Skip to main content

Add a short description here!

Project description

PyPI-Server Unit tests DOI

Decima

Introduction

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

Figure

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,

pip install decima

Or if you want to be on the cutting edge,

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

Typical installation time including all dependencies is under 10 minutes.

Tutorials

See the tutorials for instructions, including how to train your own Decima model with an example dataset.

Note

This project has been set up using BiocSetup and PyScaffold.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

decima-0.5.1.tar.gz (2.0 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

decima-0.5.1-py3-none-any.whl (94.3 kB view details)

Uploaded Python 3

File details

Details for the file decima-0.5.1.tar.gz.

File metadata

  • Download URL: decima-0.5.1.tar.gz
  • Upload date:
  • Size: 2.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for decima-0.5.1.tar.gz
Algorithm Hash digest
SHA256 8bfe8a2eab6b9655b839a77e48f4cacaad671647b5323b9936820e042fe1926b
MD5 edbb05093bd48ec37fbee272024f5145
BLAKE2b-256 5aee49cb44915ccf2645d754281c86db6e4931e31a7de625786a8a927230dbb0

See more details on using hashes here.

Provenance

The following attestation bundles were made for decima-0.5.1.tar.gz:

Publisher: publish-pypi.yml on Genentech/decima

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file decima-0.5.1-py3-none-any.whl.

File metadata

  • Download URL: decima-0.5.1-py3-none-any.whl
  • Upload date:
  • Size: 94.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for decima-0.5.1-py3-none-any.whl
Algorithm Hash digest
SHA256 fb8173315a2402478390c62283a17ca375904a3ba698fb929cb09bd06891a7b9
MD5 4f0eae9992d04e3dfc9759fe7f7b94b8
BLAKE2b-256 b37e7abdf758c0ae67965b14ee2ddbd3b6394cafeb90528616b08bedb17d72ba

See more details on using hashes here.

Provenance

The following attestation bundles were made for decima-0.5.1-py3-none-any.whl:

Publisher: publish-pypi.yml on Genentech/decima

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page