get best calls for each cluster

Usage

cor_to_call(
  cor_mat,
  metadata = NULL,
  cluster_col = "cluster",
  collapse_to_cluster = FALSE,
  threshold = 0,
  rename_prefix = NULL,
  carry_r = FALSE
)

Arguments

cor_mat: input similarity matrix
metadata: input metadata with tsne or umap coordinates and cluster ids
cluster_col: metadata column, can be cluster or cellid
collapse_to_cluster: if a column name is provided, takes the most frequent call of entire cluster to color in plot
threshold: minimum correlation coefficent cutoff for calling clusters
rename_prefix: prefix to add to type and r column names
carry_r: whether to include threshold in unassigned names

Value

dataframe of cluster, new ident, and r info

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    ref_mat = cbmc_ref
)
#> Variable features not available, using all genes instead
#> consider supplying variable features to `query_genes` argument.
#> using # of genes: 599
#> similarity computation completed, matrix of 9 x 13, preparing output

cor_to_call(res)
#> # A tibble: 9 × 3
#> # Groups:   cluster [9]
#>   cluster      type           r
#>   <chr>        <chr>      <dbl>
#> 1 B            B          0.909
#> 2 CD14+ Mono   CD14+ Mono 0.915
#> 3 FCGR3A+ Mono CD16+ Mono 0.929
#> 4 Memory CD4 T CD4 T      0.861
#> 5 Naive CD4 T  CD4 T      0.889
#> 6 DC           DC         0.849
#> 7 Platelet     Mk         0.732
#> 8 CD8 T        NK         0.826
#> 9 NK           NK         0.894