Get the per-gene marker summaries across all rivals
get_marker_summary.RdReturns the summaries a marker is judged on: the AUROC of the reference
against its rivals reduced to one row per gene and reference group. Rank on
median_auroc for a marker that survives a single closely related rival, or
on min_auroc when it has to beat every rival unambiguously.
Examples
# per gene summaries across every rival group
sc <- demo_single_cells()
res <- find_specific_markers_sc(
sc,
column_of_interest = "cell_grp",
.verbose = FALSE
)
head(get_marker_summary(res))
#> ref_grp gene_id prop_ref median_auroc min_auroc mean_auroc max_auroc
#> <char> <char> <num> <num> <num> <num> <num>
#> 1: cell_type_1 gene_01 0.9880239 0.9531087 0.9520777 0.9531087 0.9541396
#> 2: cell_type_1 gene_02 0.9880239 0.9434354 0.9397771 0.9434354 0.9470938
#> 3: cell_type_1 gene_03 1.0000000 0.9699715 0.9674987 0.9699715 0.9724444
#> 4: cell_type_1 gene_04 0.9580838 0.9179306 0.9087095 0.9179306 0.9271517
#> 5: cell_type_1 gene_05 0.8383234 0.8323145 0.8302556 0.8323145 0.8343734
#> 6: cell_type_1 gene_06 0.9760479 0.9415538 0.9400476 0.9415538 0.9430600
#> worst_rival min_rank simes_p simes_fdr max_p max_p_fdr
#> <char> <int> <num> <num> <num> <num>
#> 1: cell_type_3 4 6.632426e-47 8.290532e-46 1.443307e-46 1.804134e-45
#> 2: cell_type_3 5 1.589201e-45 1.589201e-44 3.333359e-44 2.777799e-43
#> 3: cell_type_3 2 1.354582e-50 3.386456e-49 1.197853e-49 2.994632e-48
#> 4: cell_type_2 7 1.089790e-41 7.784218e-41 1.116568e-38 7.975489e-38
#> 5: cell_type_2 9 2.145260e-27 1.191811e-26 5.714230e-27 3.174572e-26
#> 6: cell_type_3 5 9.715047e-45 8.095872e-44 2.126360e-44 2.126360e-43
unlink(sc@dir_data, recursive = TRUE, force = TRUE)