AdjScoreClustersCITE.internal: Use the Adjacency Score to evaluate colocaliztion of all...

In CamaraLab/STvEA: Spatially-resolved Transcriptomics via Epitope Anchoring

Use the Adjacency Score to evaluate colocaliztion of all pairs of CITE-seq clusters mapped to the CODEX spatial positions Takes matrices and data frames instead of STvEA.data class

Description

Use the Adjacency Score to evaluate colocaliztion of all pairs of CITE-seq clusters mapped to the CODEX spatial positions Takes matrices and data frames instead of STvEA.data class

Usage

AdjScoreClustersCITE.internal(
  adj_matrix,
  cite_clusters,
  transfer_matrix,
  c = 0,
  num_cores = 1,
  num_perms = 1000,
  perm_estimate = T
)

Arguments

adj_matrix	a (preferrably sparse) binary matrix of adjacency between the cells in the CODEX spatial coordinates
cite_clusters	a vector of cluster IDs for the CITE-seq cells
transfer_matrix	a (codex cells x cite-seq cells) matrix of weighted nearest neighbor assignments mapping each CITE-seq cell to k CODEX cells
c	constant used to determine width of diffusion, must be 0 <= c
num_cores	integer specifying the number of cores to be used in the computation. By default only one core is used. On Windows, this must be set to 1.
num_perms	number of permutations used to build the null distribution for each feature. By default is set to 1000.
perm_estimate	boolean indicating whether Gaussian distribution parameters should be determined from num_perms permutations to estimate the p-value. By default is set to TRUE.

CamaraLab/STvEA documentation built on April 2, 2024, 6:07 a.m.