Here is an example run from the command line on simulated data. Please see reference.md for details on the arguments to the SCICoNE binaries. This tutorial assumes that you have already installed the C++ program as described in the README. All the files generated from this run are available at example_data/.
Step 1: First we generate the data:
./build/simulation --n_cells=200 --n_nodes=5 --n_regions=10 --n_bins=1000 --n_reads=10000 --nu=10.0 --min_reg_size=10 --max_regions_per_node=1 --ploidy=2 --postfix=test --seed=42
Step 2: Then we detect potential breakpoints from the cells by bins counts matrix:
./build/breakpoint_detection --d_matrix_file=5nodes_10regions_10000reads_test_d_mat.csv --n_bins=1000 --n_cells=200 --window_size=30 --threshold=6.0 --postfix=test
This command creates files containing the region stops and their sizes.
Step 3: To proceed with copy number calling and tree inference, we need to use the detected breakpoints to produce a cells by regions counts matrix. We can use a utility Python script for this:
python ./scripts/segment_counts.py 5nodes_10regions_10000reads_test_d_mat.csv test_segmented_region_sizes.txt
Step 4: We can now use the output file to infer a copy number event tree:
./build/inference --d_matrix_file=5nodes_10regions_10000reads_test_d_mat_segmented_counts.txt --region_sizes_file=test_segmented_region_sizes.txt --n_regions=8 --n_cells=200 --ploidy=2 --copy_number_limit=2 --n_iters=4000 --seed=42 --postfix=test
This will create a file containing the tree in an in-house format. To visualise the tree, convert it to graphviz and generate the figure:
python ./scripts/convert_to_graphviz.py test_tree_inferred.txt
dot -Tpdf test_tree_inferred.graphviz -o test_tree_inferred.pdf
Often it is a good idea to first cluster the data and find an initial tree describing those clusters. We start by clustering the segmented data obtained from Step 3. This can be done any way you like, but we provide a script to run PhenoGraph clustering (which you must install to before running the script):
python ./scripts/run_phenograph.py 5nodes_10regions_10000reads_test_d_mat_segmented_counts.txt
This outputs a file containing the cluster assignments for each cell. We use this to condense the clusters and get their sizes:
python ./scripts/condense_clusters.py 5nodes_10regions_10000reads_test_d_mat_segmented_counts.txt 5nodes_10regions_10000reads_test_d_mat_segmented_counts_cluster_assignments.txt
We are now ready to run the tree inference on the condensed data. The command is similar to the one in Step 4, except that now we use the --cluster_sizes_file parameter as well and we adjust --n_cells to the number of clusters:
./build/inference --d_matrix_file=5nodes_10regions_10000reads_test_d_mat_segmented_counts_condensed.txt --region_sizes_file=test_segmented_region_sizes.txt --cluster_sizes_file=5nodes_10regions_10000reads_test_d_mat_segmented_counts_cluster_sizes.txt --n_regions=8 --n_cells=6 --ploidy=2 --copy_number_limit=2 --n_iters=4000 --seed=42 --postfix=cluster
We can again visualise this tree:
python ./scripts/convert_to_graphviz.py cluster_tree_inferred.txt
dot -Tpdf cluster_tree_inferred.graphviz -o cluster_tree_inferred.pdf