flowMerge-package(flowMerge)
flowMerge-package()所属R语言包：flowMerge
Merging of mixture components for automated gating of flow cytometry data.
混合成分的合并流式单元仪数据的自动门。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Merges mixture components from the flowClust framework based on the entropy of clustering and provides a simple representation of complicated, non-convex cell populations.
合并混合flowClust基于熵聚类的框架组件，并提供了一个简单的表现形式复杂，非凸的单元群。
Details
详情----------Details----------
High density, non-convex cell populations in flow cytometry data often require multiple mixture components for a good model fit. The components are often overlapping, resulting in a complicated representation of individual cell populations. flowMerge merges overlapping mixture components (based on the max BIC flowClust model fit) in an iterative manner based on an entropy criterion, allowing these cell populations to be represented by individual mixture components while retaining the good model fitting properties of the BIC solution. Estimates of the number of clusters from a flowMerge model more accurately represent the "true" number of cell populations in the data. Running flowMerge is relatively straightforward. A flowClust object is converted to a flowObj object, which groups the model and the data (a flowFrame) into a single object. This is done by a call to flowObj(model, data) with a call to merge, which takes a flowObj object. The algorithm may be run in parallel on a multi-core machine or a networked cluster of machines. It uses the functionality in the snow package to achieve this. Parallelized calls to flowClust are available via the pFlowClust and pFlowMerge functions.
高密度，非凸的单元群，流式单元仪数据往往需要一个很好的模式适合多种混合成分。组件往往重叠，造成复杂的个别单元群表示。 flowMerge合并重叠的混合组件（基于最大的BICflowClust模型拟合）基于熵准则的迭代方式，使这些单元群被个别混合成分的代表，同时保持良好的模型拟合特性的BIC解决方案。从flowMerge模型的聚类数量的估计更准确地代表的“真实”数据中的单元群的数量。运行flowMerge的是相对比较简单。一个flowClust对象被转换为flowObj对象，哪个组的模型和数据到一个单一的对象（flowFrame）。这是通过与flowObj(model, data)，这需要一个merge对象的调用调用flowObj。该算法可并行运行在多核机器或机器的网络聚类。它使用snow包的功能，实现这一目标。 flowClust并行调用通过pFlowClust和pFlowMerge功能。
flowMerge has functionality to automatically select the "correct" number of clusters by fitting a piecewise linear model to the entropy of clustering vs number of clusters, and locating the position of the changepoint. The piecewise linear model fitting is invoked by a call to fitPiecewiseLinreg, which returns the location of the changepoint.
flowMerge具有的功能，自动选择“正确”的聚类，聚类与聚类数的熵，通过分段线性模型拟合和定位变点的位置。分段线性模型拟合，被调用通过调用的fitPiecewiseLinreg，它返回的变点的位置。
作者（S）----------Author(s)----------
Greg Finak <a href="mailto:<greg.finak@ircm.qc.ca>"><greg.finak@ircm.qc.ca></a>, Raphael Gottardo <a href="mailto:<raphael.gottardo@ircm.qc.ca>"><raphael.gottardo@ircm.qc.ca></a>
Maintainer: Greg Finak <a href="mailto:<greg.finak@ircm.qc.ca>"><greg.finak@ircm.qc.ca></a>
参考文献----------References----------
<h3>See Also</h3> <code>flowClust,flowObj,pFlowMerge,pFlowClust,fitPiecewiseLinreg,merge,getData,link{plot}</code>
举例----------Examples----------
#data(rituximab)[数据（美罗华）]
#data(RituximabFlowClustFit)[数据（RituximabFlowClustFit）]
#o<-flowObj(flowClust.res[[which.max(flowMerge:::BIC(flowClust.res))]],rituximab);[Ø<flowObj的（flowClust.res [which.max（flowMerge :::（BIC flowClust.res）的）]，美罗华）;]
#m<-merge(o);[M <合并（O）]
#i<-fitPiecewiseLinreg(m);[我<fitPiecewiseLinreg（M）;]
#m<-m[[i]];[M <-M [我];]
#plot(m,pch=20,level=0.9);[图（M = 20，= 0.9级，PCH）;]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-2-25 17:56:49
