Marker atlas visualization

Find top markers per cluster and visualize as a heatmap.

Usage

VisMarkerAtlas(
  object,
  markers_top = 5,
  logfc_threshold = 0.25,
  min_percent = 0.1,
  test_method = "wilcox",
  palette = "C"
)

Arguments

object

A Seurat object; required.

markers_top

Number of top markers per cluster. Default: 5.

logfc_threshold

Log fold-change threshold for marker finding. Default: 0.25.

min_percent

Minimum percent expressed. Default: 0.1.

test_method

Differential test method, e.g., 'wilcox', 't', 'LR'. Default: 'wilcox'.

palette

Viridis palette option for color/fill. Default: "C".

Value

A list with markers (top markers table) and plot (heatmap ggplot).

Author

benben-miao

Examples

obj <- SeuratVisProExample(
    n_cells = 300,
    n_genes = 1000,
    n_clusters = 10,
    seed = 123,
    genes_mt = "^MT-",
    neighbor_dims = 10,
    cluster_res = 0.5,
    umap_dims = 10,
    spatial = FALSE)
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#> 
#> Number of nodes: 300
#> Number of edges: 4508
#> 
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.9485
#> Number of communities: 10
#> Elapsed time: 0 seconds

res <- VisMarkerAtlas(
  obj,
  markers_top = 5,
  logfc_threshold = 0.25,
  min_percent = 0.1,
  test_method = "wilcox",
  palette = "C")
#> Calculating cluster 0
#> For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
#> (default method for FindMarkers) please install the presto package
#> --------------------------------------------
#> install.packages('devtools')
#> devtools::install_github('immunogenomics/presto')
#> --------------------------------------------
#> After installation of presto, Seurat will automatically use the more 
#> efficient implementation (no further action necessary).
#> This message will be shown once per session
#> Calculating cluster 1
#> Calculating cluster 2
#> Calculating cluster 3
#> Calculating cluster 4
#> Calculating cluster 5
#> Calculating cluster 6
#> Calculating cluster 7
#> Calculating cluster 8
#> Calculating cluster 9

res$plot

head(res$markers)
#> # A tibble: 6 × 7
#>      p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene 
#>      <dbl>      <dbl> <dbl> <dbl>     <dbl> <fct>   <chr>
#> 1 9.50e-19       1.80 1     0.622  9.50e-16 0       G848 
#> 2 1.86e-17       1.83 0.976 0.622  1.86e-14 0       G809 
#> 3 1.92e-17       1.62 1     0.653  1.92e-14 0       G844 
#> 4 3.46e-17       1.69 1     0.653  3.46e-14 0       G859 
#> 5 3.79e-17       1.73 0.976 0.653  3.79e-14 0       G847 
#> 6 5.19e-19       1.97 1     0.563  5.19e-16 1       G901