fastmixture 0.94.6

Fast Ancestry Estimation

fastmixture (v0.94.6)

fastmixture is a new software for estimating ancestry proportions in unrelated individuals. It is significantly faster than previous model-based software while providing accurate and robust ancestry estimates.

Table of Contents

• Installation
• Citation
• Usage
• License
• Authors and Acknowledgements

Installation

To run the fastmixture software, you have a few options depending on your environment and preference:

1. Installing fastmixture via PyPI or Source Code

   # Option 1: Build and install via PyPI
   pip install fastmixture
   
   # Option 2: Download source and install via pip
   git clone https://github.com/Rosemeis/fastmixture.git
   cd fastmixture
   pip install .
   
   # Option 3: Download source and install in a new Conda environment
   git clone https://github.com/Rosemeis/fastmixture.git
   conda env create -f fastmixture/environment.yml
   conda activate fastmixture
   

   You can now run the program with the fastmixture command. For more details on running it, see the Usage section.

2. Using the fastmixture docker image with Docker or Apptainer

   If you prefer or need to use a containerized setup (especially useful in HPC environments), a pre-built fastmixture container image is available on Docker Hub. The latest version corresponds to v0.94.3.

   A. Using Docker

   1. Pull the image from Docker Hub

   # Docker command
   docker pull albarema/fastmixture
   

   2. Run the fastmixture container

   # Mount the directory containing the PLINK files using --volume flag (e.g. `pwd`/project-data/) 
   # Indicate the cpus available for the container to run
   # e.g. data prefix is 'toy.data' and results prefix is 'toy.fast'
   docker run --cpus=8 -v `pwd`/project-data/:/data/ albarema/fastmixture fastmixture --bfile data/toy.data --K 3 --out data/toy.fast --threads 8
   

   B. Using Apptainer (formerly Singularity)

   For Apptainer/Singularity users, please take a look at your HPC system's documentation for guidance. Apptainer will create the .sif image in your current working directory (pwd) by default. You will later use this image to run the software. If needed, specify a different directory and filename to store the image. Remember to bind the directories where the data is stored (--bind).

   1. Pull fastmixture container image into a .sif file that Apptainer can use

   # Singularity/Apptainer
   apptainer pull <fastmixture.sif> docker://albarema/fastmixture
   

   2. Run fastmixture container

   # Singularity/Apptainer
   apptainer run <fastmixture.sif> fastmixture --bfile data/toy.data --K 3 --out data/toy.fast --threads 8
   

Citation

Please cite our paper in Peer Community Journal.

Preprint also available on BioRxiv.

Usage

fastmixture requires input data in binary PLINK format.

• Choose the value of K that best fits your data. We recommend performing principal component analysis (PCA) first as an exploratory analysis before running fastmixture.
• Use multiple seeds for your analysis to ensure robust and reliable results (e.g. ≥ 5).

# Using binary PLINK files for K=3
fastmixture --bfile data --K 3 --threads 32 --seed 1 --out test

# Outputs Q and P files (test.K3.s1.Q and test.K3.s1.P)

Supervised

A supervised mode is available in fastmixture using --supervised. Provide a file of population assignments for individuals as integers in a single column file. Unknown or admixed individuals must be given a value of '0'.

# Using binary PLINK files for K=3
fastmixture --bfile data --K 3 --threads 32 --seed 1 --out super.test --supervised data.labels

# Outputs Q and P files (super.K3.s1.Q and super.K3.s1.P)

Extra options

• --iter, specify maximum number of iterations for EM algorithm (1000)
• --tole, specify tolerance for convergence in EM algorithm (0.5)
• --batches, specify number of initial mini-batches (32)
• --check, specify number of iterations performed before convergence check (5)
• --power, specify number of power iterations in SVD (11)
• --chunk, number of SNPs to process at a time in randomized SVD (8192)
• --als-iter, specify maximum number of iterations in ALS procedure (1000)
• --als-tole, specify tolerance for convergence in ALS procedure (1e-4)
• --no-freqs, do not save ancestral allele frequencies (P-matrix)
• --random-init, random parameter initialization instead of SVD

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details

Authors and Acknowledgements

• Jonas Meisner, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen
• Cindy Santander, Computational and RNA Biology, University of Copenhagen
• Alba Refoyo Martinez, Center for Health Data Science, University of Copenhagen