confidence_score_top95

Produce confidence score based on top 95 percent for XGBoost prediction

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.

Hongxiang Fu

MethScope

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

Wanding Zhou

The SAMtools/HTSlib authors 

Attractive Chaos

confidence_score_top95 function

<dl><dt>vec</dt>
<dd>A vector of predicted probability for each cell type</dd></dl>

Arguments

Produce confidence score based on top 95 percent for XGBoost prediction — confidence_score_top95

<dl>

<dt>vec</dt>
<dd>A vector of predicted probability for each cell type</dd>

</dl>

confidence_score_top95: Produce confidence score based on top 95 percent for XGBoost prediction

Description

Usage

Value

Arguments