MethScope is an R package for ultra-fast analysis of sparse DNA methylome data using Most Recurrent Methylation Patterns (MRMPs).
It supports downstream analysis for:
- Cell type annotation
- Cell type deconvolution
- Unsupervised clustering
- Cancer cell-of-origin prediction
- Missing value imputation
Sparse single-cell and spatial methylome data are often too sparse to analyze directly. MethScope compresses methylation signals into MRMP-based embeddings so you can run robust and scalable downstream tasks with standard analysis workflows.
MethScope converts high-dimensional methylation atlas signals into compact MRMP features and applies these features across multiple analysis tasks.
Core workflow:
- Binarize methylation atlas profiles and consolidate recurrent patterns
- Select top recurrent methylation patterns (MRMPs)
- Encode each sample, cell, or pixel into an MRMP-based representation
- Run downstream modeling for annotation, deconvolution, imputation, and representation learning
Use cases supported in the current pipeline:
- Cell-type annotation in sparse single-cell methylome profiles
- Mini-bulk deconvolution for mixed-cell samples
- Missing-value imputation for sparse CpG measurements
- Representation learning for clustering and embedding analysis
- R >= 4.0 (tested on R 4.5.2 and R 4.6.0)
- Operating systems tested: macOS, Linux (Ubuntu)
- R package dependencies (installed automatically):
xgboost,dplyr,tidyr,stringr,caret,doParallel,parallel,ggplot2,uwot,magrittr,FNN,data.table,nnls
No non-standard hardware is required. MethScope runs on a standard laptop or desktop CPU. No GPU is needed.
Install from CRAN:
install.packages("MethScope")Or install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("zhou-lab/MethScope")Typical install time: approximately 1 minute on a standard laptop.
A small example dataset is bundled with the package in inst/extdata/. The following demo runs end-to-end cell type annotation using the included example .cg file and a pre-built mouse brain MRMP reference.
library(MethScope)
# Locate bundled example files
example_file <- system.file("extdata", "example.cg", package = "MethScope")
reference_pattern <- system.file("extdata", "Liu2021_MouseBrain.cm", package = "MethScope")
# Step 1: Generate cell-by-MRMP embedding matrix
input_pattern <- GenerateInput(example_file, reference_pattern)
# Step 2: Predict cell types using the built-in pre-trained mouse brain model
prediction_result <- PredictCellType(MethScope:::Liu2021_MouseBrain_P1000, input_pattern)
# Step 3: Visualize results
umap_plot <- PlotUMAP(input_pattern, prediction_result)Expected output: a cell-by-MRMP matrix (input_pattern) and a data frame of predicted cell type labels with confidence scores (prediction_result). The UMAP plot will display cells colored by predicted cell type. Expected runtime on the bundled example: a few seconds.
- Documentation website: zhou-lab.github.io/MethScope
- End-to-end tutorial: MethScope-Tutorial
- Building MRMP references: MethScope-MRMP
This repository includes a reusable MethScope agent skill under agent-skills/methscope/.
Install the skill under $CODEX_HOME/skills/methscope/ with this layout:
$CODEX_HOME/skills/methscope/
SKILL.md <- copy from agent-skills/methscope/codex/SKILL.md
core/
Copy:
agent-skills/methscope/codex/SKILL.mdto$CODEX_HOME/skills/methscope/SKILL.mdagent-skills/methscope/core/to$CODEX_HOME/skills/methscope/core/
Then invoke the skill when working on MethScope package usage, vignettes, .cg and .cm inputs, MRMP embeddings, prediction, training, deconvolution, or visualization.
If you already keep a repository-level CLAUDE.md, copy the contents of agent-skills/methscope/claude/CLAUDE.md into it or reference that file from your existing Claude project instructions.
If you do not already have a project CLAUDE.md, use agent-skills/methscope/claude/CLAUDE.md as the starting project context for this repository.
Keep agent-skills/methscope/core/ in the repository, because the Claude instructions point to those shared files.
agent-skills/methscope/core/INSTRUCTIONS.mdagent-skills/methscope/core/WORKFLOWS.mdagent-skills/methscope/core/REFERENCES.md
- Example and reference data: zhou-lab/methscope_data
.cggeneration and preprocessing: YAME- Pattern interpretation: knowYourCG
This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).
Copyright (c) 2025 Hongxiang Fu and Wanding Zhou (zhouw3@chop.edu)
For commercial use or if the AGPL-3.0 restrictions are not suitable for your use case, please contact us for a commercial license: zhouw3@chop.edu
If you use MethScope, please cite (coming soon):
Fu H*, Xu H*, Lee CN, Cloud C, Deng Y, Zhou W. MethScope: Ultra-Fast Analysis of Sparse DNA Methylome via Recurrent Pattern Encoding.