"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)