Calculate adjusted p-values for hypergeometric test of gene lists or jaccard index

Usage

compare_lists(
  bin_mat,
  marker_mat,
  n = 30000,
  metric = "hyper",
  output_high = TRUE,
  details_out = FALSE
)

Arguments

bin_mat: binarized single-cell expression matrix, feed in by_cluster mat, if desired
marker_mat: matrix or dataframe of candidate genes for each cluster
n: number of genes in the genome
metric: adjusted p-value for hypergeometric test, or jaccard index
output_high: if true (by default to fit with rest of package), -log10 transform p-value
details_out: whether to also output shared gene list from jaccard

Value

matrix of numeric values, clusters from expr_mat as row names, cell types from marker_mat as column names

Examples

pbmc_mm <- matrixize_markers(pbmc_markers)

pbmc_avg <- average_clusters(
    pbmc_matrix_small,
    pbmc_meta,
    cluster_col = "classified"
)

pbmc_avgb <- binarize_expr(pbmc_avg)

compare_lists(
    pbmc_avgb,
    pbmc_mm,
    metric = "spearman"
)
#> list of markers instead of matrix, only supports jaccard
#>                 0   1   2   3   4   5   6   7   8
#> Naive CD4 T  1090 818 124 706 712  20 298 318  48
#> Memory CD4 T 1090 818 122 706 706   0 276 368  54
#> CD14+ Mono   1090 776  68 774 576 170 246 338  40
#> B            1096 772 128 864 596  18 232 404  90
#> CD8 T        1090 808  34 754 758  78 478 434 126
#> FCGR3A+ Mono 1090 792  76 776 540 150 240 284 100
#> NK           1090 798  72 706 758  22 514 380  98
#> DC           1090 792  62 806 644 182 334 506 130
#> Platelet     1094 816  72 816 606 158 296 316 194