binSpectMulti

binSpectMulti

Description

binSpect for multiple spatial kNN networks

Usage

binSpectMulti(
  gobject,
  feat_type = NULL,
  spat_unit = NULL,
  bin_method = c("kmeans", "rank"),
  expression_values = c("normalized", "scaled", "custom"),
  subset_feats = NULL,
  subset_genes = NULL,
  spatial_network_k = c(5, 10, 20),
  reduce_network = FALSE,
  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

Argument	Description
gobject	giotto object
feat_type	feature type
spat_unit	spatial unit
bin_method	method to binarize gene expression
expression_values	expression values to use
subset_feats	only select a subset of features to test
subset_genes	deprecated, use subset_feats
spatial_network_k	different k’s for a spatial kNN to evaluate
reduce_network	default uses the full network
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	percentage of top cells for binarization
do_fisher_test	perform fisher test
adjust_method	p-value adjusted method to use (see ``p.adjust` <#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

Details

We provide two ways to identify spatial genes based on gene expression binarization.

Both methods are identicial except for how binarization is performed.

• list(“1. binarize: “) list(“Each gene is binarized (0 or 1) in each cell with “, list(“kmeans”), ” (k = 2) or based on “, list(“rank”), ” percentile”)

• list(“2. network: “) list(“Alll cells are connected through a spatial network based on the physical coordinates”)

• list(“3. contingency table: “) list(“A contingency table is calculated based on all edges of neighboring cells and the binarized expression (0-0, 0-1, 1-0 or 1-1)”)

• list(“4. For each gene an odds-ratio (OR) and fisher.test (optional) is calculated”)
  Three different kmeans algorithmes have been implemented:

• list(“1. kmeans: “) list(“default, see “, list(list(“kmeans”)), ” “)

• list(“2. kmeans_arma: “) list(“from ClusterR, see “, list(list(“KMeans_arma”)), ” “)

• list(“3. kmeans_arma_subst: “) list(“from ClusterR, see “, list(list(“KMeans_arma”)), “,n”, ” but random subsetting the vector for each gene to increase speed. Change extreme_nr and sample_nr for control. “)
  Other statistics are provided (optional):

• Number of cells with high expression (binary = 1)

• Average expression of each gene within high expressing cells

• Number of hub cells, these are high expressing cells that have a user defined number of high expressing neighbors
  By selecting a subset of likely spatial genes (e.g. soft thresholding highly variable genes) can accelerate the speed. The simple implementation is usually faster, but lacks the possibility to run in parallel and to calculate hub cells. The data.table implementation might be more appropriate for large datasets by setting the group_size (number of genes) parameter to divide the workload.

Value

data.table with results (see details)