Cell-Wise Marker Gene Overlap — marker

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' or 'avg_log2FC' 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)
    data("pbmc_small")

    # 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")
})
#> Loading required package: SeuratObject
#> Loading required package: sp
#> 
#> Attaching package: ‘SeuratObject’
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, t