fastmixture 0.93.2

Fast Ancestry Estimation

fastmixture (v0.93.1)

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

# Build and install via PyPI
pip install fastmixture

# or download source and install via pip
git clone https://github.com/Rosemeis/fastmixture.git
cd fastmixture
pip install .

# or download source and install in new Conda environment
git clone https://github.com/Rosemeis/fastmixture.git
conda env create -f environment.yml
conda activate fastmixture


# You can now run the program with the `fastmixture` command

Citation

Please cite our preprint 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
• --safety, only perform safety updates

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