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
as a sort of basis representation, akin for example to trigonometric functions in a
This package is closely integrated with
You will want to become familiar with it in order to effectively use flowFP.
|Depends:||R(>= 2.5.0), flowCore, flowViz|
|Bioinformatics:||Flowcytometry, CellBasedAssays, Clustering, Statistics, Visualization|
|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
flowFPModel. Methods are supplied to retrieve and visualize the
the contents of a
flowFPPlex-class is a container for a set of congruent
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
equivalent. Both the
sampleNames and the
sampleClasses slots are consulted
for internal consistency. An error is generated if any of the
to be joined in a plex contain different
sampleNames and/or s
flowFPModel is the constructor for this class.
|| Either an
||Name given the model.|
||Parameters to consider when constructing the model.(e.g. c(1,5)).|
||The number of level of recursive subdivision.|
||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.|
||The max number of events to use out of each fcs file in a|
flowFP is the constructor for this class.
|| Either an
|| A model created using
|a model will be created from the fcs data supplied.|
||List of sample class names to be assigned in order to the instances.|
|| If the
flowFPPlex is the constructor for this class.
|| Either an single
|must share the same sample names, and class names (or no class names).|
For further information please see the vignette.
M. Roederer, et. al. (2001) Probability Binning Comparison: A Metric for Quantitating Multivariate Distribution Differences, Cytometry 45, 47-55.
W. Rogers et. al. (2008) Cytometric Fingerprinting: Quantitative Characterization of Multivariate Distributions, Cytometry Part A 73, 430-441.
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