get top calls for each cluster

Usage

cor_to_call_topn(
  cor_mat,
  metadata = NULL,
  col = "cluster",
  collapse_to_cluster = FALSE,
  threshold = 0,
  topn = 2
)

Arguments

cor_mat: input similarity matrix
metadata: input metadata with tsne or umap coordinates and cluster ids
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
topn: number of calls for each cluster

Value

dataframe of cluster, new potential ident, and r info

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    ref_mat = cbmc_ref,
    query_genes = pbmc_vargenes,
    cluster_col = "classified"
)
#> using # of genes: 599
#> similarity computation completed, matrix of 9 x 13, preparing output

cor_to_call_topn(
    cor_mat = res,
    metadata = pbmc_meta,
    col = "classified",
    collapse_to_cluster = FALSE,
    threshold = 0.5
)
#> # A tibble: 18 × 3
#> # Groups:   classified [9]
#>    classified   type                  r
#>    <chr>        <chr>             <dbl>
#>  1 B            B                 0.909
#>  2 B            CD4 T             0.641
#>  3 CD14+ Mono   CD14+ Mono        0.915
#>  4 CD14+ Mono   CD16+ Mono        0.892
#>  5 CD8 T        NK                0.826
#>  6 CD8 T        CD8 T             0.769
#>  7 DC           DC                0.849
#>  8 DC           CD16+ Mono        0.752
#>  9 FCGR3A+ Mono CD16+ Mono        0.929
#> 10 FCGR3A+ Mono CD14+ Mono        0.843
#> 11 Memory CD4 T CD4 T             0.861
#> 12 Memory CD4 T CD8 T             0.835
#> 13 NK           NK                0.894
#> 14 NK           CD8 T             0.693
#> 15 Naive CD4 T  CD4 T             0.889
#> 16 Naive CD4 T  CD8 T             0.871
#> 17 Platelet     Mk                0.732
#> 18 Platelet     r<0.5, unassigned 0.307