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Combine the top markers of each cluster into one data frame.

Source: R/getMarkers.R
topMarkers.Rd

Convenience wrapper around getMarkers output: takes the top n genes from each cluster's (already ranked) table and stacks them into a single data frame, with a leading cluster column and a gene column (the same gene can be a top marker for more than one cluster, so row names cannot stay unique). Analogous to the single table returned by Seurat::FindAllMarkers.

Usage

topMarkers(markers, n = 10, p.value = 1, min.prop = 0)

Arguments

markers

The named list returned by getMarkers.

n

Integer. Number of top genes to take from each cluster. Default 10.

p.value

Numeric. Keep only genes whose adjusted p-value (or raw PValue if no adjusted column is present) is at or below this cutoff, applied before taking the top n. Default 1 (no filtering).

min.prop

Numeric in [0, 1]. Drop genes whose detection rate (the Prop column - proportion of cells expressing in the cluster) is below this value before taking the top n, so the slots are back-filled from further down the ranking. Default 0 (no filtering).

Value

A data frame with columns cluster, gene, and the per-cluster statistics from getMarkers, ordered by cluster then by each cluster's ranking.

Examples


data("xenium_bc_spe")
spe <- normalizeAssay(spe)
spe <- runPCA(spe)
#> Genes with 0 variance are excluded: ENSG00000135218 NegControlProbe_00002 NegControlCodeword_0504 NegControlCodeword_0509 NegControlCodeword_0510 NegControlCodeword_0511 NegControlCodeword_0512 NegControlCodeword_0516 NegControlCodeword_0517 NegControlCodeword_0518 NegControlCodeword_0519 NegControlCodeword_0520 NegControlCodeword_0522 NegControlCodeword_0526 NegControlCodeword_0527 NegControlCodeword_0530 NegControlCodeword_0536 NegControlCodeword_0537 BLANK_0030 BLANK_0163 BLANK_0165 BLANK_0212 BLANK_0221 BLANK_0230 BLANK_0237 BLANK_0311 BLANK_0361 BLANK_0365 BLANK_0382 BLANK_0384 BLANK_0387 BLANK_0388 BLANK_0391 BLANK_0393 BLANK_0397 BLANK_0399 BLANK_0404 BLANK_0406 BLANK_0410 BLANK_0411 BLANK_0418 BLANK_0425 BLANK_0432 BLANK_0447
spe <- findNbrsSNN(spe, dimred = "PCA")
#> [1] "Getting K-nearest neighbour"
#> [1] "Getting shared nearest neighbour"
spe <- getClusters(spe, resolution = 0.5)
#> Reassigned all 1 cells from clusters with <= 1 cells (0 by graph, 1 by distance).
markers <- getMarkers(spe)
top <- topMarkers(markers, n = 10)