cytof_progression {cytofkit} | R Documentation

## 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("diffusionmap", "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 or diffusionmap.
`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

    d<-system.file('extdata', package='cytofkit')
    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)

[Package cytofkit version 1.12.0 Index]