Squidpy colocalisation analysis — squidpy

Squidpy is called thrugh reticulate and interacts with Seurat/Kandinsky object after anndata conversion with anndataR

squidpy_coloc(
  data = NULL,
  label = NULL,
  seed = 347548,
  nperms = 100,
  python_path = NULL,
  max.cap = 0.9,
  return.mat = T
)

Arguments

data: a Seurat object containing Kandinsky data slot
label: name of the metadata column variable containing cell type annotation to be used for join count test
seed: numeric, random seed
nperms: numeric, number of random permutations to perform for colocalisation test
python_path: path of python environment with MESA and its dependencies installed
max.cap: numeric, quantile cap to apply to colocalisation coefficients for colour scale creation. Default is 0.9, will set the coefficient value limit to the 90% quantile of all coefficients
return.mat: boolean, whether returning only colocalization plot (FALSE) or also a dataframe containing colocalization test results

a colocalization plot (when return.mat is FALSE) or a list containing colocalization plot and results in a tabular format

Squidpy must be installed within a python environment together with its dependencies