An implementation to the spatial clustering algorithm to calculate clustering of variants over cDNA. This algorithm was featured in the publication "Spatial Clustering of De Novo Missense Mutations Identifies Candidate Neurodevelopmental Disorder-Associated Genes"
If you make use of this software for academic purposes, please cite the article 10.1016/j.ajhg.2017.08.004.
Please ensure you have the following software installed on your machine:
docker
You can get docker here
Run the following command in the root directory of this project
docker build -t spatial_clustering .
Example for a hypothetical gene
docker run --rm -v $(pwd):/app --name spatial_clustering_container spatial_clustering python /app/spatial_clustering/spatial_clustering.py --gene_name=TESTGENE --variant_cDNA_locations=1,2,3,4,5,5,5,10,1000 --cDNA_length=1337 --n_permutations=10 --parallel=True --random_seed=1 --correction=1
This should result in the following output:
Computing spatial clustering for gene: TESTGENE
cDNA_length: 1337
variant_cDNA_locations: ['1', '2', '3', '4', '5', '5', '5', '10', '1000']
Settings: random_seed: 1, parallel: True, n_permutations: 10
Results:
gene: TESTGENE, with n variants: 9
geometric_mean: 6.898887607777424955370112958229102e-08
corrected p-value: 0.09090909090909091 (Bonferroni correction = 1)
The arguments '--n_permutations', '--parallel', --random_seed, and, --correction are optional and need not be included.
For further information on arguments, please refer to the help file:
-h, --help show this help message and exit
--gene_name GENE_NAME
(Required) Name of the gene of interest, example
usage: --gene_name=BRCA1
--variant_cDNA_locations VARIANT_CDNA_LOCATIONS
(Required) cDNA based variant locations, example
usage: --variant_cDNA_locations=10,50,50,123
--cDNA_length CDNA_LENGTH
(Required) total cDNA length of the gene (including
stop codon), example usage: --cDNA_length=1337
--n_permutations N_PERMUTATIONS
(Optional) total nunber of permutations,
default=100000000 (1.00E+08), example usage:
--n_permutations=100
--parallel PARALLEL (Optional) should the algorithm make use of parallel
computation?, default=True, example usage:
--parallel=True
--random_seed RANDOM_SEED
(Optional) The seed used for initialization of the
random permutations, default=1, example usage:
--random_seed=1
--correction CORRECTION
(Optional) The number of genes the p-value must be
corrected for in a Bonferonni manner, default=1,
example usage: --correction=1
The code implemented here is tested in a Python 3.5.x environment and should work for Python 2.7.x. To install the virtual environment, please follow the following steps:
After cloning this repository, ensure that you are in the project's directory.
Create virtual environment for the project
Python 2.7.x: > mkvirtualenv --no-site-packages SpatialClustering_env
Python 3.5.x: > pyvenv SpatialClustering_env
Activate virtual environment for installing packages
Python 2.7.x: > workon SpatialClustering_env
Python 3.5.x: > source SpatialClustering_env/bin/activate
Install required packages for current project
Python 2.7.x: > pip install -r requirements.txt
Python 3.5.x: > pip install -r requirements.txt
Exit the virtual environment
Python 2.7.x: > deactivate
Python 3.5.x: > deactivate
All dependencies should now be installed in the virtual environment.
Exampe for a hypothetical gene
Python 2.7.x: > workon SpatialClustering_env
Python 2.7.x: (SpatialClustering_env) > python spatial_clustering/spatial_clustering.py --gene_name=TESTGENE --variant_cDNA_locations=1,2,3,4,5,5,5,10,1000 --cDNA_length=1337 --n_permutations=10 --parallel=True --random_seed=1 --correction=1
Python 2.7.x: (SpatialClustering_env) > deactivate
~
Python 3.5.x: > SpatialClustering_env/bin/python spatial_clustering/spatial_clustering.py --gene_name=TESTGENE --variant_cDNA_locations=1,2,3,4,5,5,5,10,1000 --cDNA_length=1337 --n_permutations=10 --parallel=True --random_seed=1 --correction=1
This should result in the following output:
Computing spatial clustering for gene: TESTGENE
cDNA_length: 1337
variant_cDNA_locations: ['1', '2', '3', '4', '5', '5', '5', '10', '1000']
Settings: random_seed: 1, parallel: True, n_permutations: 10
Results:
gene: TESTGENE, with n variants: 9
geometric_mean: 6.898887607777424e-08
corrected p-value: 0.09090909090909091 (Bonferroni correction = 1)
The arguments '--n_permutations', '--parallel', --random_seed, and, --correction are optional and need not be included.
For further information on arguments, please refer to the help file:
-h, --help show this help message and exit
--gene_name GENE_NAME
(Required) Name of the gene of interest, example
usage: --gene_name=BRCA1
--variant_cDNA_locations VARIANT_CDNA_LOCATIONS
(Required) cDNA based variant locations, example
usage: --variant_cDNA_locations=10,50,50,123
--cDNA_length CDNA_LENGTH
(Required) total cDNA length of the gene (including
stop codon), example usage: --cDNA_length=1337
--n_permutations N_PERMUTATIONS
(Optional) total nunber of permutations,
default=100000000 (1.00E+08), example usage:
--n_permutations=100
--parallel PARALLEL (Optional) should the algorithm make use of parallel
computation?, default=True, example usage:
--parallel=True
--random_seed RANDOM_SEED
(Optional) The seed used for initialization of the
random permutations, default=1, example usage:
--random_seed=1
--correction CORRECTION
(Optional) The number of genes the p-value must be
corrected for in a Bonferonni manner, default=1,
example usage: --correction=1