Complete refactor of EMU. emu v1.0

README.md

@@ -1,26 +1,24 @@
 # 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 PLINK files as input.
+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 Python packages that you can install through conda (recommended) or pip:
+The EMU software relies on the following two Python packages that you can install through conda (recommended) or pip:
 
 - numpy
 - cython
-- scipy
 
 You can create an environment through conda easily as follows:
-```
+```bash
 conda env create -f environment.yml
 ```
 
 ## Install and build
 ```bash
 git clone https://github.com/Rosemeis/emu.git
 cd emu
-python setup.py build_ext --inplace
 pip3 install -e .
 ```
 
@@ -34,19 +32,12 @@ EMU works directly on PLINK files.
 emu -h
 
 # Using PLINK files directly (test.bed, test.bim, test.fam) - Give prefix
-emu -p test -e 2 -t 64 -o test.emu
-```
-
-### EM Acceleration (Default - Recommended)
-An acceleration scheme is being used with both SVD options (*halko* or *arpack*). Each iteration will be longer as 2 extra steps are performed but the overall number of iterations for convergence is decreased significantly. However, the EM acceleration can be turned off.
-```bash
-# No acceleration
-emu -p test -e 2 -t 64 -o test.emu.no_accel --no_accel
+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.](https://arxiv.org/abs/0909.4061)) 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.
 ```bash
 # Example run using '-m' argument
-emu -m -p test -e 2 -t 64 -o test.emu.mem
+emu --mem --bfile test -e 2 -t 64 -o test.emu.mem
 ```