AdmixPy

Fast Python implementation of ADMIXTOOLS-style f-statistics, qpAdm, and qpWave.

Fast f-statistics, qpAdm, and qpWave in Python.

Installation

Requires Python 3.10 or newer.

Install the latest release from PyPI:

python -m pip install admixpy

Check that the package imports:

python -c "import admixpy; print(admixpy.__version__)"

Installing from source

Create and activate a virtual environment:

python3 -m venv venv
source venv/bin/activate

Install the package from pyproject.toml:

python -m pip install --upgrade pip
python -m pip install -e .

Updating an existing clone

cd /path/to/admixpy
git pull --ff-only

After updating, run the two installation commands in Setup again.

Alternative

If you only want to install the dependencies without installing the package:

python -m pip install -r requirements.txt

Examples

Supported input layouts include EIGENSTRAT text, packed AncestryMap, TGENO, and SNP-major PLINK binary files (.bed/.bim/.fam). For .geno/.snp/.ind inputs, the genotype layout is detected from the file header/size; TGENO can also be provided as .tgeno/.snp/.ind.

Basic API

The main convenience wrappers are:

admixpy.f2(data, pop1=None, pop2=None, *, unique_only=True,
           resampling="pairwise_counts", **kwargs)
admixpy.fst(data, pop1=None, pop2=None, *, unique_only=True,
            resampling="pairwise_counts", fst_aggregation="block_ratios",
            **kwargs)
admixpy.f3(data, pop1=None, pop2=None, pop3=None, *, unique_only=True,
           resampling="pairwise_counts", verbose=True, **kwargs)
admixpy.f4(data, pop1, pop2=None, pop3=None, pop4=None, *, comb=True,
           unique_only=True, afprod=False, verbose=True, **kwargs)
admixpy.qpwave(data, left, right, ranks=None, left_base=None,
               right_base=None, rcond=1e-10, diag=0.0, max_nfev=None,
               verbose=True, **kwargs)
admixpy.qpadm(data, target, left=None, right=None, sources=None,
              fudge=0.0001, fudge_twice=False, iterations=20, getcov=True,
              return_f4=False, return_stats=False, return_cov=False,
              verbose=True, **kwargs)

The main convenience wrappers are f2, fst, f3, f4, qpwave, and qpadm.

data can be a supported genotype dataset prefix or precomputed f2 data. Population arguments can be strings or lists where the wrapper supports multiple combinations. For PLINK .bed/.bim/.fam input, population labels are read from the FID column of the .fam file.

For direct genotype input, f3, f4, qpwave, and qpadm default to allsnps=True, matching the ADMIXTOOLS1-style behavior of estimating each statistic from its available SNPs. For precomputed f2 input, allsnps defaults to False and the standard f2-based behavior is used. Pass allsnps=False to restrict direct-genotype models to SNPs shared across the required populations.

Direct genotype f3 also defaults to allsnps=True and is calculated per SNP. By default, its corrected numerator is divided by unbiased target heterozygosity. Set outgroupmode=True to return the unnormalized f3 numerator; that raw mode is directly comparable to f2-derived f3 and to original qp3Pop outgroup mode after removing the latter's factor of 1000.

Direct f3 and f4 genotype calculations (including the f4 calculations for qpAdm and qpWave) read the genotype file once by default and hold the complete SNP-by-population allele-frequency and count tables in memory. For datasets that do not fit comfortably in RAM, set stream=True to use two bounded-memory passes with 250,000 SNPs per chunk by default. The chunk size can be adjusted with chunk_size.

Lower-level helpers are also exported for direct use, including allele-frequency conversion (anygeno_to_afs, eigenstrat_to_afs, plink_to_afs, packedancestrymap_to_afs, tgeno_to_afs), f2 block IO and access (get_f2, read_f2, write_f2), and block/statistical utilities such as iter_geno_to_afs, f3_stats_from_geno, block_covariance, jackknife_cov, stats_to_loo, and est_to_loo.

SNP selection, missingness, and small samples

By default, f2 excludes SNPs with identical allele frequencies in every loaded population, while fst retains them. This matches the ADMIXTOOLS default but means the two statistics can use different SNP sets. Use poly_only=True to both calls when they should be directly comparable.

AdmixPy uses resampling="pairwise_counts" as default for data with missing genotypes: each population pair is weighted by the SNP observations actually available for that pair. Pairwise f2 and fst result tables include n. Set resampling="nominal_blocks" to reproduce the older behavior in which every pair uses nominal block sizes. Raw-genotype f4 with allsnps=True already uses per-statistic counts on a common SNP intersection. Cached pairwise f3/f4 instead defines a pairwise-available estimator and cannot reconstruct that common intersection.

FST cache files additionally retain numerator and denominator sums. The default fst_aggregation="block_ratios" averages stored block estimates. Set fst_aggregation="pooled_components" to recompute full-data and leave-one-block-out FST as ratios of pooled numerator and denominator sums.

Bias-corrected f2 and FST require at least two independent allele observations in each population. SNP values with a count below two are excluded with a warning when apply_corr=True. Setting apply_corr=False explicitly requests the finite but sampling-biased raw estimate; the Hudson FST denominator remains (p1-p2)^2 + p1(1-p1) + p2(1-p2) in either mode.

Cache files without real per-pair SNP counts are rejected and must be rebuilt.

Run an f4 statistic from a supported genotype dataset prefix:

import admixpy

prefix = "/path/to/dataset_prefix"

result = admixpy.f4(
    prefix,
    "Mbuti",
    "Germany_ViesenhaeuserHof_EN",
    "Sardinian",
    "French",
)

print(result)

qpAdm can be run the same way from a Python REPL:

>>> import admixpy as a
>>> prefix = "/path/to/dataset_prefix"
>>> target = "Sardinian"
>>> left = ["Turkey_N", "Russia_Samara_EBA_Yamnaya", "Luxembourg_Loschbour_Mesolithic", "Iran_GanjDareh_N"]
>>> right = ["Chimp", "Turkey_Epipaleolithic", "Georgia_KotiasKlde_Mesolithic", "Russia_Vologda_Mesolithic", "Switzerland_Epipaleolithic", "Iran_BeltCave_Mesolithic"]
>>> res = a.qpadm(prefix, target=target, left=left, right=right)
>>> res
QpAdmResult(target='Sardinian')

weights:
                           left weight     se     z
                       Turkey_N  0.686  0.013 52.45
      Russia_Samara_EBA_Yamnaya  0.102  0.012  8.54
Luxembourg_Loschbour_Mesolithic  0.119 0.0064 18.57
               Iran_GanjDareh_N  0.094  0.013  7.11

rankdrop:
f4rank dof   chisq         p  p_nested
     3   2    0.53     0.769 9.39e-242
     2   6 1123.16 2.03e-239         0
     1  12 3317.58         0         0
     0  20 6849.91         0       NaN

popdrop:
 pat                                                                    dropped f4rank dof   chisq         p  feasible status
0000                                                                                 3   2    0.53     0.769      True   PASS
0001                                                           Iran_GanjDareh_N      2   3   58.58  1.18e-12      True   FAIL
0010                                            Luxembourg_Loschbour_Mesolithic      2   3   370.2  6.29e-80     False   FAIL
0100                                                  Russia_Samara_EBA_Yamnaya      2   3   73.09  9.29e-16      True   FAIL
1000                                                                   Turkey_N      2   3  825.23 1.46e-178     False   FAIL
 ...

Citation

AdmixPy implements methods from Patterson et al. (2012) and Maier et al. (2023).

License

MIT License .