Compute clone-level mean expression from single-cell data

For each patient, groups cells by clone_id and computes the mean expression of each gene within each clone. This reduces single-cell resolution to subclone resolution, matching the original PERCEPTION pipeline.

Usage

clone_mean_expression(expression_matrix, cell_clone_map, patient_ids = NULL)

Arguments

expression_matrix

Matrix or data frame. Gene expression matrix with genes as rows and cells as columns.

cell_clone_map

Data frame with columns cell_id and clone_id, mapping each cell to its clone. Typically from Seurat clustering.

patient_ids

Character vector. Patient ID for each cell, same length as columns of expression_matrix. If NULL, all cells are assumed from one patient.

Value

A named list of matrices, one per patient. Each matrix has genes as rows and clone IDs as columns, with mean expression values.