https://github.com/holmrenser/taxus

Gaussian Process models for transcriptome data
https://github.com/holmrenser/taxus

Last synced: about 1 month ago
JSON representation

Gaussian Process models for transcriptome data

• Host: GitHub
• URL: https://github.com/holmrenser/taxus
• Owner: holmrenser
• Created: 2022-03-25T13:00:13.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-04-28T07:41:39.000Z (over 2 years ago)
• Last Synced: 2024-11-16T19:08:24.335Z (about 2 months ago)
• Language: Python
• Size: 2.3 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md

Awesome Lists containing this project

README

        
# _taxus_

### Gaussian Process models for transcriptome data

[![PyPI version](https://badge.fury.io/py/taxus.svg)](https://badge.fury.io/py/taxus)

[![CI](https://github.com/holmrenser/taxus/actions/workflows/ci.yml/badge.svg)](https://github.com/holmrenser/taxus/actions/workflows/ci.yml)

[![Coverage Status](https://coveralls.io/repos/github/holmrenser/taxus/badge.svg?branch=main)](https://coveralls.io/github/holmrenser/taxus?branch=main)

```

pip install taxus

```

```python

import taxus as tx

# at the moment importing data has to be handled by the user

covariates, counts = get_mock_data()

gp = tx.GP('~ time + treatment', covariates, counts, kernel='rbf', likelihood='poisson')

elbo = gp.fit()

likelihood_ratio_rbf = tx.LRT(

    full_formula='~ time + treatment',

    reduced_formula='~ time',

    covariates=covariates,

    expression=counts,

    kernel='rbf',

    likelihood='nb'

)

likelihood_ratio_linear = tx.LRT(

    full_formula='~ C(time) + C(treatment) + C(time) : C(treatment)',

    reduced_formula='~ C(time) + C(treatment)',

    covariates=covariates,

    expression=counts,

    kernel='linear',

    likelihood='nb'

)

```