
linear models) per-feature weights. Classifiers carry a required framework `kind`
mark (`xgboost` / `threshold` / `logistic`); `predict` rejects an unmarked
bundle. Upscale decoders (`.updecx`) run via `upscale`, deconvolution references
(`.refx`, `kind=refx`) via `deconv`.
See the [methscope-cli documentation](https://zhou-lab.github.io/methscope-cli/)
for the full command reference and runnable examples.
|