Startle is a set of methods for lineage tree reconstruction that attempt to infer the most parsimonious tree under the star homoplasy evolutionary model.
If you found the tool useful in your research, please cite us at:
@article{sashittal2023startle,
title={Startle: a star homoplasy approach for crispr-cas9 lineage tracing},
author={Sashittal, Palash and Schmidt, Henri and Chan, Michelle and Raphael, Benjamin J},
journal={Cell Systems},
volume={14},
number={12},
pages={1113--1121},
year={2023},
publisher={Elsevier}
}
startle can be installed automatically using Conda.
Simply execute,
$ conda install -c schmidt73 startle
and Startle will be available as startle.
startle is implemented in C++ and is packaged with the dependencies
needed to execute the program. In particular, the only dependencies are
a recent version of CMAKE and a modern C++17 compliant compiler.
To build startle from source, first clone the repository and its submodules:
$ git clone --recurse-submodules https://github.com/raphael-group/startle.git
Then from the root of the project directory, execute the following sequence of commands:
$ mkdir build; cd build
$ cmake ..
$ make
The output binary will be located at build/src/startle.
To run startle, simply execute the binary.
$ startle
Usage: startle [--help] [--version] {large,small}
Optional arguments:
-h, --help shows help message and exits
-v, --version prints version information and exits
Subcommands:
large Solves the star homoplasy LARGE parsimony problem.
small Solves the star homoplasy SMALL parsimony problem.
The tool contains algorithms for the star homoplasy
SMALL and LARGE parsimony problems, available using the
sub-commands small and large.
Both algorithms implemented in startle require several files. In particular,
startle requires a character_matrix file as a CSV, a mutation_prior
file as a CSV, and a tree as Newick file. An example character matrix and mutation prior file
can be found in examples under the names examples/n100_m30_d0.2_s0_p0.2_character_matrix.csv and
examples/n100_m30_d0.2_s0_p0.2_mutation_prior.csv.
For the small parsimony algorithm, we compute the maximum parsimony score of the input tree under the
star homoplasy model. For the large parsimony algorithm, the tree is the seed to start the stochastic search in startle.
The seed tree can be obtained from any algorithm, but for convenience, we have attached a script script/nj.py that generates
a seed tree using neighbor joining and a character_matrix.
As an example, we can run startle by first generating a seed tree
and then running the startle command.
$ python scripts/nj.py examples/n100_m30_d0.2_s0_p0.2_character_matrix.csv --output examples/n100_m30_d0.2_s0_p0.2_seed_tree.newick
$ startle large examples/n100_m30_d0.2_s0_p0.2_character_matrix.csv examples/n100_m30_d0.2_s0_p0.2_mutation_prior.csv \
examples/n100_m30_d0.2_s0_p0.2_seed_tree.newick \
--output examples/n100_m30_d0.2_s0_p0.2_startle
startle-ILP is a command-line tool for inferring lineage trees from cell-by-state matrices. It takes as input a character-matrix and a set of estimated mutation probabilities for each character-state pair and outputs a inferred tree in Newick format.
To install startle-ILP you will need the following set of libraries and programming languages available on your device.
Requirements:
After ensuring the aforementioned libraries are properly installed,
run the following command to build the startle integer linear programming
solver - the output file will be named startle and will be located under
the build/ directory. You will need to ensure that
the library and include paths (specified by the -D flags) are properly set
for your own system.
$ mkdir build; cd build
$ cmake -DLIBLEMON_ROOT=/n/fs/ragr-data/users/palash/libraries/lemon\
-DCPLEX_INC_DIR=/n/fs/ragr-code/general/ILOG/CPLEX_Studio128/cplex/include\
-DCPLEX_LIB_DIR=/n/fs/ragr-code/general/ILOG/CPLEX_Studio128/cplex/lib/x86-64_linux/static_pic\
-DCONCERT_INC_DIR=/n/fs/ragr-code/general/ILOG/CPLEX_Studio128/concert/include\
-DCONCERT_LIB_DIR=/n/fs/ragr-code/general/ILOG/CPLEX_Studio128/concert/lib/x86-64_linux/static_pic\
..
$ make
To install the required Python dependencies, we recommend you create a new conda environment for this project.
$ conda create -n startle
$ conda install python=3.8
$ conda install -c conda-forge biopython
$ conda install -c conda-forge funcy loguru tqdm networkx pandas numpy seaborn
After ensuring the startle executable is located in the build/ directory and
that the required python dependencies are installed, you are ready to run the
startle-ILP. The input to this tool is a character matrix in CSV format and
a set of estimated mutation probabilities in CSV format. There are several
command line options to control the number of threads, the time limit, and
the optimality gap of the ILP.
$ perl scripts/startle-ilp.pl --help
usage: Infer lineage trees using Startle-ILP solver. --output|-o
--mutation-priors|-m --character-matrix|-c [--help|-h] [--mip-gap] [--threads]
[--time-limit]
required named arguments:
--output, -o OUTPUT Output directory name.
--mutation-priors, -m MUTATION-PRIORS Input mutation prior probabilities as CSV.
--character-matrix, -c CHARACTER-MATRIX Input character matrix as CSV.
optional named arguments:
--help, -h ? show this help message and exit
--mip-gap MIP-GAP ? Optimality gap for ILP solver.
Default: 0.05
--threads THREADS ? Number of threads for ILP solver.
Default: 8
--time-limit TIME-LIMIT ? Time limit for ILP solver.
Default: 7200
For reference, an example character matrix and mutation prior file generated via
simulation are included in the repository. As a sanity check, ensure that this example
runs. The example lineage tree was built for 100 cells and 30 characters with dropout set
to 20%. The ground truth tree is also included for comparison across methods.
$ perl scripts/startle_ilp.pl -c examples/n100_m30_d0.2_s0_p0.2_character_matrix.csv\
-m examples/n100_m30_d0.2_s0_p0.2_mutation_prior.csv\
-o example_output
