Evaluate annotation labels using Seurat markers

Identifies differentially expressed markers between clusters and evaluates their performance.

Usage

test_markers(seurat_obj, annotation, min_FC = 5, ideal_FC = 20, ncores = 4)

Arguments

seurat_obj

A Seurat object containing single-cell gene expression data

annotation

A character string indicating the name of the column in seurat_obj@meta.data containing the annotation of interest.

min_FC

Minimum fold change required for a marker to pass the diagnosis. The default value is 5.

ideal_FC

Ideal fold change required for a marker to be considered outstanding. The default value is 20.

ncores

Number of cores to use in parallelization for the FindAllMarkers function. The default value is 4.

Value

A list of three items: * top_mks: data.frame with top 10 markers for each cluster, with their average fold change and p-value. * full_mks: data.frame with all markers tested, with their average fold change, p-value, and other metrics. * mk_status: named vector with the status of each cluster based on the expression of their top markers.