perform.diffusion.map: Diffusion maps

In connorhknight/IBRAP_no_decontX: Integrated Benchmarking scRNA-seq Analytical Pipeline

Description

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

Usage

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

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))                     

connorhknight/IBRAP_no_decontX documentation built on Feb. 13, 2022, 2:32 p.m.