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.
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")'
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 git@github.com:OpenGenomics/dpclust.git
cd dpclust/inst/example
# single sample case
./run.sh
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 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).
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 |
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 |