plinkformatter 0.1.83

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PLINKFORMATTER

plinkformatter transforms genotype and phenotype inputs into PLINK-compatible artifacts for downstream linear mixed-model workflows (primarily PyLMM).

This repository is based on the original R workflow implemented in:

• plinkformatter/IGNORE_misc/pyLMM_utils.R
• tests/IGNORE_MISC/hao_v2/pyLMM_analysis_NonDO.R

The Python implementation keeps the same core workflow while improving maintainability, testability, and performance for large PED/MAP datasets.

Prerequisites

• Python 3.8+
• Poetry
• PLINK 2.0 (required for PLINK integration tests and full pipeline runs)

Install dependencies:

poetry install

If plink2 is not on your PATH, set PLINK2_PATH explicitly.

PowerShell example:

$env:PLINK2_PATH = "C:\path\to\plink2.exe"

Running Tests

Run all commands from the repository root.

1) Fast test pass (no external PLINK dependency)

poetry run pytest -q tests/test_generate_pheno_plink_fast.py

2) PLINK utility tests

These include PLINK-facing behavior and may require a working plink2 binary.

poetry run pytest -q tests/test_plink_utils.py

3) Full test suite

poetry run pytest -q tests

Test output flags

• -q: quiet output (compact summary)
• -s: show stdout/stderr (print, logs written to console)

Example:

poetry run pytest -s -q tests/test_generate_pheno_plink_fast.py

Workflow Parity with R

The Python pipeline mirrors the same logical stages as the R scripts:

1. Extract/normalize phenotype rows for selected measure IDs.
2. Generate per-measure, sex-specific .ped/.map/.pheno files.
3. Build .bed/.bim/.fam with PLINK.
4. Align .pheno ordering to .fam and recompute rank-Z on retained samples.
5. Compute kinship (PyLMM3 or PLINK-based path).

Performance Design (Large PED Files)

A major difference from older dataframe-heavy patterns is how PED is handled in generate_pheno_plink_fast.py:

• It does not load the full PED into a pandas DataFrame.
• It builds a compact byte-offset index (strain -> file position) once.
• It seeks directly to needed PED rows and writes outputs in a streaming manner.

This avoids high memory usage and scales better for large genotype files than pandas.read_csv() on full PED content.

Publishing to PyPI

1. Update version:

poetry version patch

2. Build:

poetry build

3. Configure repository and token:

poetry config repositories.pypi https://upload.pypi.org/legacy/
poetry config pypi-token.pypi pypi-YourActualTokenHere

4. Publish:

poetry publish