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 |
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.