fastmixture2

fastmixture2 estimates ancestry proportions (Q) and ancestral allele frequencies (P) by maximum likelihood. It reads SNP-major PLINK genotypes or ANGSD Beagle genotype likelihoods. Unsupervised, supervised and projection modes run on CPU or, with --gpu, on NVIDIA CUDA.

Installation

Use Python 3.11 or newer. For best CPU performance, build from source. This uses -march=native to tune the code to the build machine and can be faster than portable wheels.

Install the current GitHub version:

git clone https://github.com/MeisnerLab/fastmixture2.git
cd fastmixture2
python -m pip install .

Source builds need a C compiler with OpenMP. On macOS, install OpenMP with brew install libomp. On HPC, build on the compute-node type used for your analyses.

With Conda, the CPU and GPU environments retain native CPU tuning. Run from the cloned repository:

conda env create --file environment-cpu.yml
conda activate fastmixture2-cpu

For GPU use, select environment-gpu.yml and activate fastmixture2-gpu.

PyPI wheels cover Linux (glibc) and macOS 15 or newer, on x86-64 and ARM64. They use AVX2 on supported x86-64 CPUs and NEON on ARM64, including Apple Silicon:

python -m pip install fastmixture2

To build from PyPI source with native CPU tuning:

python -m pip install --no-cache-dir --no-binary=fastmixture2 fastmixture2

With uv:

uv venv
source .venv/bin/activate
uv pip install fastmixture2

Use uv pip install . from the cloned repository for native CPU tuning. To build from source without tying the binaries to the build CPU:

FASTMIXTURE2_PORTABLE=1 python -m pip install .

For NVIDIA CUDA 13, install "fastmixture2[gpu-cuda13]" from PyPI or '.[gpu-cuda13]' from source with pip or uv. A compatible NVIDIA driver is required. Add --gpu when running an analysis.

Quick start

Run an unsupervised fit with five ancestry components:

fastmixture2 --bfile cohort --K 5 --threads 8 --out cohort
fastmixture2 --beagle cohort.beagle.gz --K 5 --threads 8 --out cohort.gl

• --bfile takes one or more ordered prefixes of SNP-major PLINK .bed, .bim and .fam files. Genotypes stay packed on CPU and GPU. Missing genotypes are supported.
• --beagle takes a gzipped ANGSD Beagle file. Likelihood triplets are normalized internally. Exactly uniform triplets contribute no ancestry information.

Per-chromosome inputs are memory-mapped and fitted jointly without a merged BED:

fastmixture2 --bfile chr{1..22} --K 5 --threads 8 --out cohort

The shell expands chr{1..22} in chromosome order. Explicit prefixes also work, such as --bfile chr1 chr2 chr3. All FAM files must have identical FID/IID pairs in the same order. Marker rows, including annotated P output, follow the supplied file order. Per-chromosome input works on CPU and CUDA in all modes.

Perform sample and variant QC first. For each K, try several seeds and keep the converged fit with the highest log-likelihood. Component labels may change between runs, so align them before comparing Q or P.

Add --random-init to draw Q and P randomly instead of using SVD/ALS or pooled frequencies. It uses --seed and works with either input format on CPU or CUDA in unsupervised mode. With --exact-cv, each fold also starts randomly. Resuming a checkpoint retains its saved estimates.

Add --gpu to use CUDA with either input format:

fastmixture2 --bfile cohort --K 5 --threads 8 --gpu --out cohort.gpu

CPU and CUDA support 1 <= K <= 128 for both input formats in all modes. CUDA keeps input data on the GPU when they fit and streams in blocks otherwise. Small CPU/GPU differences can arise from floating-point arithmetic.

At K > 1, unsupervised fits use SVD/ALS on up to 50,000 genome-wide SNPs, with subset refinement for larger panels. All modes use safeguarded SQP/ZAL with EM/QN rescue and float64 likelihood checks. CPU fitting uses float64. CUDA uses mixed precision followed by float64 refinement.

Processing uses bounded tiles. Total memory still grows with panel size and K. Temporary files use TMPDIR. For large panels, point it to disk-backed scratch with sufficient space.

The defaults are --threads 1, --seed 42, --iter 1000 and --tole 1e-10. --threads also controls CPU work during GPU fits. The optimizer scales the tolerance by observed genotypes or informative likelihood triplets. --iter and --tole apply to each refinement stage. Run fastmixture2 --help for the full CLI.

Beagle cache

A cache avoids decompressing the Beagle file again for each K or seed:

fastmixture2 --beagle cohort.beagle.gz --K 5 --threads 8 \
  --gl-cache cohort.gl.npy --out cohort.gl

The cache stores three normalized float32 likelihoods per sample and variant, using about 12 × variants × samples bytes. An invalid or outdated cache is rebuilt. The cache is optional.

