DirichletProcessClustering: Main function to run subclonal reconstruction

View source: R/DirichletProcessClustering.R

DirichletProcessClusteringR Documentation

Main function to run subclonal reconstruction

Description

Will perform clustering using the given data. The method decides automatically whether the 1D or nD method is run based on the number of samples given at the input. The number of samples is determined through the number of columns of the input.

Usage

DirichletProcessClustering(mutCount, WTCount, totalCopyNumber,
  copyNumberAdjustment, mutation.copy.number, cellularity, output_folder,
  no.iters, no.iters.burn.in, subsamplesrun, samplename, conc_param,
  cluster_conc, mut.assignment.type, most.similar.mut, mutationTypes,
  max.considered.clusters)

Arguments

mutCount

Matrix with readcounts of the mutated allele

WTCount

Matrix with readcounts of the wild-type allele

totalCopyNumber

Matrix with total copynumber at each mutation locus

copyNumberAdjustment

Matrix with multiplicity values

mutation.copy.number

Matrix with mutation copy number values

cellularity

Vector with sample purities

output_folder

Directory where to write output

no.iters

The number of iterations to run the MCMC chain for

no.iters.burn.in

Number of iterations to discard as burn in

subsamplesrun

Samplenames of individual samples for this donor

samplename

Donor name, used in plots and to name output files

conc_param

Hyperparameter setting that affects the sampling of the alpha stick-breaking parameter

cluster_conc

Legacy parameter, no longer used

mut.assignment.type

Type of mutation assignment to be used

most.similar.mut

Vector with most similar mutation for mutations removed during sampling (if any)

mutationTypes

Vector with mutation types, used for plotting

max.considered.clusters

Maximum number of clusters to consider

Author(s)

sd11


Wedge-Oxford/dpclust documentation built on July 6, 2024, 2:02 p.m.