continuous integration of association summary statistics for network analysis
Project description
cimr
cimr is not yet released for public use
continuous integration and analysis of complex traits
YoSon Park
Useful links: Source repository | Issues & Ideas | Documentation | cimr-d
cimr (continuously integrated meta-resource) is a convenience tool for continuous analyses of variant-based association results from GWAS (genome-wide association studies), eQTL (expression-quantitative trait loci mapping) or other association studies. cimr aims to streamline the pre-analysis processing steps, provide standardized input files and automate scripting for standard downstream analyses.
Installation
Installing python
cimr requires python :math: ge 3.6. Installation of data analysis bundles such as miniconda or anaconda are recommended and will install all python packages cimr depends on. However, all required python packages can be downloaded and installed with setup.py or requirements.txt provided here.
Installing git lfs
cimr-d and some functionalities in cimr may use git large file storage (LFS) . See how to install git .
To install git-lfs on Ubuntu, run:
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash sudo apt-get install -y git git-lfs git-lfs install
Alternatively, you can install git-lfs through conda:
conda install -c conda-forge git-lfs && git lfs install
Installing cimr
You can use pip to install the latest stable release of cimr:
pip3 install cimr
If you want to try out the nightly build of cimr at your own risk, clone the repository from git:
git clone https://github.com/greenelab/cimr.git cd cimr pip3 install -r requirements.txt python3 setup.py build python3 setup.py install
Analysis examples
Quality assurance and processing of association summary statistics files
You can use cimr to standardize public datasets using a yaml file, e.g.:
# example.yaml
data_file:
description: >-
Global Lipid Genetics Consortium GWAS results for high-density
cholesterol levels
location:
url: https://zenodo.org/record/3338180/files/HDL_Cholesterol.txt.gz
md5: 2b28816a0a363db1a09ad9a6ba1a6620
columns:
variant_id: panel_variant_id
variant_chrom: chromosome
variant_pos: position
rsnum: variant_id
data_info:
citation: 10.1038/ng.2797
data_source: http://lipidgenetics.org/
data_type: gwas
context: hdl cholesterol
build: b38
sample_size: 187167
n_cases: na
can_be_public: true
method:
name: linear regression
tool: PLINK;SNPTEST;EMMAX;Merlin;GENABEL;MMAP
website: >-
http://zzz.bwh.harvard.edu/plink/download.shtml;
https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html;
https://genome.sph.umich.edu/wiki/EMMAX;
https://csg.sph.umich.edu/abecasis/Merlin/tour/assoc.html;
http://www.genabel.org/sites/default/files/html_for_import/GenABEL_tutorial_html/GenABEL-tutorial.html;
https://mmap.github.io/
contributor:
name: Contributor Name
github: contributorgithub
email: contributoremail@emaildomain.emailextension
Details can be found in the cimr-d contributions.md.
Once the yaml file is prepared, you can run cimr locally:
cimr processor -process -yaml-file example.yaml
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