preprocessing.batch {MetaCyto} | R Documentation

## Preprocessing fcs files from different studies in batch.

### Description

It transform and compensate for the raw fcs files and write out the processed data to a new set of fcs files.

### Usage

    preprocessing.batch(inputMeta, assay = c("FCM", "CyTOF"), outpath,
      b = 1/150, fileSampleSize = 5000,
      excludeTransformParameters = c("FSC-A", "FSC-W", "FSC-H", "Time",
      "Cell_length"))

### Arguments

`inputMeta` | A data frame containing 2 columns: a column called "fcs_files" that contains the location (relative to the working directory) of each fcs file on the hard drive and a column called "study_id" that specify what study each fcs file belongs to.
`assay` | A vector, each element is either "FCM" or "CyTOF" to indicate the type of cytometry data.
`outpath` | A string indicating the directory the pre-processed fcs files will be written to.
`b` | A positive number used to specify the arcsinh transformation. f(x) = asinh (b*x) where x is the original value and f(x) is the value after transformation. The suggested value is 1/150 for flow cytometry (FCM) data and 1/8 for CyTOF data. If b = 0, the transformation is skipped.
`fileSampleSize` | An integer specifying the number of events sampled from each fcs file. If NULL, all the events will be pre-processed and wrote out to the new fcs files.
`excludeTransformParameters` | A vector specifying the name of parameters not to be transformed (left at linear scale).

### Details

The function takes a data frame which specifies the location of the fcs files and the panels the fcs files belong to. It transform the cytometry data using the arcsinh transformation. For flow cytometry data, it compensate the data using the compensation matrix supplied in the fcs file. the preprocessed fcs files and a table called "processed_sample_summary.csv" will be wrote out to outpath as well.

### Value

Does not return anything. The output is written to the directory specified by the "outpath".

### 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 for 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"))

[Package MetaCyto version 1.4.0 Index]