flowPeaks (version 1.14.0)

flowPeaks: Doing the flowPeaks analysis

Description

This is the core function in the flowPeaks package. It generates the output of the cluster and information associated with each cluster, which can be used by the function plot for visualization

Usage

flowPeaks(x,tol=0.1,h0=1,h=1.5)

Arguments

x

a data matrix for the flow cytometry data, it needs to have at least two rows, and the names for each column should be unique. For a flowFrame data, its exprssion matrix slot should be used as x, where only channles of interest are selected (see the example below).

tol

The tolerance (between 0 and 1) when neighboring clusters should be considered to be merged

h0

The multiplier of the vaiarance matrix S0

h

The multiplier of the variance matrix S

Value

It returns an object of class flowPeaks, which is a list of the following variables:

peaks.cluster

An integer shows the cluster labels (between 1 and K for K clusters) for each cell. The clustering is based on the flowPeaks algorithm

peaks

A summary of the cluster information. It is a list with the following three variables:

• cid: cluster labels, should always be 1:K;
• w: the weights of the K clusters;
• mu: The mean of all cells in the K clusters;
• S: The variance matrix of the K clusters. Note that each variance matrix for each cluster has been stacked as a column vector

kmeans.cluster

An integer shows the cluster labels for the initial kmeans clustering

kmeans

A summary of the initial kmeans clustering. The meaning of the variables can be seens in the description of peaks above

info

The information that can be used for plot, and how the initial kmeans clustering and the final flowPeaks clustering are connected

x

The input data x

References

Ge Y. et al, flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding, 2012, Bioinformatics, in press.

See Also

plot.flowPeaks

Examples

##demonstrate how to use a flowFrame
## Not run: 
# require(flowCore)
# samp <- read.FCS(system.file("extdata","0877408774.B08",
# package="flowCore"))
# ##do the clustering based on the asinh transforamtion of
# ##the first two FL channels
# fp<-flowPeaks(asinh(samp@exprs[,3:4]))
# plot(fp)
# ## End(Not run)

data(barcode)
fp<-flowPeaks(barcode[,c(1,3)])
plot(fp)

##to compare it with the gold standard
evalCluster(barcode.cid,fp$peaks.cluster,method="Vmeasure")

#to remove the outliers
fpc<-assign.flowPeaks(fp,fp$x)
plot(fp,classlab=fpc,drawboundary=FALSE,
  drawvor=FALSE,drawkmeans=FALSE,drawlab=TRUE)


#to adjust the cluster by increasing the tol,h0, h, which results
#in a smaller number of clusters
fp2<-adjust.flowPeaks(fp,tol=0.5,h0=2,h=2) 
summary(fp2)
print(fp) #an alternative of using summary(fp)