kMeansBin: Bin sample using K-means binning

Description Arguments Details Value Examples

Description

Bin sample using K-means binning

Arguments

object

flowSample to bin

n.bins=128

number of bins to use. This should be a power of 2, and will be rounded down to the nearest power of 2 if not.

n.neighbours=1

number of neighbours to use for KNN mapping of bins from clustered tube

snow.cluster=NULL

Optional snow cluster to use for parallel execution.

random.seed=101

Random seed to set to make K-means clustering deterministic.

dequantize=T

If TRUE, adds a small (region of 1e-8) value to flow data to help break ties when binning.

Details

Runs K-means clustering on the binning markers in the first tube of the data set. These clusters are then mapped to the other tubes using K-nearest neighbours.

Value

a BinnedFlowSample

Examples

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data(amlsample)
normed.sample <- quantileNormalise(aml.sample)
res <- kMeansBin(normed.sample)

Example output

Loading required package: flowCore
Loading required package: flowFP
Loading required package: flowViz
Loading required package: lattice

flowBin documentation built on Nov. 17, 2017, 10:37 a.m.