processingCluster: processingCluster

In CytoTree: A Toolkit for Flow And Mass Cytometry Data

Description

Calculate Principal Components Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (tSNE), Diffusion Map and Uniform Manifold Approximation and Projection (UMAP) of clusters calculated by runCluster.

Usage

processingCluster(
  object,
  perplexity = 5,
  k = 5,
  downsampling.size = 1,
  force.resample = TRUE,
  random.cluster = FALSE,
  umap.config = umap.defaults,
  verbose = FALSE,
  ...
)

Arguments

object	a CYT object
perplexity	numeric. Perplexity parameter (should not be bigger than 3 * perplexity < nrow(X) - 1, see details for interpretation). See Rtsne for more information.
k	numeric. The parameter k in k-Nearest Neighbor.
downsampling.size	numeric. Percentage of sample size of downsampling. This parameter is from 0 to 1. by default is 1.
force.resample	logical. Whether to do resample if downsampling.size < 1
random.cluster	logical. Whether to perfrom random downsampling. If FALSE, an uniform downsampling will be processed.
umap.config	object of class umap.config. See umap.
verbose	logic. Whether to print calculation progress.
...	options to pass on to the dimensionality reduction functions.

Value

A CYT object with dimensionality reduction of clusters

See Also

umap, fast.prcomp, Rtsne, destiny

Examples

cyt.file <- system.file("extdata/cyt.rds", package = "CytoTree")
cyt <- readRDS(file = cyt.file)

# After running clustering
set.seed(1)
cyt <- runCluster(cyt, cluster.method = "som", xdim = 3, ydim = 3, verbose = TRUE)

# Do not perfrom downsampling
cyt <- processingCluster(cyt, perplexity = 2)

# Perform cluster based downsampling
# Only keep 50% cells
cyt <- processingCluster(cyt, perplexity = 2, downsampling.size = 0.5)

# Processing clusters without downsampling step
cyt <- processingCluster(cyt, perplexity = 2, force.resample = FALSE)

CytoTree documentation built on Nov. 10, 2020, 2 a.m.