Package: MethScope 1.0.4

MethScope: Ultra-Fast Analysis of Sparse DNA Methylome via Recurrent Pattern Encoding

Methods for analyzing DNA methylation data via Most Recurrent Methylation Patterns (MRMPs). Supports cell-type annotation, spatial deconvolution, unsupervised clustering, and cancer cell-of-origin inference. Includes C-backed summaries for YAME ".cg/.cm" files (overlap counts, log2 odds ratios, beta/depth aggregation), an XGBoost classifier, NNLS deconvolution, and plotting utilities. Scales to large spatial and single-cell methylomes and is robust to extreme sparsity.

Authors:Hongxiang Fu [aut, cre], Wanding Zhou [cph, fnd], The SAMtools/HTSlib authors [ctb, cph], Attractive Chaos [ctb, cph]

MethScope_1.0.4.tar.gz
MethScope_1.0.4.zip(r-4.7-x86_64)MethScope_1.0.4.zip(r-4.7-arm64)MethScope_1.0.4.zip(r-4.6-x86_64)MethScope_1.0.4.zip(r-4.6-arm64)MethScope_1.0.4.zip(r-4.5-x86_64)
MethScope_1.0.4.tgz(r-4.6-x86_64)MethScope_1.0.4.tgz(r-4.6-arm64)MethScope_1.0.4.tgz(r-4.5-x86_64)MethScope_1.0.4.tgz(r-4.5-arm64)
MethScope_1.0.4.tar.gz(r-4.7-arm64)MethScope_1.0.4.tar.gz(r-4.7-x86_64)MethScope_1.0.4.tar.gz(r-4.6-arm64)MethScope_1.0.4.tar.gz(r-4.6-x86_64)
MethScope_1.0.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MethScope/json (API)

# Install 'MethScope' in R:
install.packages('MethScope', repos = c('https://hongxiang2023.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • zlib– Compression library

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

zlib

4.18 score 7 scripts 424 downloads 16 exports 87 dependencies

Last updated from:e927782e5d. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK196
linux-devel-x86_64OK221
source / vignettesOK358
linux-release-arm64OK222
linux-release-x86_64OK185
macos-release-arm64OK212
macos-release-x86_64OK281
macos-oldrel-arm64OK195
macos-oldrel-x86_64OK494
windows-devel-arm64OK430
windows-devel-x86_64OK199
windows-release-arm64OK417
windows-release-x86_64OK142
windows-oldrel-x86_64OK162
wasm-releaseOK159

Exports:confidence_scoreconfidence_score_top95filter_cellGenerateInputGenerateReferenceimputeRowMeanInput_trainingLiu2021_MouseBrain_P1000nnls_deconvPlotConfusionPlotF1PlotUMAPPlotUMAP_fixedwindowPredictCellTypesmooth_matrixZhou2025_HumanAtlas_P1000

Dependencies:BHcaretclasscliclockcodetoolscpp11data.tablediagramdigestdoParalleldplyrdqrnge1071farverFNNforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredirlbaisobanditeratorsjsonliteKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixModelMetricsnlmennetnnlsnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppAnnoyRcppEigenRcppProgressrecipesreshape2rlangrpartRSpectraS7scalesshapesitmosparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8uwotvctrsviridisLitewithrxgboost

methscope-cli
Overview | When to Use methscope-cli | Build methscope-cli | Example Commands | Cell-Type Prediction | Generate a Feature Matrix | Deconvolution | Upscaling | Runnable example | Relationship to the R Package | Notes on Input Order
Rendered frommethscope-cli.Rmdusingknitr::rmarkdown

Last update: 2026-08-26
Started: 2026-08-26

MethScope-Input
Overview | Install command-line dependencies | Choose the correct CpG reference | Convert M/U count BED files | Convert ALLC files | Convert beta or methylation fraction files | Convert binary BED tracks | Use the converted file in MethScope | Troubleshooting | Related resources
Rendered fromMethScope-Input.Rmdusingknitr::rmarkdown

Last update: 2026-08-26
Started: 2026-08-26

MethScope-MRMP
Generate an MRMP reference | Prerequisites | Step 1: Binarize the reference .cg file | Step 2: Build MRMP pattern definitions | Step 3: Pack patterns into a .cm reference | Build MRMP references from cell-type pseudobulks | Step 1: Create pseudobulk profiles per cell type | Step 2: Repeat per cell type and build MRMPs
Rendered fromMethScope-MRMP.Rmdusingknitr::rmarkdown

Last update: 2026-08-26
Started: 2025-12-09

MethScope-Tutorial
Overview | 1) Generate cell/pixel/sample MRMP embeddings | 2) Perform cell-type annotation | 3) Train a custom classification model | 4) Visualize prediction results | 5) Cell-type deconvolution | 6) Unsupervised clustering (Seurat example) | 7) Upscaling Network (10k-CpG block example) | 7.1 Prepare model input (x) and target (y) | 7.2 Train decoder (Python) | 7.3 Evaluate | Related resources
Rendered fromMethScope-Tutorial.Rmdusingknitr::rmarkdown

Last update: 2026-08-26
Started: 2025-12-09

Pretrained models & bundles
Overview | Using a pretrained bundle | Bundling and unbundling | Bundle format (technical) | Models in R
Rendered frompretrained-models.Rmdusingknitr::rmarkdown

Last update: 2026-08-26
Started: 2026-08-26

Using the MethScope agent skill
Overview | Install for Codex | Example Codex prompts | Install for Claude-style project context | What the skill knows | Update the skill
Rendered fromagent-skill.Rmdusingknitr::rmarkdown

Last update: 2026-08-26
Started: 2026-08-26