moved repo

README.md

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 # permGWAS
 Permutation based GWAS
+
+## Introduction
+tbd
+## Requirements
+
+To ensure a stable working environment, we recommend using [docker](https://www.docker.com). To follow this recommendation, 
+docker needs to be installed and running on your machine. We provide a Dockerfile based on CUDA 11.1 and Ubuntu 20.4.
+
+
+## Installation
+
+Clone the repository into the directory where you want to set up the project
+
+```shell
+git clone https://github.com/grimmlab/permGWAS.git
+```
+
+Navigate to `config` and build a Docker image using the provided Dockerfile
+
+```shell
+docker build -t IMAGENAME .
+```
+
+Run an interactive Docker container based on the created image.\
+You have to mount the directory where the repository is located on your machine in the Docker container. 
+If you want to work on GPU, specify the GPUs to mount.
+
+```shell
+docker run -it -v PATH/TO/REPO/FOLDER:NAME/OF/DIRECTORY/IN/CONTAINER --gpus=all --name CONTAINERNAME IMAGENAME
+```
+
+
+## Execution
+
+In the Docker container, navigate to the repository in the mounted directory.
+
+Run the script with the test data provided
+
+```shell
+python3 run_permgwas.py -x ./data/x_matrix.h5 -y ./data/y_matrix.csv 
+```
+
+
+## Input data
+The minimal requirement is to provide a genotype and a phenotype file. We provide test data in the folder `data`.
+permGWAS is designed to work with several genotype file formats:
+
+### .hdf5/.h5/.h5py
+The file has to contain the following keys:
+
+- snps: genotype matrix, 012 encoded
+- sample_ids: vector containing corresponding sample ids
+- position_index: vector containing the positions of all SNPs
+- chr_index: vector containing the corresponding chromosome number
+
+```shell
+python3 run_permgwas.py -x ./data/x_matrix.h5 -y ./data/y_matrix.csv 
+```
+
+### .csv
+The first column should be the sample ids. The column names should be the SNP identifiers in the form "CHR_POSITION"
+(e.g. Chr1_657). The values should be the genotype matrix in additive encoding. 
+
+```shell
+python3 run_permgwas.py -x ./data/x_matrix.csv -y ./data/y_matrix.csv 
+```
+
+### PLINK
+To use PLINK data, a .map and .ped file with the same prefix need to be in the same folder. 
+To run permGWAS with PLINK files, you can use PREFIX.map or PREFIX.ped as option for the genotype file.
+
+```shell
+tbd
+```
+
+### binary PLINK
+To use binary PLINK data, a .bed, .bim and .fam file with the same prefix need to be in the same folder. 
+To run permGWAS with binary PLINK files, you can use PREFIX.bed, PREFIX.bim or PREFIX.fam as option for the genotype file.
+
+```shell
+tbd
+```
+
+### phenotype file 
+permGWAS currently only accepts .csv files for the phenotype. Here the first column should contain the sample ids with 
+"accession_id" as column name. 
+The remaining columns should contain the phenotype values with the phenotype name as column name.
+
+
+### kinship file
+Per default permGWAS computes the realized relationship kernel as kinship matrix. 
+It is also possible to provide a kinship matrix. Currently, permGWAS only accepts .csv files as kinship file, where
+the first column contains the sample ids. The sample ids need to match those of the genotype matrix.
+
+```shell
+python3 run_permgwas.py -x ./data/x_matrix.h5 -y ./data/y_matrix.csv --k ./data/k_matrix.csv
+```
+
+### covariates file
+It is possible to run permGWAS with covariates. If no covariates file is provided, only the intercept will be used as 
+fixed effect. Currently, permGWAS only accepts .csv files for covariates. Here the first column should contain the 
+sample ids with "accession_id" as column name. The sample ids must match those of the phenotype file.
+
+```shell
+python3 run_permgwas.py -x ./data/x_matrix.h5 -y ./data/y_matrix.csv --cov ./data/cov_matrix.csv
+```
+
+### Options
+|||
+|---|---|
+|-x (-x_file, -genotype) |   absolute or relative path to genotype file |
+|-y (-y_file, -phenotype) |  absolute or relative path to phenotype file |
+|--y_name (--pt_name)|       name of phenotype (column) to be used in phenotype file, optional, default is "phenotype_value"|
+|--k (--k_file, --kinship) | absolute or relative path to kinship file, optional|
+|--cov (--cov_file)|         absolute or relative path to covariates file, optional|
+|--maf|                      minor allele frequency threshold as percentage value, optional, default is 0|
+|--perm|                     number of permutations to be performed, optional, default is 0|
+|--out_dir|                  name of the directory result-files should be stored in, optional, if not provided, files will be stored in folder "results" in current directory|
+|--out_file|                NAME of result files, will be stored as NAME_p_values and NAME_min_p_values, optional, if not provided name of phenotype will be used|
+|--device|                   GPU device to be used, optional, default is 0|
+|--batch|                    number of SNPs to work on simultaneously, optional, default is 10000|
+|--plot|                     creates manhattan plot, optional|
+
+
+
+## Citation
+
+tbd

config/Dockerfile

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+FROM nvidia/cuda:11.1-base-ubuntu20.04
+RUN apt update && apt install -y python3.8 && apt install -y python3-pip
+RUN apt install -y vim
+RUN apt install -y git
+RUN mkdir /configfiles
+COPY requirements.txt /configfiles
+RUN pip3 install -r /configfiles/requirements.txt
+RUN pip3 install torch==1.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

config/requirements.txt

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+glimix-core
+h5py
+matplotlib
+numpy
+pandas
+pandas-plink
+scipy
+seaborn
+