`runRankEnrich`¶

runRankEnrich

Description¶

Function to calculate gene signature enrichment scores per spatial position using a rank based approach.

Usage¶

runRankEnrich(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  sign_matrix,
  expression_values = c("normalized", "raw", "scaled", "custom"),
  reverse_log_scale = TRUE,
  logbase = 2,
  output_enrichment = c("original", "zscore"),
  ties_method = c("average", "max"),
  p_value = FALSE,
  n_times = 1000,
  rbp_p = 0.99,
  num_agg = 100,
  name = NULL,
  return_gobject = TRUE
)

Arguments¶

Argument	Description
`gobject`	Giotto object
`spat_unit`	spatial unit
`feat_type`	feature type
`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

Details¶

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. list()

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.

Value¶

data.table with enrichment results

Seealso¶

``makeSignMatrixRank` <#makesignmatrixrank>`_

runRankEnrich¶