Tree inference from mitochondrial mutations

mt-SCITE infers trees from a matrix of mutation probabilities built from a statistical model of alternate read counts in mitochondrial sequencing reads.

Installation

clang++ src/*.cpp -o mtscite

Usage

mtscite -i <mut_probabilities> -n <n_sites> -m <n_cells> -l <n_iters> -seed <random_seed>

End-to-end analysis

We also provide notebooks and scripts to perform an end-to-end tree inference analysis using mt-SCITE.

Environment

conda create -n mtSCITE python=3.10 conda activate mtSCITE conda install -c conda-forge biopython conda install -c anaconda pandas conda install -c anaconda scipy conda install -c conda-forge matplotlib conda install -c anaconda seaborn conda install -c anaconda graphviz pip install --global-option=build_ext --global-option="-I~/miniconda3/envs/mtSCITE/include" --global-option="-L~/miniconda3/envs/mtSCITE/include/lib/" pygraphviz conda install -c anaconda python-graphviz conda install -c anaconda pydot conda install -c anaconda networkx conda install -c anaconda numpy=1.22

Workflow

Raw sequencing data was processed by the snakemake pipeline in preprocessing_pipeline/Snakefile. This generated tsv files specifying the number of reads supporting the four different nucleotides A, C, G, T and total number of reads for each position in the mitochondrial genome for each sample.

Computing mutation probabilities

compute_mutations_probabilities.ipynb was run to generate mutation probability matrices for a range of error rates. The mutation probability matrices were stored in /path/to/matrices/

Selecting the error rate

We used a 3-fold cross validation procedure to select the error rate that we used to infer the trees using mt-SCITE. After generating probability matrices for different error rates and storing them in /path/to/matrices/, you can perform cross-validation by running this command: python scripts/cv.py </path/to/matrices/> </path/to/mtscite> This will create a CSV file named val_scores.csv with the 3-fold cross-validation likelihood scores.

learn_error_rate.ipynb was run to analyze val_scores.csv and to select the error rate to be used for tree building.

Build trees

Run mt-SCITE with this command: `</path/to/mtscite> -i pmat.csv -n <n_mutations> -m <n_samples> -r 1 -l 200000 -fd 0.0001 -ad 0.0001 -cc 0.0 -s -a -o </path/to/output/run_id>`

where pmat.csv is the mutation probability matrix generated with the learned error rate, n_mutations is the number of rows in pmat.csv and n_samples is the number of columns in pmat.csv.