"FCSgate-class" {rflowcyt} | R Documentation |
This class of objects extends the class FCS-class
to incorporate information from gating which is a procedure by which
rows or cells from the data are selected via one or two dimensional
value restrictions or gating ranges.
Objects can be created by calls of the form new("FCSgate", ...)
.
Essentially this new object includes the FCS-class
object.
gate
:"matrix"
containing the
gating indices such that each column corresponds to a different
gating procedure/index and the rows correspond to the positions of
the original row/cell observations. history
:"vector"
containing the
gating history strings such that each vector element corresponds
to a different gating procedure/index and each string contains
information about the particular gate, column variables that were
used, and other additional comments.extractGatedData.msg
:"vector"
containing strings describing any extraction that took place
corresponding to each gating procedure/index and history string; each
string contains information about the particular corresponding
gate column position and gate name and what value index was for
inclusion/selection (ie, IndexValue.In)current.data.obs
:"vector"
contains the current data positional values from the original data data
:"matrix"
which holds
integer data such that the columns are the variables (usually
immunofluorescence measurements) and the rows are the cell
observations. metadata
:"FCSmetadata"
which
holds information about the file, data, and column variables among
other items in the header of the original raw FCS binary file.
Class "FCS"
, directly.
No methods defined with class "FCSgate" in the signature.
The methods createGate
and icreateGate
, functionally
without plots
or interactively with plots, respectively, extends the
FCS-class
to the
FCSgate-class. Some interactive gating schemes are noted in FHCRC.HVTNFCS
and VRC.HVTNFCS
. Further testing after gating is implemented by
runflowcytests
on the particular variable of interest which is
usually the Interferon Gamma Immunofluoroescence measurement.
A.J. Rossini, J.Y. Wan, and Zoe Moodie
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.
createGate
,
icreateGate
,
extractGatedData
,
extractGateHistory
,
FHCRC.HVTNFCS
,
VRC.HVTNFCS
,
"FCS-class"
,
runflowcytests
default.FCSgateobj<-new("FCSgate")