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>`_