EMU

EMU is a software for performing principal component analysis (PCA) in the presence of missingness for genetic datasets. EMU can handle both random and non-random missingness by modelling it directly through a truncated SVD approach. EMU uses binary PLINK files as input.

Citation

Please cite our paper in Bioinformatics: https://doi.org/10.1093/bioinformatics/btab027

Dependencies

The EMU software relies on the following two Python packages that you can install through conda (recommended) or pip:

• numpy
• cython

You can create an environment through conda easily as follows:

conda env create -f environment.yml

Install and build

git clone https://github.com/Rosemeis/emu.git
cd emu
pip3 install .

You can now run EMU with the emu command.

Usage

Running EMU

EMU works directly on PLINK files.

# See all options
emu -h

# Using PLINK files directly (test.bed, test.bim, test.fam) - Give prefix
emu --bfile test --n_eig 2 --threads 64 --out test.emu

Memory efficient implementation

A more memory efficient implementation has been added. It is based of the randomized SVD algorithm (Halko et al.) but using custom matrix multiplications that can handle decomposed matrices. Only factor matrices as well as the 2-bit data matrix is kept in memory.

# Example run using '-m' argument
emu --mem --bfile test -e 2 -t 64 -o test.emu.mem