Supervised estimation

Use known reference assignments to fix their Q rows while estimating P and the Q rows of unassigned samples:

fastmixture2 --bfile cohort --K 5 --threads 8 \
  --supervised populations.txt --out cohort.supervised

Supply one label per sample in input order: 1 through K for reference populations, or 0 for an unassigned sample. At least one reference sample is required. Include IDs so the program can check sample order:

FID IID POP
family1 sample1 1
family2 sample2 0

A one-column label file also works, but sample order cannot then be checked. For Beagle input, use each sample label as both FID and IID.

Projection

Given reference allele frequencies, estimate Q for new samples while keeping P fixed:

fastmixture2 --bfile new_samples --K 5 --threads 8 \
  --projection reference.P --out new_samples.projected

An annotated P matrix lets the program check marker order and alleles:

SNP A1 A2 POP1 POP2 POP3 POP4 POP5
rs1 A G 0.02 0.08 0.15 0.04 0.10

Marker IDs and order must match. Swapped and strand-complemented alleles are handled automatically. A/T and C/G variants are rejected by default. Use --allow-ambiguous only when both panels share a strand convention. Plain numeric P matrices also work, but their marker and allele alignment cannot be checked. Use --write-info when creating an annotated reference P matrix.

Supervision and projection also accept --beagle and --gpu.

Cross-validation

To compare K values, use the same seed and number of folds. Lower scores are better:

fastmixture2 --bfile cohort --K 5 --threads 8 --seed 42 --cv 5 --out cohort.cv

CV works with either input format in unsupervised mode. By default, each fold starts from the full-data fit, so held-out entries have influenced its starting point. Use --exact-cv to initialize each fold from its training entries.

Use --K 1 as a pooled-frequency baseline for CV verification. Q is all ones, and initialization skips SVD/ALS.

PLINK CV reports one-half binomial deviance. Beagle CV withholds complete triplets and reports mean held-out negative log marginal likelihood over nonuniform triplets. Missing genotypes are excluded from PLINK CV. The log identifies folds that did not converge. Adjust fold tolerance with --cv-tole. With --gpu, refits run on CUDA. Exact CV initialization remains on CPU.

Bootstrap uncertainty

Estimate Q standard errors by resampling blocks of neighboring SNPs:

fastmixture2 --bfile cohort --K 5 --threads 8 \
  --bootstrap 200 --bootstrap-block 1500 --out cohort

--bootstrap defaults to 200 replicates. Each replicate starts from the full-data fit and takes up to three refinement iterations. Use --bootstrap-iter for longer refits. Add --bootstrap-tole to stop at a mean likelihood gain per informative entry, within that iteration limit. The log reports refit termination. Short refits give a local estimate of uncertainty.

For PLINK input, blocks stay within chromosomes. Without --bootstrap-block, block lengths average 10 cM using BIM genetic-map spans. Set a block length in SNPs when map positions are absent or zero. Beagle input always requires --bootstrap-block and treats the marker stream as one segment.

Add --gpu for CUDA refits. Projection errors are conditional on the supplied P matrix. Supervised reference samples have zero Q uncertainty. Bootstrap and CV must be run separately.

Restarts

Try additional unsupervised fits with --restarts N (default 0):

fastmixture2 --bfile cohort --K 5 --threads 8 --restarts 6 --out cohort

Each round tries a Q-column permutation and a component merge/split mutation, followed by full-data refits. Panels above 50,000 SNPs use fresh subsets. For K >= 3, N rounds add up to 2N full-data fits. Outputs and checkpoints retain the best full-data fit.

Restarts support both input formats on CPU and CUDA, use --seed, and keep the same --iter and --tole per stage. K = 2 uses only Q permutations. At K = 1 they have no effect.

Checkpointing

Save accepted estimates and resume an interrupted CPU or GPU fit:

fastmixture2 --bfile cohort --K 5 --threads 8 --checkpoint run.ckpt --out cohort
fastmixture2 --bfile cohort --K 5 --threads 8 --resume run.ckpt --out cohort

Keep the same input, supervision and projection files when resuming. With --restarts, checkpoints retain the best full-data fit. Resuming runs the requested number of additional restart rounds after the resumed fit.

Output

For --out cohort --K 5 --seed 42:

Rows follow input order and columns correspond to components. Use --write-info for headers and sample or variant IDs, or --no-freqs to omit P. Projection does not write P. For PLINK, P is the frequency of BIM A1. For Beagle, it is the frequency of allele2.

Matrices use ten decimal places. Logs retain full-precision likelihoods and iteration counts, including ALS when used.

Outputs, checkpoints and caches use standard file permissions controlled by your umask.

The log distinguishes convergence, iteration limits, stalled optimization and numerical failure. Unsuccessful fits retain the best accepted parameters.

Parallel jobs should use distinct output prefixes. Build each likelihood cache with one job, then reuse it across fits, or use a separate cache path per job.

Authors and license

Lisa Thunegard and Jonas Meisner. Developed at MeisnerLab and distributed under the GNU General Public License v3.0.