directlfq-rs 1.0.0

Rust-backed directLFQ quantification for DIA-NN report.parquet and generic TSV inputs

directlfq_rs

directlfq_rs is a Python package that exposes a Rust implementation of the core directLFQ protein quantification workflow.

This repository is a mixed Rust/Python project with a split architecture:

• the PyO3 binding crate and Python packaging live at the repository root
• the Rust quantification core lives in crates/directlfq-core

It is intended for fast label-free quantification from:

• DIA-NN report.parquet files
• Generic wide TSV matrices with protein, ion, and sample columns

The package currently focuses on the programmatic API rather than the full feature surface of the original Python directlfq package.

Features

• Native Rust execution for the main LFQ pipeline
• Direct DIA-NN report.parquet input support
• Generic TSV fallback for preformatted inputs
• Optional pandas DataFrame convenience wrapper
• Packaged type stubs for IDE signature and type rendering

Repository Layout

directlfq-rs/
├── pyproject.toml
├── Cargo.toml                 # PyO3 binding crate + workspace root
├── directlfq_rs/              # Python package
├── src/                       # PyO3 Rust bindings
├── tests/                     # Python-side tests
└── crates/
    └── directlfq-core/        # Rust quantification core crate

Installation

Install from PyPI:

pip install directlfq_rs

If you install from a source distribution, a Rust toolchain is required during build.

Python Version Support

The package is built with PyO3 abi3-py310, so one wheel per platform supports CPython 3.10 and newer Python 3 releases. The release workflow currently smoke tests Python 3.10, 3.11, 3.12, 3.13, and 3.14.

Release Builds

GitHub Actions builds release artifacts for:

• Linux x86_64 and aarch64
• macOS universal2
• Windows x64
• source distribution

Publishing uses PyPI Trusted Publishing from the Build and Publish workflow and the pypi GitHub environment.

Python API

File-path API

from directlfq_rs import quantify

results = quantify("report.parquet")
print(results["P001"])

quantify() returns a dict[str, list[float | None]] mapping each protein ID to its per-sample protein intensities in linear space.

DataFrame API

import pandas as pd
from directlfq_rs import quantify_dataframe

df = pd.DataFrame(
    {
        "protein": ["P001", "P001"],
        "ion": ["I1", "I2"],
        "sample_0": [100.0, 120.0],
        "sample_1": [200.0, 240.0],
    }
)

protein_df = quantify_dataframe(df)
print(protein_df)

Supported Inputs

DIA-NN report.parquet

For DIA-NN output, .parquet input is auto-detected and parsed as a precursor report.

Generic TSV

For generic wide-table input, the expected shape is:

• one protein column
• one ion column
• one or more sample columns in linear intensity space

Empty cells are treated as missing values.

Scope

This package ships the Rust-backed quantification core and a compact Python API. It does not aim to mirror every workflow and helper exposed by the upstream directlfq package.

License

This project is licensed under the MIT License.