a function to determine statistically trait-enriched cell by permutation test.
get_sigcell_simple(
knn_sparse_mat,
seed_idx,
topseed_npscore,
permutation_times = 1000,
true_cell_significance = 0.05,
rda_output = FALSE,
out_rda = tempfile(fileext = "true_cell_df.rda"),
mycores = 1,
rw_gamma = 0.05
)a sparse matrix used for network propagation, which indicates the adjacent matrix (m x m, where m is the cell number) of cell-to-cell network (M-kNN graph)
a logical vector indicating seed cells (TRUE) and non-seed cells (FALSE) with length of m, where m is the cell number. The length and position are corresponding to knn_sparse_mat
a numeric vector of real network propagation score
an integer describe times of permutation test for each cell
a numeric value between 0-1 indicating the significant threshold used to determine statistically trait-enriched cell
if output details of each permutation as a rda
if rda_output=T, an rda will be outputed with path and name specified
how many cores used for permutation test
gamma from randomWalk_sparse function. Keep the same with what you used in the real setting
A dataframe with two columns, the first one is seed index (the same with input), the second one is significance from permutation test. In addition, an R data is generated, which contains this dataframe and another dataframe depicting the network propagation score for each cell at each permuation.
knn_sparse_mat <- example_results$mutualknn30
mono_mat <- example_results$mono_mat
ture_cell_df <- get_sigcell_simple(knn_sparse_mat=knn_sparse_mat,
seed_idx=mono_mat$seed_idx,
topseed_npscore=mono_mat$np_score,
permutation_times=100)
#> Get started!
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#> 100
#> cells passed 0.001 threshold: 13.28%
#> cells passed 0.01 threshold: 16.77%
#> cells passed 0.05 threshold: 26.39%
#> your emprical P value threshold: 0.05
#> what propertion of enriched cells over all cells: 26.39%
#> what propertion of seed cells that are enriched cells: 98.25%
#> fold of true cell over seed: 5.25