Diffusion maps — perform.diffusion.map • IBRAP

Produces diffusion maps from previous reductions, i.e. PCA. Diffusion maps are known to better represent cellular trajectories in non-linear space

perform.diffusion.map(
  object,
  assay,
  reduction,
  dims,
  n.dcs = 15,
  k = 15,
  diffmap.name.suffix = "",
  verbose = FALSE,
  seed = 1234,
  ...
)

Arguments

object: IBRAP S4 class object
assay: Character. String containing indicating which assay to use
reduction: Character. String defining which reduction to supply to the clustering algorithm.
dims: Numerical list. The number of dimensions to use for each reduction. This is supplied as a list respective to the order of reductions.
n.dcs: Numerical. The number of diffusion components to produce. Default = 15
k: Numerical. How many neighbours should be found per cell. A higher value tends to be more accurate. Default = 15
diffmap.name.suffix: Character. What should be used as a suffix for diffmap

Examples


samp <- perform.diffusion.map(object = samp, 
                              assay = c('SCT','SCRAN','SCANPY'), 
                              reduction = 'pca', 
                              dims = list(20))                     
#> Error in is(object, "IBRAP"): object 'samp' not found