perform.nn: Finds the shared nearest neighbourhood for the cells. This...

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

Finds the shared nearest neighbourhood for the cells. This supplies a graph.

Description

Neighbourhood graph generator utilised. According to the assay name a different method will be used. If the assay is derived from scanpy then the scanpy algorithm will be applied. Otherwise, the seurat implementation will be applied.

Usage

perform.nn(
  object,
  assay,
  reduction,
  neighbour.name.suffix = "",
  dims.use = NULL,
  k.param = 20,
  prune.SNN = 1/15,
  nn.method = "annoy",
  n.trees = 50,
  nn.eps = 0,
  annoy.metric = "euclidean",
  n_neighbors = 15,
  random_state = 0,
  method = "umap",
  metric = "euclidean",
  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 which reduction to supply to the clustering algorithm.
neighbour.name.suffix	Character. String defining the names to store the neighbourhood graph results under.
dims.use	Numerical. (scanpy only) How many components of the reduction should be used, 0 means that all will be used. Default = 0
k.param	Numerical. The number of k when calcuating k-nearest neighbour. Default = 20
prune.SNN	Numerical. Setas acceptance cutoff for jaccard index whilst computing neighbourhood overlap for SNN construction. Any edges with a value less than this parameter will be removed. 0 = no pruning and 1 = prune everything. Default = 0
nn.method	Character. Nearest neighbour method, either 'rann' or 'annoy'. Default = 'annoy'
n.trees	Numerical. More trees facilitates hgiher precision when using 'annoy' method. Default = 20
nn.eps	Numerical. Margin of error when performing nearest neighbour search whilst using rann method. 0 would imply an exact search. Default = 0.0
annoy.metric	Character. Distance metric for annoy method. Options: 'euclidean', 'cosine', 'manhattan', 'hamming'. Default = 'euclidean'
n_neighbors	Numerical. (scanpy only) How many neighbours should be found per cell, a higher value typically achieves more accurate results. Default = 15
random_state	Numerical. (scanpy only) The seed value to use. Default = 0
method	Character. (scanpy only) String indicating which methodology to use including: ‘umap’, ‘gauss’ or ‘rapids’
metric	Character. (scanpy only) String indicating which distance metric to use, including: ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’, ‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’ or ‘manhattan’
generate.diffmap	Boolean. (scanpy only) 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. (scanpy only) How many components should be generated for the diffusion maps. Default = 15
compute.SNN	Boolean. Should the shared nearest neighbour graph be calculated. Default = TRUE
diffmap.name	Character. (scanpy only) What should the diffusion maps be named.

Examples

# generates a diffusion map from the scanpy assay
object <- perform.nn(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'), 
                     reduction = c('pca'),
                     dims = list(0,0))
                        

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