flowPeaks (version 1.14.0)

assign.flowPeaks: Obtain the flowPeaks cluster lables with the option of identifying outliers and applying to a new data set

Description

The function takes a flowPeaks output and a new data set (or could be the same dataset that generated the flowPeaks), and compute the cluster label assignment

Usage

assign.flowPeaks(fp,A,tol=0.01,fc=0.8)

Arguments

fp

an object of class flowPeaks, the output from the function flowPeaks or adjust.flowPeaks

A

A data matrix with the same number of columns as the data that geneterated fp

tol

All points where the probability density is less than tol (default is 1%) of the peak denisty of that cluster are labled as outliers. If tol is set 0, no outliers according to this rule. The details can be seen in the first equation of Section 2.5 in the flowPeaks manuscript (Ge et al 2012)

fc

All points where the classified cluster contributes less than fc (default is 80%) of overall denstiy are labeled as outliers. if fc is set to 0%, no outliers can be found according to this rule. The details can be seen in the second equation of Section 2.5 in the flowPeaks manuscript (Ge et al 2012)

Value

It returns the class label assignment of each data point, where -1 indicates outliers. When A is the same data that generated fp, If tol is 1 and fc is 0, the returned labels are the same as fp$peaks.cluster.

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

flowPeaks