## Quick Start Example ## ---------- ### Prerequisites ### - Completion of all the steps in the [Installation][1] guide. - [SigProfilerSimulator][2] - [SigProfilerMatrixGenerator][3] reference genome download - GRCh37 ### Example Data Download ### [BRCA_example_subs.vcf][5] <br> or [BRCA_example_indels.vcf][7] <br> or [MELA_example.vcf][6] <br> After downloading the example breast cancer or melanoma sample, place the file in a new project directory. These samples and their expected outputs are also found under the examples/ directory within the GitHub repository (https://github.com/AlexandrovLab/SigProfilerClusters). Note: The simulations folder is not included to reduce the memory size of the GitHub repo. ### Running SigProfilerClusters ### First, start a Python interactive shell and import [SigProfilerMatrixGenerator][3], [SigProfilerSimulator][2], and SigProfilerClusters libraries. $ python3 >>> import from SigProfilerMatrixGenerator import install as genInstall >>> from SigProfilerSimulator import SigProfilerSimulator as sigSim >>> from SigProfilerClusters import SigProfilerClusters as hp #### Run SigProfilerSimulator #### Then, run [SigProfilerSimulator][2] to generate 100 possible simulations of intermutational distances from the given data. >>> sigSim.SigProfilerSimulator("BRCA", "path/to/data", "GRCh37", contexts = ['288'], chrom_based=True, simulations=100) #### Run SigProfilerClusters #### Finally, run SigProfilerClusters to cluster mutations based on the simulated distribution. >>> hp.analysis("BRCA", "GRCh37", "96", ["288"], "path/to/data", analysis="all", sortSims=True, subClassify=True, correction=True, calculateIMD=True, max_cpu=4, TCGA=True, sanger=False) **Breakdown of output will be found on the [Output][4] page or within the examples/ directory on the GitHub repository (https://github.com/AlexandrovLab/SigProfilerClusters).** [1]: https://osf.io/qpmzw/wiki/Installation/ [2]: https://osf.io/usxjz/wiki/home/ [3]: https://osf.io/s93d5/wiki/home/ [4]: https://osf.io/qpmzw/wiki/4.%20Output/ [5]: https://osf.io/gy2bh/ [6]: https://osf.io/a8bq5/ [7]: https://osf.io/tyw3u/