Function to calculate gene signature enrichment scores per spatial position using a rank based approach.
runRankEnrich(
gobject,
sign_matrix,
expression_values = c("normalized", "raw", "scaled", "custom"),
reverse_log_scale = TRUE,
logbase = 2,
output_enrichment = c("original", "zscore"),
ties_method = c("random", "max"),
p_value = FALSE,
n_times = 1000,
rbp_p = 0.99,
num_agg = 100,
name = NULL,
return_gobject = TRUE
)Giotto object
Matrix of signature genes for each cell type / process
expression values to use
reverse expression values from log scale
log base to use if reverse_log_scale = TRUE
how to return enrichment output
how to handle rank ties
calculate p-values (boolean, default = FALSE)
number of permutations to calculate for p_value
fractional binarization threshold (default = 0.99)
number of top genes to aggregate (default = 100)
to give to spatial enrichment results, default = rank
return giotto object
data.table with enrichment results
sign_matrix: a rank-fold matrix with genes as row names and cell-types as column names.
Alternatively a scRNA-seq matrix and vector with clusters can be provided to makeSignMatrixRank, which will create
the matrix for you.
First a new rank is calculated as R = (R1*R2)^(1/2), where R1 is the rank of fold-change for each gene in each spot and R2 is the rank of each marker in each cell type. The Rank-Biased Precision is then calculated as: RBP = (1 - 0.99) * (0.99)^(R - 1) and the final enrichment score is then calculated as the sum of top 100 RBPs.