| "FCS-class" {rflowcyt} | R Documentation |
This class represents objects read from raw binary Flow Cytometry Standard (FCS) files. These files contain a data portion, consisting of immunofluorescence and other column variables for each cell or row observation, and a metadata portion, which contains information such as parameter shortnames, longnames, ranges and data dimensions as well as file information.
Objects can be created by calls of the form new("FCS", ...).
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.
signature(x = "FCS"): Extracts the datasignature(x = "FCS"): Replaces or sets the datasignature(x = "FCS"): Extracts the metadata signature(x = "FCS"): Replaces or sets the
metadata signature(x = "FCS", colvar = "vector"):
Adds a column parameter to the data signature(x = "FCS"): Checks the
compatibility of the metadata against the data dimensions and
column/parameter names and ranges signature(from = "FCS", to = "matrix"): Returns
the data as a matrixsignature(from = "FCS", to = "data.frame"):
Returns the data as a data.frame signature(from = "matrix", to = "FCS"): Returns
an FCS object with data and default prototype metadatasignature(from = "data.frame", to = "FCS"): Returns
an FCS object with data and default prototype metadata signature(x = "FCS") : Returns the dimensions
(ie, the number of rows and columns respectively) of the data
matrix; the output is a vector signature(x = "FCS", y = "FCS"): Compares the
equality of two objects in terms of data and metadata
correspondence signature(x = "FCS"): Sets the discrepant
metadata slots to values in from the data signature(x = "FCS"): Returns the complete data
portion of the objectsignature(x = "FCS"): Returns the complete
metadata portion of the object signature(x = "FCS", y = "missing"): Plots the
object as a pairs plot (with rectangular binned contour-image plots or
hexagonal binned image plots) or as a joint or marginal image
parallel coordinates plotsignature(x = "FCS"): Prints a brief description
about the original filename, dimensions of the data, and the
original status of the current object's datasignature(object = "FCS"): Prints a brief description
about the original filename, dimensions of the data, and the
original status of the current object's data signature(object = "FCS"): Summaries the
data's dimensions, five-number summaries on the column parameters,
the information contained in the metadata
The function read.FCS is used to read in a raw binary FCS
files and output a "FCS-class" object.
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.
read.FCS,
"FCSgate-class",
"FCSsummary-class",
"FCSmetadata-class",
"plot-methods",
"print-methods",
"show-methods",
"summary-methods",
"coerce-methods",
"[-methods",
"[[-methods",
"[<--methods",
"[[<--methods",
checkvars,
fixvars,
equals,
addParameter,
fluors,
metaData,
dim.FCS
## a default FCS object
default.FCSobj<-new("FCS")
## making my own FCS object
## first making up the data
dummy.data<-matrix(1:1000, ncol=10)
colnames(dummy.data)<-paste("foo", 1:10, sep="")
## second making up the metadata
## default FCSmetadata
dummy.metadata<-new("FCSmetadata")
## user-defined metadata
foo.metadata<-new("FCSmetadata", mode="none", size=100, nparam=10,
shortnames=paste("V", 1:10, sep=""), longnames=colnames(dummy.data),
paramranges=unlist(apply(dummy.data, 2, max)), filename="",
objectname="foo.FCSobj", fcsinfo=list("extraInfo1"="dummy FCS",
"extraInfo2"=9:20))
foo.FCSobj<-new("FCS", data=dummy.data, metadata=foo.metadata)
dummy.FCSobj<-new("FCS", data=matrix(), metadata=dummy.metadata)
## extraction of the metadata
foo.FCSobj[["size"]]
## replacement of the metadata
## introduce an error in the column length
foo.FCSobj[["nparam"]]<-0
## extraction of the data
first.ten.obs<-foo.FCSobj[1:10,]
## replacement of the data
foo.FCSobj[1:10,]<-matrix(1:100, ncol=10)
## addParameter
foo.FCSobj<-addParameter(foo.FCSobj, 1:100, shortname="newvar",
longname="newlymadevariable", use.shortname=FALSE)
## replacement of the metadata
## introduce an error in the column length
foo.FCSobj[["nparam"]]<-0
## checkvars
correct.status.is.FALSE<-checkvars(foo.FCSobj)
## coerce FCS to matrix
coerced.mat<-as(foo.FCSobj, "matrix")
is(coerced.mat, "matrix")
## coerce FCS to data.frame
coerced.df<-as(foo.FCSobj, "data.frame")
is(coerced.df, "data.frame")
## coerce matrix to FCS
FCSobj1<-as(coerced.mat, "FCS")
is(FCSobj1, "FCS")
## coerce data.frame to FCS
FCSobj2<-as(coerced.df, "FCS")
is(FCSobj2, "FCS")
##obtaining the dimensions of the data
dim.FCS(FCSobj2)
## equals
## should be TRUE
equals(FCSobj1, FCSobj2, check.filename=TRUE, check.objectname=TRUE)
## default does not check filename or objectname equality
## should be FALSE
equals(foo.FCSobj, dummy.FCSobj)
## fixvars
foo.FCSobj<-fixvars(foo.FCSobj)
## fluors
data.mat<-fluors(foo.FCSobj)
## metaData
metadata.ls<-metaData(foo.FCSobj)
## plot
## not interesting to plot dummy data
## default plot is pairs.CSP <pairs plot with Contour-images>
## plot(foo.FCSobj)
## can do joint image.parallel.coordinates pairs plots
## plot(foo.FCSobj, image.parallel.plot=TRUE)
## can do marginal image parallel coordinates pairs plots
## plot(foo.FCSobj, image.parallel.plot=TRUE, joint=FALSE)
## print
print(foo.FCSobj)
foo.FCSobj
## show
show(foo.FCSobj)
## summary
summary(foo.FCSobj)
summary(dummy.FCSobj)