Description Arguments Details Value Examples

Bin sample using K-means binning

`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. |

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.

a `BinnedFlowSample`

1 2 3 | ```
data(amlsample)
normed.sample <- quantileNormalise(aml.sample)
res <- kMeansBin(normed.sample)
``` |

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

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