perform.bbknn: Performs BBKNN integration

In connorhknight/IBRAP: Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP)

Performs BBKNN integration

Description

Performs BBKNN integration on defined method-assays and reductions contained within. This is performed on reductions.

Usage

perform.bbknn(
  object,
  assay,
  reduction,
  graph.name.suffix = "",
  batch,
  approx = FALSE,
  metric = "euclidean",
  neighbors_within_batch = 3,
  n_pcs = NULL,
  trim = NULL,
  annoy_n_trees = 10,
  use_faiss = TRUE,
  set_op_mix_ratio = 1,
  local_connectivity = 1,
  generate.diffmap = FALSE,
  n_comps = 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 the name of the reduction to provide for BBKNN. Default = NULL
graph.name.suffix	Character. Should a suffix be added to the end of bbknn as the graph name, i.e. parameter changes?
batch	Character. Column name in metadata indicating batch. Can be multiple.
approx	Character. Employs annoy's approximate neighbour finding. Useful for large datasets but may increase correction.
neighbors_within_batch	Numerical. How many neighbours to report per batch. Default = 3
n_pcs	Numerical. Range of principal components to use. Default = NULL
trim	Numerical. Trims the n of neighbours per cell to this value. Helps with population independence. Default = NULL
annoy_n_trees	Numerical. Number of trees to generate in annoy forest. More trees provides higher precision at the cost of increased resource demand and run time. Default = 10
use_faiss	Boolean. Uses faiss package to compute nearest neighbour, this improves run time at the cost of precision. Default = TRUE
set_op_mix_ratio	Numerical. UMAP connectivity parameter between 0 and 1. controls the blen d between a connectivity matrix formed exclusively from mutual nearest neighbour pairs (0) and a union of all observed neighbour relationships with the mutual pairs emphasised (1). Default = 1.0
local_connectivity	Numerical. How many nearest neighbours of each cell are assumed to be fully connected. Default = 1
generate.diffmap	Boolean. Should diffusion maps be generated from the neighourhood graphs, these will be stored in computational_reductions and can be used for umap generation and further neighbourhood generation. Default = TRUE
n_comps	Numerical. How many components should be generated for the diffusion maps. Default = 15
verbose	Logical Should function messages be printed?
seed	Numerical What seed should be set. Default = 1234
metric.	Character. Which distance metric to use when approx is TRUE, options: 'angular', 'euclidean', 'manhattan' or 'hamming'. Default = 'euclidean'
diffmap.name.sufix	Character. Should a suffix be added to the end of bbknn:diffmap as the reduction name, i.e. parameter changes?

Value

BBKNN connectivity graph contained in graphs in the indicated method-assays

Examples

object <- perform.bbknn(object = object, 
                        assay = c('SCT', 'SCANPY', 'SCRAN'), 
                        reduction = c('pca'),
                        batch = 'tech')
                        

connorhknight/IBRAP documentation built on March 9, 2023, 7:01 p.m.