runRankEnrich | R Documentation |
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
)
gobject |
Giotto object |
sign_matrix |
Matrix of signature genes for each cell type / process |
expression_values |
expression values to use |
reverse_log_scale |
reverse expression values from log scale |
logbase |
log base to use if reverse_log_scale = TRUE |
output_enrichment |
how to return enrichment output |
ties_method |
how to handle rank ties |
p_value |
calculate p-values (boolean, default = FALSE) |
n_times |
number of permutations to calculate for p_value |
rbp_p |
fractional binarization threshold (default = 0.99) |
num_agg |
number of top genes to aggregate (default = 100) |
name |
to give to spatial enrichment results, default = rank |
return_gobject |
return giotto object |
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.
data.table with enrichment results
makeSignMatrixRank
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