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README.md

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+---
+license: agpl-3.0
+library_name: methscope
+tags:
+- dna-methylation
+- methylation
+- wgbs
+- imputation
+- single-cell
+---
+
+# MethScope — whole-genome methylation upscaler (`hg38_wg.updecx`)
+
+Pretrained **whole-genome CpG upscaler** for
+[methscope-cli](https://github.com/zhou-lab/methscope-cli). A single
+self-contained bundle (`.updecx`) — the `UPDEC2` decoder plus its 1000-pattern
+MRMP feature definition — that reconstructs dense CpG methylation from a sparse
+query methylome.
+
+## Usage
+
+```sh
+# fetch the model
+hf download zhou-lab/methscope hg38_wg.updecx --local-dir .
+
+# impute genome-wide CpG methylation from a sparse .cg
+methscope upscale -o imputed.cg hg38_wg.updecx sparse.cg
+```
+
+Inference is pure C — no CUDA or BLAS at run time (~2 s / sample, ~3 GB RAM).
+
+## Model
+
+| | |
+|---|---|
+| genome | hg38 (29,401,795 CpGs) |
+| input | 1000 MRMP features (beta + missing indicator) |
+| architecture | `UPDEC2` — one processing unit per MRMP membership, N16 leaky low-rank factor, uniform rank |
+| training | Loyfer sorted-cell WGBS (207 cells), fixed-29k simulated sparse queries |
+| external validation | 2018_Zhou cohort, genome-uniform MAE **0.1076** (beats the per-block baseline; see the lab journal) |
+| size | 2.8 GB |
+
+Trained with `methscope upscale-train` (CUDA). See the
+[methscope-cli documentation](https://zhou-lab.github.io/methscope-cli/) and the
+[model catalog](https://github.com/zhou-lab/methscope_data) for the classifier
+and deconvolution bundles.
+
+## Citation
+
+Hongxiang Fu, Chin Nien Lee, Cameron Cloud, Hao Xu, Yanxiang Deng, Wanding Zhou.
+*MethScope: Ultra-fast Analysis of Sparse DNA Methylome via Recurrent Pattern
+Encoding.*