genepi 1.0.3

A package for detecting epsitasis by machine learning

GenEpi

GenEpi is a package to uncover epistasis associated with phenotypes by machine learning approach, developed by Yu-Chuan Chang at c4Lab of National Taiwan University.

The architecture and modules of GenEpi.

Getting Started

Installation

$ pip install GenEpi

NOTE: GenEpi is a memory-consuming package, which might cause memory errors when calculating the epistasis of a gene containing a large number of SNPs. We recommend that the memory for running GenEpi should be over 256 GB.

Inputs

1. Genotype Data GenEpi takes Genotype File Format (.GEN) used by Oxford statistical genetics tools, such as IMPUTE2 and SNPTEST as input format for genotype data. If your files is in PLINK text format (.PED and .MAP), you could use GTOOL with following command to convert .PED files to .GEN file.

$ gtool -P --ped example.ped --map example.map --og out.gen --os out.sample

2. Phenotype & Environmental Factor Data GenEpi takes .csv file without header line as input format for phenotype and environmental factor data. The last column of the file would be considered as phenotype data and the others would be considered as covariate (environmental factor data).

NOTE: The order of the phenotype data should be same as .GEN file.

Usage example

Running a test

We provided an example script in example folder. Please use following command for running a quick test.

$ python example.py

Applying on your data

You may use this example script as a recipe and modify the input file names in Line 14 and 15 for running your data.

str_inputFileName_genotype = "../sample.gen" # full path of the .GEN file.
str_inputFileName_phenotype = "../sample.csv" # full path of the .csv file.

Options

For changing the build of USCS genome browser, please modify parameter of step one:

genepi.DownloadUCSCDB(str_hgbuild="hg38") # for example: change to build hg38.

You could modify the threshold for Linkage Disequilibrium dimension reduction in step two:

#default float_threshold_DPrime=0.9 and float_threshold_RSquare=0.9
genepi.EstimateLDBlock(str_inputFileName_genotype, float_threshold_DPrime=0.8, float_threshold_RSquare=0.8)

Meta

Chester (Yu-Chuan Chang) - [e-mail]
Distributed under the MIT license. See LICENSE for more information.
https://github.com/Chester75321/GenEpi/