| Title: | Ultra-Fast Analysis of Sparse DNA Methylome via Recurrent Pattern Encoding |
| Version: | 1.0.0 |
| Description: | 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. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | xgboost, dplyr, utils, tidyr, stringr, caret, doParallel, parallel, ggplot2, uwot, magrittr, FNN, data.table, nnls |
| Suggests: | knitr, rmarkdown, spelling |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | yes |
| Packaged: | 2025-12-03 17:38:44 UTC; fuh1 |
| Maintainer: | Hongxiang Fu <fhx@seas.upenn.edu> |
| Language: | en-US |
| Author: | Hongxiang Fu 
[aut, cre],
Wanding Zhou [cph, fnd],
The SAMtools/HTSlib authors [ctb, cph] (BGZF components; see
inst/COPYRIGHTS),
Attractive Chaos [ctb, cph] (Author and copyright holder of khash.h
(klib, MIT license)) |
| Repository: | CRAN |
| Date/Publication: | 2025-12-09 16:00:07 UTC |
Generate pattern level data for cell type annotation
Description
Generate pattern level data for cell type annotation
Usage
GenerateInput(query_fn, knowledge_fn)
Arguments
query_fn |
File path to query .cg |
knowledge_fn |
File path to pattern file .cm |
Value
A cell by pattern matrix.
Examples
qry <- system.file("extdata", "toy.cg", package = "MethScope")
msk <- system.file("extdata", "toy.cm", package = "MethScope")
res <- GenerateInput(qry, msk)
Generate reference pattern labels (no default writing)
Description
Generate reference pattern labels (no default writing)
Usage
GenerateReference(binary_file, min_CG = 50, output_path = NULL)
Arguments
binary_file |
Path to the pattern strings file (one string per line). |
min_CG |
Minimum CpG count a pattern must have to keep its own ID (default: 50).
Patterns with frequency <= |
output_path |
Optional file path to write the resulting labels. If |
Value
A character vector of pattern labels (same length/order as the input file).
Examples
## Not run:
# DO write only to a temp location in examples/vignettes/tests:
tmp_out <- file.path(tempdir(), "patterns.txt")
labs <- GenerateReference("path/to/pattern_strings.txt", min_CG = 50, output_path = tmp_out)
# Or skip writing and just get the vector:
labs <- GenerateReference("path/to/pattern_strings.txt", min_CG = 50)
## End(Not run)
Train XGBoost model to predict cell type
Description
Train XGBoost model to predict cell type
Usage
Input_training(
summary_results,
cell_type_label,
number_patterns = 1000,
cross_validation = FALSE,
xgb_parameters = list()
)
Arguments
summary_results |
a wide cell by pattern matrix generated from GenerateInput function |
cell_type_label |
a vector of the corresponding cell type label for each row of the summary results |
number_patterns |
a numeric value to indicate number of patterns to be used (Default: 1000) |
cross_validation |
a boolean varaible whether to perform cross_validation to obtain the best hyper parameters for the model |
xgb_parameters |
an optional list for xgb model parameters provided by the user |
Value
the xgb model trained
Generate confusion table for the final prediction
Description
Generate confusion table for the final prediction
Usage
PlotConfusion(prediction_result, actual_label, log2 = FALSE)
Arguments
prediction_result |
Prediction result from PredictCellType |
actual_label |
Ground truth cell label |
log2 |
Log scale count (Default: False) |
Value
A ggplot2 confusion table object.
Generate F1 score barplot for each class
Description
Generate F1 score barplot for each class
Usage
PlotF1(prediction_result, actual_label)
Arguments
prediction_result |
Prediction result from PredictCellType |
actual_label |
Ground truth cell label |
Value
A ggplot2 object.
Generate UMAP for the final prediction based on cell patterns
Description
Generate UMAP for the final prediction based on cell patterns
Usage
PlotUMAP(predictMatrix, prediction_result, n_component = 30, seed = 123, ...)
