marker_overlap: Cell-Wise Marker Gene Overlap
In Core-Bioinformatics/ClustAssess: Tools for Assessing Clustering

marker_overlap

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' 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")
})

Core-Bioinformatics/ClustAssess documentation built on Dec. 19, 2024, 8:19 p.m.