flowObj-class: Class "flowObj"

In RGLab/flowMerge: Cluster Merging for Flow Cytometry Data

Description

A class inheriting from flowClust that groups the model and data in a single object.

Objects from the Class

Objects can be created by calls of the form new("flowObj", ...). Has a convenience method flowObj(flowClustObj, flowFrameObj) for creating instances of the class.

Slots

DATA:

An "environment" that holds a pointer to the flowFrame data in position [[1]].

expName:

As described in the flowClust documentation

varNames:

As described in the flowClust documentation

K:

As described in the flowClust documentation

w:

As described in the flowClust documentation

mu:

As described in the flowClust documentation

sigma:

As described in the flowClust documentation

lambda:

As described in the flowClust documentation

nu:

As described in the flowClust documentation

z:

As described in the flowClust documentation

u:

As described in the flowClust documentation

label:

As described in the flowClust documentation

uncertainty:

As described in the flowClust documentation

ruleOutliers:

As described in the flowClust documentation

flagOutliers:

As described in the flowClust documentation

rm.min:

As described in the flowClust documentation

rm.max:

As described in the flowClust documentation

logLike:

As described in the flowClust documentation

BIC:

As described in the flowClust documentation

ICL:

As described in the flowClust documentation

Extends

Class "flowClust", directly.

Methods

getData

signature(obj = "flowObj"): Retreives the contents of the DATA environment

merge

signature(x = "flowObj", y = "missing"): the flowMerge algorithm is called via this function on objects of type flowObj.

plot

signature(x = "flowObj", y = "missing"): A simplified plotting method. Does not require specification of the data since it is contained in the flowObj object. Takes most of the same parameters as plot.flowClust, except the data parameter

Author(s)

Greg Finak <[e-mail]>, Raphael Gottardo <[e-mail]>

References

Finak G, Bashasharti A, Brinkmann R, Gottardo R. Merging Mixture Model Components for Improved Cell Population Identification in High Throughput Flow Cytometry Data; Advances in Bioinformatics (To Appear)

See Also

flowMerge-class, flowObj

RGLab/flowMerge documentation built on Aug. 10, 2020, 12:52 p.m.