Arguments
predictMatrix |
a wide cell by pattern matrix generated from GenerateInput function |
prediction_result |
Prediction result from PredictCellType |
n_component |
Number of PCA components to use (Default: 30) |
seed |
A number for random seed (Default: 123) |
... |
Additional arguments passed to |
Value
A list of two ggplot2 UMAP object.
Generate UMAP for the final prediction based on fixed window eg.100kb bin widows
Description
Generate UMAP for the final prediction based on fixed window eg.100kb bin widows
Usage
PlotUMAP_fixedwindow(
query_fn,
knowledge_fn,
prediction_result,
n_component = 30,
seed = 123,
...
)
Arguments
query_fn |
File path to query .cg |
knowledge_fn |
File path to 100bk bins window or reference pattern |
prediction_result |
Prediction result from PredictCellType |
n_component |
Number of PCA components to use (Default: 30) |
seed |
A number for random seed (Default: 123) |
... |
Additional arguments passed to |
Value
A list of two ggplot2 UMAP object.
Predict cell type annotation from the trained model
Description
Predict cell type annotation from the trained model
Usage
PredictCellType(bst_model, predictMatrix, smooth = FALSE, KNeighbor = 5)
Arguments
bst_model |
The boosting model trained from ModelTrain |
predictMatrix |
A wide cell by pattern matrix generated from GenerateInput function |
smooth |
A Boolean variable to indicate whether smooth the matrix (Default: FALSE) |
KNeighbor |
number of knn neighbors to use for smoothing (Default: 5) |
Value
A cell by cell type matrix with confidence score and labeled cell type.
Examples
qry <- system.file("extdata", "toy.cg", package = "MethScope")
msk <- system.file("extdata", "toy.cm", package = "MethScope")
res <- GenerateInput(qry, msk)
## Not run:
prediction <- PredictCellType(Liu2021_MouseBrain_P1000,res)
## End(Not run)
Produce confidence score for XGBoost prediction
Description
Produce confidence score for XGBoost prediction
Usage
confidence_score(vec)
Arguments
vec |
A vector of predicted probability for each cell type |
Value
A numeric confidence score from 0 to 1.
Produce confidence score based on top 95 percent for XGBoost prediction
Description
Produce confidence score based on top 95 percent for XGBoost prediction
Usage
confidence_score_top95(vec)
Arguments
vec |
A vector of predicted probability for each cell type |
Value
A numeric confidence score from 0 to 1.
Filter final prediction to reduce noise
Description
Filter final prediction to reduce noise
Usage
filter_cell(pred_result, knn_res, KNeighbor = 5)
Arguments
pred_result |
The prediction result from XGBoost |
knn_res |
knn graph from smooth_matrix |
KNeighbor |
Number of knn neighbors to use for smoothing (Default: 5) |
Value
The final prediction result after dropping few cell types
Impute missing value for 100K window matrix
Description
Impute missing value for 100K window matrix
Usage
imputeRowMean(mtx, na_percent = 30)
Arguments
mtx |
A cell by 100K window data frame with missing values |
na_percent |
A na percent threshold to be filterd (Default: 30) |
Value
A cell by 100K window data frame with imputed values
Estimate cell type relative proportion
Description
Estimate cell type relative proportion
Usage
nnls_deconv(ref, mixture_matrix, number_patterns = 1000, var_threshold = 0.01)
Arguments
ref |
An imputed wide cell by pattern matrix generated from GenerateInput function using reference Pseudobulk |
mixture_matrix |
An imputed wide cell by pattern matrix generated from GenerateInput function |
number_patterns |
a numeric value to indicate number of patterns to be used (Default: 1000) |
var_threshold |
a numeric value to indicate variance that should filter the patterns (Default: 0.1) |
Value
A cell type by cell matrix showing the relative cell type proportion estimate for each cells
Smooth cell by pattern matrix to reduce noise
Description
Smooth cell by pattern matrix to reduce noise
Usage
smooth_matrix(predictMatrix, KNeighbor = 5)
Arguments
predictMatrix |
A wide cell by pattern matrix generated from GenerateInput function |
KNeighbor |
Number of knn neighbors to use for smoothing (Default: 5) |
Value
A wide cell by pattern matrix after smoothing and knn graph