kMeansBin: Bin sample using K-means binning

In flowBin: Combining multitube flow cytometry data by binning

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

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. 8, 2020, 8:25 p.m.