RunBBKNN: Perform batch balanced KNN

In bbknnR: Perform Batch Balanced KNN in R

Perform batch balanced KNN

Description

Batch balanced KNN, altering the KNN procedure to identify each cell’s top neighbours in each batch separately instead of the entire cell pool with no accounting for batch. The nearest neighbours for each batch are then merged to create a final list of neighbours for the cell. Aligns batches in a quick and lightweight manner.

Usage

RunBBKNN(object, ...)

## Default S3 method:
RunBBKNN(
  object,
  batch_list,
  n_pcs = 50L,
  neighbors_within_batch = 3L,
  trim = NULL,
  approx = TRUE,
  use_annoy = TRUE,
  annoy_n_trees = 10L,
  pynndescent_n_neighbors = 30L,
  pynndescent_random_state = 0L,
  use_faiss = TRUE,
  metric = "euclidean",
  set_op_mix_ratio = 1,
  local_connectivity = 1,
  seed = 42,
  verbose = TRUE,
  ...
)

## S3 method for class 'Seurat'
RunBBKNN(
  object,
  batch_key,
  assay = NULL,
  reduction = "pca",
  n_pcs = 50L,
  graph_name = "bbknn",
  set_op_mix_ratio = 1,
  local_connectivity = 1,
  run_TSNE = TRUE,
  TSNE_name = "tsne",
  TSNE_key = "tSNE_",
  run_UMAP = TRUE,
  UMAP_name = "umap",
  UMAP_key = "UMAP_",
  min_dist = 0.3,
  spread = 1,
  seed = 42,
  verbose = TRUE,
  ...
)

Arguments

object	An object
...	Arguments passed to other methods
batch_list	A character vector with the same length as nrow(pca)
n_pcs	Number of dimensions to use. Default is 50.
neighbors_within_batch	How many top neighbours to report for each batch; total number of neighbours in the initial k-nearest-neighbours computation will be this number times the number of batches. This then serves as the basis for the construction of a symmetrical matrix of connectivities.
trim	Trim the neighbours of each cell to these many top connectivities. May help with population independence and improve the tidiness of clustering. The lower the value the more independent the individual populations, at the cost of more conserved batch effect. Default is 10 times neighbors_within_batch times the number of batches. Set to 0 to skip.
approx	If TRUE, use approximate neighbour finding - RcppAnnoy or pyNNDescent. This results in a quicker run time for large datasets while also potentially increasing the degree of batch correction.
use_annoy	Only used when approx = TRUE. If TRUE, will use RcppAnnoy for neighbour finding. If FALSE, will use pyNNDescent instead.
annoy_n_trees	Only used with annoy neighbour identification. The number of trees to construct in the annoy forest. More trees give higher precision when querying, at the cost of increased run time and resource intensity.
pynndescent_n_neighbors	Only used with pyNNDescent neighbour identification. The number of neighbours to include in the approximate neighbour graph. More neighbours give higher precision when querying, at the cost of increased run time and resource intensity.
pynndescent_random_state	Only used with pyNNDescent neighbour identification. The RNG seed to use when creating the graph.
use_faiss	If approx = FALSE and the metric is "euclidean", use the faiss package to compute nearest neighbours if installed. This improves performance at a minor cost to numerical precision as faiss operates on float32.
metric	What distance metric to use. The options depend on the choice of neighbour algorithm. "euclidean", the default, is always available.
set_op_mix_ratio	Pass to 'set_op_mix_ratio' parameter for umap
local_connectivity	Pass to 'local_connectivity' parameter for umap
seed	Set a random seed. By default, sets the seed to 42. Setting NULL will not set a seed.
verbose	Whether or not to print output to the console
batch_key	Column name in meta.data discriminating between your batches.
assay	used to construct Graph.
reduction	Which dimensional reduction to use for the BBKNN input. Default is PCA
graph_name	Name of the generated BBKNN graph. Default is bbknn.
run_TSNE	Whether or not to run t-SNE based on BBKNN results.
TSNE_name	Name to store t-SNE dimensional reduction.
TSNE_key	Specifies the string before the number of the t-SNE dimension names. tSNE by default.
run_UMAP	Whether or not to run UMAP based on BBKNN results.
UMAP_name	Name to store UMAP dimensional reduction.
UMAP_key	Specifies the string before the number of the UMAP dimension names. tSNE by default.
min_dist	Pass to 'min_dist' parameter for umap
spread	Pass to 'spread' parameter for umap

Value

Returns a Seurat object containing a new BBKNN Graph. If run t-SNE or UMAP, will also return corresponded reduction objects.

References

Polański, Krzysztof, et al. "BBKNN: fast batch alignment of single cell transcriptomes." Bioinformatics 36.3 (2020): 964-965.

bbknnR documentation built on Sept. 23, 2022, 1:06 a.m.