autoCluster.batch {MetaCyto} | R Documentation

## Cluster the preprocessed fcs files from different studies in batch

### Description

A function that clusters the pre-processed fcs files from different studies in batch.

### Usage

    autoCluster.batch(preprocessOutputFolder,
      excludeClusterParameters = c("TIME"), labelQuantile = 0.95,
      clusterFunction = flowSOM.MC, minPercent = 0.05, ...)

### Arguments

`preprocessOutputFolder` | Directory where the preprocessed results are stored. Should be the same with the outpath argument in preprocessing.batch function.
`excludeClusterParameters` | A vector specifying the name of markers not to be used for clustering and labeling. Typical example includes: Time, cell_length.
`labelQuantile` | A number between 0.5 and 1. Used to specify the minimum percent of cells in a cluster required to express higher or lower level of a marker than the cutoff value for labeling.
`clusterFunction` | The name of unsupervised clustering function the user wish to use for clustering the cells. The default is "flowSOM.MC". The first argument of the function must take a flow frame, the second argument of the function must take a vector of excludeClusterParameters. The function must return a list of clusters containing cell IDs. flowSOM.MC and flowHC are implemented in the package. For other methods, please make your own wrapper functions.
`minPercent` | A number between 0 and 0.5. Used to specify the minimum percent of cells in the positive and negative region after bisection. Keep it small to avoid bisecting uni-mode distributions.
`...` | Pass arguments to clusterFunction

### Value

A vector of labels identified in the cytometry data.

### Examples

    #get meta-data
    fn=system.file("extdata","fcs_info.csv",package="MetaCyto")
    fcs_info=read.csv(fn,stringsAsFactors=FALSE,check.names=FALSE)
    fcs_info$fcs_files=system.file("extdata",fcs_info$fcs_files,
                                   package="MetaCyto")
    # Make sure the transformation parameter "b" and the "assay" argument
    # are correct of FCM and CyTOF files
    b=assay=rep(NA,nrow(fcs_info))
    b[grepl("CyTOF",fcs_info$study_id)]=1/8
    b[grepl("FCM",fcs_info$study_id)]=1/150
    assay[grepl("CyTOF",fcs_info$study_id)]="CyTOF"
    assay[grepl("FCM",fcs_info$study_id)]="FCM"
    # preprocessing
    preprocessing.batch(inputMeta=fcs_info,
                        assay=assay,
                        b=b,
                        outpath="Example_Result/preprocess_output",
                        excludeTransformParameters=c("FSC-A","FSC-W","FSC-H",
                        "Time","Cell_length"))
    # Make sure marker names are consistant in different studies
    files=list.files("Example_Result",pattern="processed_sample",
                     recursive=TRUE,full.names=TRUE)
    nameUpdator("CD8B","CD8",files)
    # find the clusters
    excludeClusterParameters=c("FSC-A","FSC-W","FSC-H","SSC-A",
                               "SSC-W","SSC-H","Time",
                              "CELL_LENGTH","DEAD","DNA1","DNA2")
    cluster_label=autoCluster.batch(
                  preprocessOutputFolder="Example_Result/preprocess_output",
                  excludeClusterParameters=excludeClusterParameters,
                  labelQuantile=0.95,
                  clusterFunction=flowHC)

[Package MetaCyto version 1.12.0 Index]