<!--  -->

<!-- [](https://pepy.tech/project/directlfq) [](https://pepy.tech/project/directlfq) [](https://pepy.tech/project/directlfq) -->

directLFQ

---

<!-- PROJECT LOGO --> <br /> <div align="center"> <img src="release/logos/alpha_logo.png" alt="Logo" width="80" height="80"> <h3 align="center">directlfq</h3> <p align="center"> <a href="https://doi.org/10.1016/j.mcpro.2023.100581">Publication</a> · <a href="https://github.com/Mannlabs/directlfq/releases/latest">Download</a> · <a href="#installation">Installation</a> <!--· <a href="#usage">Usage</a>--> · <a href="https://alphapept.org">alphapept.org</a> </p> </div>

directLFQ is an open-source Python package for quantifying protein intensities based on peptide intensities or fragment-ion intensities measured with from Mass Spectrometry-based proteomics. It preserves peptide ratios, shows very accurate quantification and has a robust normalization approach. Furthermore, it allows fast processing also of very large sample cohorts, as runtime increases linearly with sample number. It is part of the AlphaPept ecosystem from the Mann Labs at the Max Planck Institute of Biochemistry and the University of Copenhagen.

You can process DIA and DDA data analyzed by AlphaPept, MaxQuant, FragPipe, Spectronaut and DIANN as well as generic formats, using a Graphical User Interface (GUI) or the python package.

---

About

Generating protein intensities from Mass Spectrometry proteomics data comes with a variety of challenges. Differing peptides that belong to the same protein can have strongly differing intensities, for example due to differing ionization efficiencies. Missing values (i.e. peptides that have been detected in one run but not in the other) make simple summarization of peptide intensities to protein intensities problematic. Differences in sample loading can introduce systematic biases into the analysis. With directLFQ, we provide a novel algorithm for addressing these challenges in an efficient and accurate manner. directLFQ retains peptide ratios and uses them to infer protein ratios and uses the concept of intensity traces for it's main processing steps. For futher details on the algorithm, please refer to the preprint.

Visit alphapept.org for other packages of AlphaPept ecosystem.

---

Installation

directLFQ can be installed and used on all major operating systems (Windows, macOS and Linux). There are different types of installation possible:

• One-click GUI installation: Choose this installation if you only want the GUI and/or keep things as simple as possible.
• Pip installation: Choose this installation if you want to use directlfq as a Python package in an existing Python 3.9 environment (e.g. a Jupyter notebook). If needed, the GUI and CLI can be installed with pip as well.
• Developer installation: Choose this installation if you are familiar with CLI tools, conda and Python. This installation allows access to all available features of directlfq and even allows to modify its source code directly. Generally, the developer version of directlfq outperforms the precompiled versions which makes this the installation of choice for high-throughput experiments.
• Docker installation: Choose this installation if you want to use directlfq without any changes to your system.

One-click GUI installation

The GUI of directlfq is a completely stand-alone tool that requires no knowledge of Python or CLI tools.

You can download the latest release of directlfq here.

Windows

Download the latest directlfq-X.Y.Z-windows-amd64.exe build and double click it to install. If you receive a warning during installation click Run anyway. Important note: always install directlfq into a new folder, as the installer will not properly overwrite existing installations.

Linux

Download the latest directlfq-X.Y.Z-linux-x64.deb build and install it via dpkg -i directlfq-X.Y.Z-linux-x64.deb.

MacOS

Download the latest build suitable for your chip architecture (can be looked up by clicking on the Apple Symbol > About this Mac > Chip ("M1", "M2", "M3" -> arm64, "Intel" -> x64), directlfq-X.Y.Z-macos-darwin-arm64.pkg or directlfq-X.Y.Z-macos-darwin-x64.pkg. Open the parent folder of the downloaded file in Finder, right-click and select open. If you receive a warning during installation click Open.

In newer MacOS versions, additional steps are required to enable installation of unverified software. This is indicated by a dialog telling you “directlfq. ... .pkg” Not Opened.

1. Close this dialog by clicking Done.
2. Choose Apple menu > System Settings, then Privacy & Security in the sidebar. (You may need to scroll down.)
3. Go to Security, locate the line "directlfq.pkg was blocked to protect your Mac" then click Open Anyway.
4. In the dialog windows, click Open Anyway.

Older releases remain available on the release page, but no backwards compatibility is guaranteed.

Pip installation

directlfq can be installed in an existing Python environment with a single bash command. This bash command can also be run directly from within a Jupyter notebook by prepending it with a !:

pip install "directlfq[dask]"

Installing directlfq like this avoids conflicts when integrating it in other tools, as this does not enforce strict versioning of dependencies. However, if new versions of dependencies are released, they are not guaranteed to be fully compatible with directlfq. This should only occur in rare cases where dependencies are not backwards compatible.

You can always force directlfq to use dependency versions which are known to be compatible with:

pip install "directlfq[stable,dask-stable]"

It is also possible to directly install any branch (e.g. some-branch) from GitHub with

pip install "git+https://github.com/MannLabs/directlfq.git@some-branch#egg=directlfq[stable,development-stable]"

The GUI version can be installed with

pip install "directlfq[gui,dask]"

or

pip install "directlfq[stable,gui-stable,dask-stable]"

Note: The 'dask' extra can be omitted for slimmer dependencies, but this comes with a performance penalty for large datasets.

Developer installation

directlfq can also be installed in "editable" mode. This allows to fully customize the software and even modify the source code to your specific needs.

First, clone the directlfq repository from GitHub to a new directory

mkdir -p ~/directlfq/project/folder && cd ~/directlfq/project/folder
git clone https://github.com/MannLabs/directlfq.git && cd directlfq

Next, it is highly recommended to use a separate conda virtual environment, as otherwise dependency conflicts can occur with already existing packages

conda create --name directlfq python=3.9 -y
conda activate directlfq

Finally, directlfq and all its dependencies need to be installed. To take advantage of all features and allow development (with the -e flag), this is best done by also installing the development dependencies instead of only the core dependencies:

pip install -e ".[dask,development]"

By default this installs 'loose' dependencies (no pinned versions), although it is also possible to use stable dependencies (e.g. pip install -e ".[stable,dask-stable,development-stable]").

By using the editable flag -e, all modifications to the directlfq source code folder are directly reflected when running directlfq. Note that the directlfq folder cannot be moved and/or renamed if an editable version is installed. In case of confusion, you can always retrieve the location of any Python module with e.g. the command import module followed by module.__file__.

Docker installation

The containerized version can be used to run directlfq without any installation to your system.

1. Setting up Docker

Install the latest version of docker (https://docs.docker.com/engine/install/).

2. Prepare folder structure

Set up your data to match the expected folder structure: create a folder and store its name in a variable, and specify a port

DATA_FOLD