| marker_overlap {ClustAssess} | R Documentation |
Cell-Wise Marker Gene Overlap
Description
Calculates the per-cell overlap of previously calculated marker genes.
Usage
marker_overlap(
markers1,
markers2,
clustering1,
clustering2,
n = 25,
overlap_type = "jsi",
rank_by = "-p_val",
use_sign = TRUE
)
Arguments
markers1 |
The first data frame of marker genes, must contain columns called 'gene' and 'cluster'. |
markers2 |
The second data frame of marker genes, must contain columns called 'gene' and 'cluster'. |
clustering1 |
The first vector of cluster assignments. |
clustering2 |
The second vector of cluster assignments. |
n |
The number of top n markers (ranked by rank_by) to use when calculating the overlap. |
overlap_type |
The type of overlap to calculated: must be one of 'jsi' for Jaccard similarity index and 'intersect' for intersect size. |
rank_by |
A character string giving the name of the column to rank marker genes by. Note the sign here: to rank by lowest p-value, preface the column name with a minus sign; to rank by highest value, where higher value indicates more discriminative genes (for example power in the ROC test), no sign is needed. |
use_sign |
A logical: should the sign of markers match for overlap calculations? So a gene must be a positive or a negative marker in both clusters being compared. If TRUE, markers1 and markers2 must have a 'avg_logFC' column, from which the sign of the DE will be extracted. |
Value
A vector of the marker gene overlap per cell.
Examples
suppressWarnings({
set.seed(1234)
library(Seurat)
# cluster with Louvain algorithm
pbmc_small = FindClusters(pbmc_small, resolution=0.8, verbose=FALSE)
# cluster with k-means
pbmc.pca = Embeddings(pbmc_small, 'pca')
pbmc_small@meta.data$kmeans_clusters = kmeans(pbmc.pca, centers=3)$cluster
# compare the markers
Idents(pbmc_small) = pbmc_small@meta.data$seurat_clusters
louvain.markers = FindAllMarkers(pbmc_small,
logfc.threshold=1,
test.use='t',
verbose=FALSE)
Idents(pbmc_small) = pbmc_small@meta.data$kmeans_clusters
kmeans.markers = FindAllMarkers(pbmc_small,
logfc.threshold=1,
test.use='t',
verbose=FALSE)
pbmc_small@meta.data$jsi = marker_overlap(louvain.markers, kmeans.markers,
pbmc_small@meta.data$seurat_clusters, pbmc_small@meta.data$kmeans_clusters)
# which cells have the same markers, regardless of clustering?
FeaturePlot(pbmc_small, 'jsi')
})