runRankEnrich: runRankEnrich

In bernard2012/Giotto: Spatial single-cell transcriptomics toolbox

Description

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

Usage

runRankEnrich(
  gobject,
  sign_matrix,
  expression_values = c("normalized", "scaled", "custom"),
  reverse_log_scale = TRUE,
  logbase = 2,
  output_enrichment = c("original", "zscore"),
  p_value = FALSE,
  n_times = 1000,
  name = NULL,
  return_gobject = TRUE
)

Arguments

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
p_value	calculate p-values (boolean, default = FALSE)
n_times	number of permutations to calculate for p_value
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.

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

See Also

makeSignMatrixRank

bernard2012/Giotto documentation built on Sept. 22, 2020, 10:29 a.m.