# Progression estimation of cytof expression data

### Description

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

### Usage

1 2 3 |

### 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 calcualtion, default is "euclidean", other choices like "manhattan", "cosine", "rankcor". |

`out_dim` |
Number of transformed dimenions choosed for output. |

`clusterSampleMethod` |
Cluster sampling method including |

`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

1 2 3 4 5 6 7 8 9 | ```
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, sep = "\t", header = TRUE)[, 1])
xdata <- cytof_exprsMerge(fcsFile, markers = markers, 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)
``` |