extractGatedData {rflowcyt}R Documentation

Extract the data of a FCS object using a specified Gating Index

Description

This function will subset/reduce the rows of the data of an FCS object according to a column index of the "gate" matrix, which is created by using the function createGate-methods.

Usage

extractGatedData(x, gateNum = NULL, IndexValue.In = 1, MY.DEBUG = FALSE)

Arguments

x an "FCSgate" object obtained from createGate
gateNum the column position of the gating index that is specified in the "gate" matrix
IndexValue.In either 0 or 1 depending on what value should be set for inclusion in the extraction. The default is the value 1.
MY.DEBUG a boolean value that prints out debugging comments The default is FALSE and no debugging comments are printed.

Details

A "FCSgate" object with data having a reduced row length will be output along with an update to the following slots: "extractGatedData.msg" (The gateNum along with the inclusion value will be noted as a string), "current.data.obs" (the index of original data row positions that are currently in the data will be noted), and "metadata" (data dimension information will be updated along with the original status being changed to FALSE).

Value

A "FCSgate" S4 object is returned that extends the "FCS" object to contain additional slots:

gate a matrix whose columns are the gating indices for the original data
history vector which corresponds to each column gating index in "gate" and holds information about what variables and type of gate that was implemented and for what ranges of values
extractGatedData.msg vector of strings to specify what if any extraction has been implemented using extractGatedData; "NONE" specifies no extraction has been implemented on the data for that particular corresponding gating index
current.data.obs vector of the original data row positions that are currently still in the data matrix

Author(s)

A.J. Rossini and J.Y. Wan

References

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics : New York, 2001. pp.279-283.

Jerome H. Friedman and Nicholas I. Fisher. Bump Hunting in High-Dimensional Data. Tech Report. October 28, 1998.

J. Paul Robinson, et al. Current Protocols in Cytometry. John Wiley & Sons, Inc : 2001.

Mario Roederer and Richard R. Hardy. Frequency Difference Gating: A Multivariate Method for Identifying Subsets that Differe between Samples. Cytometry, 45:56-64, 2001.

Mario Roederer and Adam Treister and Wayne Moore and Leonore A. Herzenberg. Probability Binning Comparison: A Metric for Quantitating Univariate Distribution Differences. Cytometry, 45:37-46, 2001.

Keith A. Baggerly. Probability Binning and Testing Agreement between Multivariate Immunofluorescence Histograms: Extending the Chi-Squared Test. Cytometry, 45:141-150, 2001.

See Also

FCS-class, FCSgate-class, createGate

Examples

 if (require(rfcdmin)) {
      data.there<-is.element("MC.053",objects())
      if ((sum(data.there) != length(data.there))) {
        ## obtaining the FCS objects from VRC data
        data(MC.053min)
      }
  
####  test1 : Gating type: uniscut, univariate single cut
test1 <- createGate(MC.053, varpos=1, gatingrange=256,
                    type="uniscut", MY.DEBUG=TRUE)

#### test2.3 : Gating type : biscut -/-
test2.3 <- createGate(test1, varpos=c(1,2),
                      gatingrange=c(256, 300),
                      type="biscut",
                      biscut.quadrant="-/-",
                      prev.gateNum=NULL,
                      MY.DEBUG=TRUE)

### test 2.3.1 : extraction
test2.3.1 <- extractGatedData(test2.3, gateNum=2,
                              IndexValue.In=1,
                              MY.DEBUG=TRUE)
}

[Package rflowcyt version 1.4.0 Index]