flowFP-package: Package overview
In flowFP: Fingerprinting for Flow Cytometry

Description Details Classes Constructors Note Author(s) References See Also Examples

This package is used to explore flow cytometry data through the use of fingerprints. The broad aim of the package is to transform flow cytometric data into a form amenable to algorithmic analysis tools. Thus, it is useful to think of flowFP as an intermediate step between the acquisition of high-throughput flow cytometric data and empirical modeling, machine learning and knowledge discovery.

A fingerprint is a feature vector meant to efficiently represent the multivariate probability distribution function for a flow cytometry data set. It is produced by first creating a data-relevant model of a space, and then applying the model to a dataset, thereby producing fingerprints. Model creation is done through the flowFPModel constructor which can be customized via function arguments. After the model is built, it can be applied to arbitrary flowFrames or flowSets using the flowFP constructor. The resulting S4 object implements plotting and summary methods that allow the user to compare and contrast instances, using the flowFPModel as a sort of basis representation, akin for example to trigonometric functions in a Fourier Transform.

This package is closely integrated with flowCore. You will want to become familiar with it in order to effectively use flowFP.

Package:	flowFP
Type:	Package
Version:	1.1.2
Depends:	R(>= 2.5.0), flowCore, flowViz
Collate:	flowFPModel.R flowFP.R
Bioinformatics:	Flowcytometry, CellBasedAssays, Clustering, Statistics, Visualization
License:	Artistic-2.0
Built:	R 2.8.0; unix

flowFPModel-class is the fundamental class for the flowFP package. It represents the multivariate probability distribution function for a flow cytometry data set. Information is maintained in a number of slots, which should only be accessed through methods, described below, not by direct use of the @ operator. For a complete detailed list of slot names and descriptions look at the flowFPModel-class help page.

flowFP-class extends the flowFPModel and contains additional slots to record the assignment to and number of events in the bins of a flowFPModel. Methods are supplied to retrieve and visualize the the contents of a flowFP.

flowFPPlex-class is a container for a set of congruent flowFP objects (by congruent, we mean that each flowFP is a description of the same set of instances). When constructing or appending flowFPs into a plex, simple error checking is done to ensure each instance in each flowFP is equivalent. Both the sampleNames and the sampleClasses slots are consulted for internal consistency. An error is generated if any of the flowFPs to be joined in a plex contain different sampleNames and/or sampleClasses.

flowFPModel is the constructor for this class.

1 2	flowFPModel(fcs, name="Default Model", parameters=NULL, nRecursions="auto", dequantize=TRUE, sampleSize=NULL)

`fcs`	Either an `flowFrame` or a `flowSet` used to create the model.
`name`	Name given the model.
`parameters`	Parameters to consider when constructing the model.(e.g. c(1,5)).
`nRecursions`	The number of level of recursive subdivision.
`dequantize`	Setting this value causes a small incremental value to be added
	to each event starting with 1e-8. This effectively reduces the
	number of duplicate values to break ties when binning.
`sampleSize`	The max number of events to use out of each fcs file in a
	`flowFrame` or `flowSet`.

flowFP is the constructor for this class.

1	flowFP(fcs, model=NULL, sampleClasses=NULL, ...)

`fcs`	Either an `flowFrame` or a `flowSet` used to create the fingerprint.
`model`	A model created using `flowFPModel` or NULL in which case
	a model will be created from the fcs data supplied.
`sampleClasses`	List of sample class names to be assigned in order to the instances.
`...`	If the `model` is NULL the parameters are passed through to
	`flowFPModel`.

flowFPPlex is the constructor for this class.

1	flowFPPlex(fingerprints=NULL)

`fingerprints`	Either an single `flowFP` or a list of `flowFP`s
	(e.g. `fingerprints = c(fp1, fp2)`). All of the fingerprints
	must share the same sample names, and class names (or no class names).