Gaussian Process models for transcriptome data
Project description
taxus
Gaussian Process models for transcriptome data
pip install taxus
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'
)
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
taxus-0.0.4.tar.gz
(6.2 kB
view hashes)
Built Distribution
taxus-0.0.4-py3-none-any.whl
(6.5 kB
view hashes)