binSpectMultiMatrix#

Description#

binSpect for a single spatial network and a provided expression matrix

Usage#

binSpectMultiMatrix(
  expression_matrix,
  spatial_networks,
  bin_method = c("kmeans", "rank"),
  subset_feats = NULL,
  kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
  nstart = 3,
  iter_max = 10,
  extreme_nr = 50,
  sample_nr = 50,
  percentage_rank = c(10, 30),
  do_fisher_test = TRUE,
  adjust_method = "fdr",
  calc_hub = FALSE,
  hub_min_int = 3,
  get_av_expr = TRUE,
  get_high_expr = TRUE,
  implementation = c("data.table", "simple", "matrix"),
  group_size = "automatic",
  do_parallel = TRUE,
  cores = NA,
  verbose = T,
  knn_params = NULL,
  set.seed = NULL,
  summarize = c("adj.p.value", "p.value")
)

Arguments#

expression_matrix

expression matrix

spatial_networks

list of spatial networks in data.table format

bin_method

method to binarize gene expression

subset_feats

only select a subset of features to test

kmeans_algo

kmeans algorithm to use (kmeans, kmeans_arma, kmeans_arma_subset)

nstart

kmeans: nstart parameter

iter_max

kmeans: iter.max parameter

extreme_nr

number of top and bottom cells (see details)

sample_nr

total number of cells to sample (see details)

percentage_rank

vector of percentages of top cells for binarization

do_fisher_test

perform fisher test

adjust_method

p-value adjusted method to use (see p.adjust)

calc_hub

calculate the number of hub cells

hub_min_int

minimum number of cell-cell interactions for a hub cell

get_av_expr

calculate the average expression per gene of the high expressing cells

get_high_expr

calculate the number of high expressing cells per gene

implementation

enrichment implementation (data.table, simple, matrix)

group_size

number of genes to process together with data.table implementation (default = automatic)

do_parallel

run calculations in parallel with mclapply

cores

number of cores to use if do_parallel = TRUE

verbose

be verbose

knn_params

list of parameters to create spatial kNN network

set.seed

set a seed before kmeans binarization

summarize

summarize the p-values or adjusted p-values

Value#

data.table with results