`DPClustering.dpclustering`

— Method`dpclustering(y::Array{Real, 1}, N::Array{Real, 1}; <keyword arguments>)`

Perform dirichlet clustering on the variant allele frequency distribution of cancer sequencing data and find the number of clusters that the data supports, y is a vector of the number of reads reporting each mutant, N is the total depth at each locus.

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**Arguments**

`iterations = 1000`

: number of iterations of the gibbs samples`C = 30`

: Max number of clusters to consider`burninstart = round(Int64, iterations/2)`

: Burn in of the gibbs samples`bw = 0.01`

: Bandwidth of density estimation`maxxaxis = 0.7`

:`cutoffweight = 0.05`

: Minimum weight to be called a cluster`verbose = true`

: Show progress of gibbs sampling with`ProgressMeter`

package`A = 0.01`

: Hyperparameter for α, see Nik-Zainal et al`B = 0.01`

: Hyperparameter for α, see Nik-Zainal et al

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`DPClustering.plotresults`

— Method`plotresults(dp; <keyword arguments>)`

Plot results from DPClustering object. Will plot histogram of raw data with density estimates from Gibbs sampling. ...

**Arguments**

`save = false`

: Set to`true`

if you want the plot to be saved`dir = ""`

: Directory where the plot will be saved to. Default is the current working directory.`plotname = "DPclustering"`

: Name to call plot when saving.

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