https://github.com/genentech/decima

Decima is a Python library to train sequence models on single-cell RNA-seq data.
https://github.com/genentech/decima

Last synced: 5 months ago
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Decima is a Python library to train sequence models on single-cell RNA-seq data.

• Host: GitHub
• URL: https://github.com/genentech/decima
• Owner: Genentech
• License: other
• Created: 2024-10-09T22:41:30.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-12-25T05:29:03.000Z (5 months ago)
• Last Synced: 2026-01-08T21:30:33.045Z (5 months ago)
• Language: Python
• Homepage: https://genentech.github.io/decima/
• Size: 6.06 MB
• Stars: 59
• Watchers: 4
• Forks: 8
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.txt
  • Authors: AUTHORS.md

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# Decima

## Introduction

Decima is a Python library to train sequence models on single-cell RNA-seq data.

![Figure](assets/fig1.png)

## Weights

Weights of the trained Decima models (4 replicates) are now available at https://zenodo.org/records/15092691. See the tutorial for how to load and use these.

## Preprint

Please cite https://www.biorxiv.org/content/10.1101/2024.10.09.617507v3. Also see https://github.com/Genentech/decima-applications for all the code used to train and apply models in this preprint.

## Requirements

Decima has been tested on Ubuntu 24.04.3 and MacOS 15.6.1 using Python 3.9-3.12.

## Installation

Install the package from PyPI,

```sh

pip install decima

```

Or if you want to be on the cutting edge,

```sh

pip install git+https://github.com/genentech/decima.git@main

```

Typical installation time including all dependencies is under 10 minutes.

## Tutorials

See the [tutorials](docs/tutorials) for instructions, including how to train your own Decima model with an example dataset.

## Note

This project has been set up using [BiocSetup](https://github.com/biocpy/biocsetup)

and [PyScaffold](https://pyscaffold.org/).