fitPiecewiseLinreg: Fit Piecewise Linear Regression for a list of flowMerge...
In flowMerge: Cluster Merging for Flow Cytometry Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Fits a two–component piecewise linear regression to the entropy vs number of clusters for a list of merged cluster solutions.

1	fitPiecewiseLinreg(x, plot=FALSE, normalized=TRUE, ...)

`x`	A "list" of `flowMerge` objects for `1` through `K` clusters derived from a single max BIC `flowObj` or `flowClust` object.
`plot`	A `logical` indicating whether to plot the fit or not. Default is FALSE.
`normalized`	A `logical` indicating whether the merging should be done using the normalized or unnormalized entropy
`...`	Additional arguments not currently used.

An S4 method that takes a list of flowMerge objects output by the merge method, extracts the entropy and fits a piecwise linear regression to the entropy vs number of clusters in order to find the postion of the changepoint. The location of the changepoint corresponds to the optimal merged cluster solution. The piecewise linear regression now is fitted to the entropy vs cumulative sum of merged observations at each number of clusters. This normalizes the change in entropy for the number of data points as described in Baudry et al.

An integer value corresponding to the position of the changepoint.

Greg Finak <greg.finak@ircm.qc.ca>

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)

merge

#data(rituximab)
#data(RituximabFlowClustFit)
#o<-flowObj(flowClust.res[[which.max(BIC(flowClust.res))]],rituximab);
#m<-merge(o)
#i<-fitPiecewiseLinreg(m);

flowMerge documentation built on Nov. 8, 2020, 8:08 p.m.