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Dirichlet Process based methods for subclonal reconstruction of tumours

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DPClust R package

This R package contains methods for subclonal reconstruction through SNVs and/or CNAs from whole genome or whole exome sequencing data. The methods are originally described in The Life History of 21 Breast Cancers, see here for a recent write up.

Installation instructions

Install the latest release version from Github

R -q -e 'source("http://bioconductor.org/biocLite.R"); biocLite(c("optparse","KernSmooth","ks","lattice","ggplot2","gridExtra"))'
R -q -e 'devtools::install_github("OpenGenomics/dpclust")'

Alternatively, the software can be installed after downloading a release

R -q -e 'source("http://bioconductor.org/biocLite.R"); biocLite(c("optparse","KernSmooth","ks","lattice","ggplot2","gridExtra"))'
R -q -e 'install.packages([DPClust tarball], repos=NULL, type="source")'

Running DPClust

The DPClust package comes with an example pipeline and some simulated data in inst/example. Run the examples as follows:

# check out the repository
git clone [email protected]:OpenGenomics/dpclust.git
cd dpclust/inst/example

# single sample case
./run.sh 

Docker

Run DPClust on provided example data. After checking out this repository, build the image:

docker build -t quay.io/ohsugdanpipelines/dpclust .

Run DPClust as follows

docker run -it -v `pwd`:/mnt/output/ quay.io/ohsugdanpipelines/dpclust /opt/dpclust/example/run_docker.sh

Example data and pipelines

Example data is included with this package. See inst/extdata/simulated_data/simulated_1d.txt.

The DPClust input file should be generated via the DPClust pre-processing package (dpclust3p). Example pipelines to generate the DPClust input file (here and here).

DPClust input file

The DPClust input file requires these columns. See here for more details on the meaning and derivation of these values.

Column Description
chr Chromosome on which the mutation occurred
end Position at which the mutation occurred
WT.count The number of sequencing reads supporting the reference allele
mut.count The number of sequencing reads supporting the mutation allele
subclonal.CN The total copy number at the location of the mutation
mutation.copy.number The raw estimate of the average number of chromosome copies that carry the mutation
subclonal.fraction The estimate of the fraction of tumour cells (CCF) that carry the mutation
no.chrs.bearing.mut The mutation's multiplicity estimate

Output description

Single sample

DPClust creates the following output for a single sample case

Column Description
*_bestClusterInfo.txt Contains the mutation clusters found, for each cluster in each sample the proportion of tumour cells that the cluster represents (CCF) and the number of mutations assigned to the cluster
*_bestConsensusAssignments.bed Assignment of mutations to clusters, the cluster column refers to the cluster.no in the *_bestClusterInfo.txt file
*_mutationClusterLikelihoods.bed Likelihoods of each mutation belonging to each cluster, this file may contain likelihoods of cluster assignments to clusters that were considered but did not yield any mutations when performing a hard assignment. These clusters will not show up in *_bestClusterInfo.txt
*_bestConsensusResults.RData R data file with all the output
*_DirichletProcessplot_with_cluster_locations_2.png Main output figure showing the raw input data in the background, the posterior density of mutation clusters in purple (with blue confidence interval) and the called mutation clusters and the number of assigned mutations in the foreground
*_mutation_assignments.png Table figure showing the called mutation clusters

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Dirichlet Process based methods for subclonal reconstruction of tumours

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