compare clustering parameters and classification outcomes

Usage

overcluster_test(
  expr,
  metadata,
  ref_mat,
  cluster_col,
  x_col = "UMAP_1",
  y_col = "UMAP_2",
  n = 5,
  ngenes = NULL,
  query_genes = NULL,
  threshold = 0,
  do_label = TRUE,
  do_legend = FALSE,
  newclustering = NULL,
  combine = TRUE
)

Arguments

expr: expression matrix
metadata: metadata including cluster info and dimension reduction plotting
ref_mat: reference matrix
cluster_col: column of clustering from metadata
x_col: column of metadata for x axis plotting
y_col: column of metadata for y axis plotting
n: expand n-fold for over/under clustering
ngenes: number of genes to use for feature selection, use all genes if NULL
query_genes: vector, otherwise genes with be recalculated
threshold: type calling threshold
do_label: whether to label each cluster at median center
do_legend: whether to draw legend
newclustering: use kmeans if NULL on dr or col name for second column of clustering
combine: if TRUE return a single plot with combined panels, if FALSE return list of plots (default: TRUE)

Value

faceted ggplot object

Examples

set.seed(42)
overcluster_test(
    expr = pbmc_matrix_small,
    metadata = pbmc_meta,
    ref_mat = cbmc_ref,
    cluster_col = "classified",
    x_col = "UMAP_1",
    y_col = "UMAP_2"
)
#> using # of genes: 599
#> similarity computation completed, matrix of 9 x 13, preparing output
#> using # of genes: 599
#> similarity computation completed, matrix of 45 x 13, preparing output
#> Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider increasing max.overlaps
#> Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider increasing max.overlaps