The following package implements the method described in Clonality inference in multiple tumor samples using phylogeny

Citup estimates the clone phylogeny and clonal genotypes for deep sequencing of SNVs in multiple tumour biopsies. The input are cellular frequencies of mutations as estimated from the deep sequencing data. The method infers an evolutionary tree, and an assignment of mutations to nodes in the tree.

The supported installation method for citup is using conda. If you do not already use conda, we recommend installing anaconda or miniconda.

You will need to add my conda channel using:

conda config --add channels https://conda.anaconda.org/dranew

Cplex is required to run citup. For licensing reasons this could not be included in my public conda repo. A license is required and is free for academic use. The recommended installation procedure involves building and installing cplex using conda. A conda build recipe for cplex is provided.

With cplex installed you should be able to type:

conda install citup

To run the iterative version of citup, provide a table of frequencies. The input format is tab/whitespace separated, with each row representing the frequency of a mutation and each column is a tumour sample. No header is required.

For example, the following would be input for 3 mutations in 2 samples.

0.2 0.1
0.4 0.3
0.5 0.1

Given mutation frequences freq.txt, run citup iter using the following command.

run_citup_iter.py freq.txt results.h5

The above command will run citup iter and produce results in pandas hdf5 format in results.h5.

For additional options run run_citup_iter.py -h.

To run the QIP version of citup, provide a table of mutation frequencies, and a table of mutation clusters. The input format for mutation frequencies is described above. The mutation clusters is a single line per mutation, containing a 0 based integer cluster index for that mutation. For example, the following specifies 3 mutations, the first two in the same cluster the last in a different cluster.

0
0
1

Given mutation frequences freq.txt and mutation clusters clusters.txt run citup QIP using the following command.

run_citup_qip.py freq.txt clusters.txt results.h5

The above command will run citup iter and produce results in pandas hdf5 format in results.h5.

For additional options run run_citup_qip.py -h.

The output of citup is pandas hdf5 format.

The results store contains the following pandas series, with an entry per tree solution:

• /results/bic
• /results/error_rate
• /results/likelihood
• /results/num_mutations
• /results/num_nodes
• /results/num_samples
• /results/objective_value
• /results/optimal
• /results/tree_id
• /results/tree_index
• /results/tree_string

The store also contains the following pandas series, with one series per tree solution:

• /trees/{tree_solution}/cluster_assignment
• /trees/{tree_solution}/objective_value

Finally, the store contains the following pandas dataframes, with one frame per tree solution:

• /trees/{tree_solution}/adjacency_list
• /trees/{tree_solution}/clade_freq
• /trees/{tree_solution}/clone_freq
• /trees/{tree_solution}/gamma_matrix