flowPeaks {flowPeaks} | R Documentation |
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
flowPeaks(x,tol=0.1,h0=1,h=1.5)
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 |
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:
|
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 |
Yongchao Ge yongchao.ge@gmail.com
Ge Y. et al, flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding, 2012, Bioinformatics 8(15):2052-8
##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)