dot-compute_weighted_scores: Compute weighted scores for per-cell annotation

In SlimR: Adaptive Machine Learning-Powered, Context-Matching Tool for Single-Cell and Spatial Transcriptomics Annotation

Compute weighted scores for per-cell annotation

Description

This function uses an improved weighting scheme that considers:

1. Expression level (log-normalized)

2. Detection rate (binary: above min_expression threshold)

3. Marker specificity (how unique is this marker to this cell type)

4. Expression variability (CV-based: more variable genes are more discriminative)

Usage

.compute_weighted_scores(expr_matrix, marker_sets, min_expression)

SlimR documentation built on Feb. 5, 2026, 5:08 p.m.