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AlphaQuant

AlphaQuant is an innovative open-source Python package for proteomics data analysis. It implements tree-based quantification - a hierarchical approach to organize and analyze quantitative data across multiple levels - from fragments and MS1 isotopes through charge states, modifications, peptides, and genes.

It is part of the AlphaPept ecosystem from the Mann Labs at the Max Planck Institute of Biochemistry and the University of Copenhagen.

Who Should Use AlphaQuant?

AlphaQuant is designed for proteomics researchers analyzing DDA or DIA experiments with multiple conditions (e.g., control vs. treatment, time-series, or multi-condition studies). If your goal is to compare and interpret quantitative proteomics data systematically, AlphaQuant provides:

• All-in-one Statistical Analysis: AlphaQuant delivers comprehensive statistical analysis of your differential experiments, performing all critical steps from normalization to multiple testing correction in one go, with results visualized through volcano plots and other informative displays.
• Sensitive Detection of Changes: AlphaQuant excels at capturing subtle patterns and handling missing values to ensure important biological signals are not overlooked. This is achieved by using Fragment and MS1-level analysis as well as intensity-dependent counting statistics.
• Proteoform Analysis: AlphaQuant automatically performs clustering of peptides with similar quantitative behavior to infer regulated proteoforms.
• Support for Major Search Engines: Direct support for all major search engines in DDA and DIA workflows (DIA-NN, Spectronaut, AlphaDIA, MaxQuant, FragPipe, AlphaPept) - just use their standard output files

Table of Contents

• Installation
  • One-click GUI
  • Pip
  • Developer installation
  • Docker
• Usage
  • GUI
  • Python and jupyter notebooks
• Troubleshooting
• Citations
• For Developers: Modifying AlphaQuant
• How to contribute
• License
• Changelog

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Installation

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

• One-click GUI installer Choose this installation if you only want the GUI and/or keep things as simple as possible. Install time should be in the order of minutes.

• Pip installer: Choose this installation if you want to use AlphaQuant as a Python package in an existing python 3.11 environment (e.g. a Jupyter notebook). If needed, the GUI 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 AlphaQuant and even allows to modify its source code directly. Generally, the developer version of AlphaQuant outperforms the precompiled versions which makes this the installation of choice for high-throughput experiments.

• Docker Choose this installation if you want to use AlphaQuant without any installation to your system.

E2E tests are run on GitHub's "-latest" runner images, which correspond to Ubuntu 22.04, macOS 14, and Windows Server 2022 at the time of public release.

One-click GUI installation

Currently available for MacOS, Windows. You can download the latest release of alphaquant here.

• Windows: Download the latest alphaquant-X.Y.Z-windows-amd64.exe build and double click it to install. If you receive a warning during installation click Run anyway.
• 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), alphaquant-X.Y.Z-macos-darwin-arm64.pkg or alphaquant-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. If you want to use .raw files on Thermo instruments alphaRaw is required, which depends on Mono. A detailed guide to installing alphaRaw with mono can be found here.

Pip

AlphaQuant can be installed in an existing python 3.11 environment with

pip install alphaquant

Installing AlphaQuant like this avoids conflicts when integrating it in other tools, as this does not enforce strict versioning of dependancies. However, if new versions of dependancies are released, they are not guaranteed to be fully compatible with AlphaQuant. While this should only occur in rare cases where dependencies are not backwards compatible, you can always force AlphaQuant to use dependancy versions which are known to be compatible with:

pip install "alphaquant[stable]"

if you want to add the GUI to your environment, you can install it with the following command:

pip install "alphaquant[stable,gui-stable]"

For those who are really adventurous, it is also possible to directly install any branch (e.g. @development) with any extras (e.g. #egg=alphaquant[stable,development-stable]) from GitHub with e.g.

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

Optional extras

For processing very large PTM files that don't fit in memory, install the optional dask extra:

pip install "alphaquant[dask]"

or the version with a pinned dependency (recommended)

pip install "alphaquant[dask-stable]"

Developer installation

AlphaQuant can also be installed in editable (i.e. developer) mode with a few bash commands. This allows to fully customize the software and even modify the source code to your specific needs. When an editable Python package is installed, its source code is stored in a transparent location of your choice. While optional, it is advised to first (create and) navigate to e.g. a general software folder:

mkdir ~/folder/where/to/install/software
cd ~/folder/where/to/install/software

The following commands assume you do not perform any additional cd commands anymore.

Next, download the AlphaQuant repository from GitHub either directly or with a git command. This creates a new AlphaQuant subfolder in your current directory.

git clone https://github.com/MannLabs/alphaquant.git

For any Python package, it is highly recommended to use a separate conda virtual environment, as otherwise dependancy conflicts can occur with already existing packages.

conda create --name alphaquant python=3.11 -y
conda activate alphaquant

Finally, install AlphaQuant:

pip install -e .

By using the editable flag -e, you can make modifications to the alphaquant source code and these modifications will be directly reflected when running AlphaQuant. We currently recommend the stable

Some details: By default this installs loose dependancies (no explicit versioning). It is also possible to install additional development dependencies, which allows to make use of more features (the call is then a bit more complex and could be e.g. pip install -e "./alphaquant[stable,development-stable]").

Docker

The containerized version can be used to run AlphaQuant 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, e.g. DATA_FOLDER=/home/username/data; mkdir -p $DATA_FOLDER

3. Start the container

docker run -v $DATA_FOLDER:/app/data -p 41215:41215 mannlabs/alphaquant:latest

After initial download of the container, alphaquant will start running immediately, and can be accessed under localhost:41215.

Note: in the app, the local $DATA_FOLDER needs to be referred to as "/app/data".

Alternatively: Build the image yourself

If you want to build the image your