First: Clone this repository
git clone https://github.com/broadinstitute/PhylogicNDT.git
cd PhylogicNDT
Then either :
Install python 2.7, R (optional) and required packages For debian:
apt-get install python-pip build-essential python-dev r-base r-base-dev git graphviz libgraphviz-dev
Install setuptools and wheel
pip install setuptools wheel
Install required packages
pip install -r req
Install scipy, matplotlib, and pandas (these versions are recommended)
pip install pandas==0.19.2 scipy==1.0.0 matplotlib==2.0.0
pip install -e git+https://github.com/rmcgibbo/logsumexp.git#egg=sselogsumexp (for faster compute)
Install docker from https://www.docker.com/community-edition#/download
docker build --tag phylogicndt .
./PhylogicNDT.py --help
If running from the docker, first run:
docker run -i -t phylogicndt
cd phylogicndt
To run clustering on the provided sample input data:
To specify inputs:
./PhylogicNDT.py Cluster -i Patient_ID -s Sample1_id:Sample1_maf:Sample1_CN_seg:Sample1_Purity:Sample1_Timepoint -s Sample2_id:Sample2_maf:Sample2_CN_seg:Sample2_Purity:Sample2_Timepoint ... SampleN_info
alternatively - provide a tsv sample_information_file (.sif)
with headers: sample_id maf_fn seg_fn purity timepoint
./PhylogicNDT.py Cluster -i Patient_ID -sif Patient.sif
the .maf should contain pre-computed raw ccf histograms based on mutations alt/ref count
(Absolute annotated mafs or .Rdata files are also supported)
if the ccf histograms are absent - the --maf_input_type flag must be set to
calc_ccf and sample purity must be provided. Also local copy number must
be attached to each mutation in the maf with columns named local_cn_a1 and local_cn_a2
CN_seg is optional to annotate copy-number information on the trees
To specify number of iterations:
./PhylogicNDT.py Cluster -ni 1000
Acknowledgment: Clustering Module is partially inspired (primary 1D clustering) by earlier work of Carter & Getz (Landau D, Carter S , Stojanov P et al. Cell 152, 714–726, 2013)
The GrowthKinetics module fully incorporates the BuildTree libraries, so when rates are desired, there is no need to run both.
- The -w flag should provide a measure of tumor burden, with one value per input sample maf in clustering. When ommited, stable tumor burden is assumed.
- The -t flag should provide relative time for spacing the samples. When omitted, equal spacing is assumed.
Just BuildTree
./PhylogicNDT.py BuildTree -i Indiv_ID -sif Patient.sif -m mutation_ccf_file -c cluster_ccf_file
GrowthKinetics
./PhylogicNDT.py GrowthKinetics -i Indiv_ID -sif Patient.sif -ab cell_population_abundance_mcmc_trace -w 10 10 10 10 10 -t 1 2 3 4 5
Run Cluster together with BuildTree
./PhylogicNDT.py Cluster -i Patient_ID -sif Patient.sif -rb
SinglePatientTiming requires a maf input and a seg file input for each sample. The maf file should be the output of PhylogicNDT Clustering module. The seg file should have the following columns:
Chromosome Start End A1.Seg.CN A2.Seg.CN
To run SinglePatientTiming:
./PhylogicNDT.py Timing -i Indiv_ID -sif Patient.sif
LeagueModel requires an input of comparison tables. The comparison tables should be the output of SinglePatientTiming ending in ".comp.tsv"
To run LeagueModel:
./PhylogicNDT.py LeagueModel -cohort Cohort -comps comp1 comp2 ... compN
Alternatively, one can use a single aggregated table. The table should have the following columns:
sample event1 event2 p_event1_win p_event2_win unknown
To run with the aggregated table:
./PhylogicNDT.py LeagueModel -cohort Cohort -comparison_cn comps
A simulation module is provided for convenience.
./PhylogicNDT.py PhylogicSim --help
Command to visualize all the options and help.
./PhylogicNDT.py PhylogicSim
Run the simulation with the default paramters.
./PhylogicNDT.py PhylogicSim -i MySimulation
Specify a prefix for all the output files
./PhylogicNDT.py PhylogicSim -i MySimulation -ns 7
Specify the number of samples you want to simulate.
./PhylogicNDT.py PhylogicSim -i MySimulation -nodes 5
Specify the number of distinct clones present in your samples. Minimum 2 (The first clone is always the clonal clone)
./PhylogicNDT.py PhylogicSim -i MySimulation -nodes 5 -seg /Example_SegFile.txt
Specify a segment file with copy number values to sample from. See the "Example_SegFile.txt" for a format example. If no file is specified, a build-in CN profile is used, based on the hg19 contigs.
./PhylogicNDT.py PhylogicSim -i MySimulation -nodes 5 -clust_file /Example_Clust_File.txt
Force the ccf values of each cluster on each sample, instead of generating a new random phylogeny from scratch. If -clust_file is specified, the -ns and -nodes flags are ignored an instead replaced with the values from the Clust_File. Each line of the tsv file represents a sample, with each tab separated value the ccf of a cluster. The last value of each line must always be -1 to account for the artifact cluster.
./PhylogicNDT.py PhylogicSim -i MySimulation -nodes 5 -clust_file /Example_Clust_File.txt -a 0.3
Specify the proportion of mutations that are artifactual (Random af unrelated to mutation/CN). Can be combined with a clust_file.
./PhylogicNDT.py PhylogicSim -i MySimulation -nodes 5 -clust_file /Example_Clust_File.txt -pfile /Example_PurityFile.txt
TSV file to specify the purity of each sample individualy (Otherwise, the purity is specified for all the samples using the -p flag.). Each line represents a sample. The file can optionally contain an extra three columns with the alpha, beta and N values for the coverage betabinomial for each sample (Otherwise, those values are set for all samples using the -ap, -b and -nb flags respectively).