cytof_progression: Progression estimation of cytof expression data

In JinmiaoChenLab/cytofkit2: cytofkit2: an integrated mass cytometry data analysis pipeline. Cytofkit provides functions for data pre-processing, fast clustering, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets

Progression estimation of cytof expression data

Description

Infer the progression based on the relationship of cell subsets estimated using ISOMAP or Diffusion map.

Usage

cytof_progression(
  data,
  cluster,
  method = c("isomap", "NULL"),
  distMethod = "euclidean",
  out_dim = 2,
  clusterSampleMethod = c("ceil", "all", "fixed", "min"),
  clusterSampleSize = 500,
  sampleSeed = 123
)

Arguments

data	Expression data matrix.
cluster	A vector of cluster results for the data.
method	Method for estimation of cell progression, isomap.
distMethod	Method for distance calculation, default is "euclidean", other choices like "manhattan", "cosine", "rankcor".
out_dim	Number of transformed dimensions choosen for output.
clusterSampleMethod	Cluster sampling method including ceil, all, min, fixed. The default option is ceil, up to a fixed number (specified by fixedNum) of cells are sampled without replacement from each cluster and combined for analysis. all: all cells from each cluster are combined for analysis. min: The minimum number of cells among all clusters are sampled from cluster and combined for analysis. fixed: a fixed num (specified by fixedNum) of cells are sampled (with replacement when the total number of cell is less than fixedNum) from each cluster and combined for analysis.
clusterSampleSize	The number of cells to be sampled from each cluster.
sampleSeed	The seed for random down sample of the clusters.

Value

a list. Includes: sampleData, sampleCluster and progressionData.

Examples

## Not run: 
d<-system.file('extdata', package='cytofkit2')
fcsFile <- list.files(d, pattern='.fcs$', full=TRUE)
parameters <- list.files(d, pattern='.txt$', full=TRUE)
markers <- as.character(read.table(parameters, header = TRUE)[, 1])
xdata <- cytof_exprsMerge(fcsFile, mergeMethod = 'fixed', fixedNum = 2000)
clusters <- cytof_cluster(xdata = xdata, method = "Rphenograph")
prog <- cytof_progression(data = xdata, cluster = clusters, clusterSampleSize = 100)
d <- as.data.frame(cbind(prog$progressionData, cluster = factor(prog$sampleCluster)))
cytof_clusterPlot(data =d, xlab = "diffusionmap_1", ylab="diffusionmap_2",
                  cluster = "cluster", sampleLabel = FALSE)

## End(Not run)

JinmiaoChenLab/cytofkit2 documentation built on May 12, 2022, 8:09 a.m.