JointShrink

JointShrink estimates effects jointly across tissues or conditions using multivariate adaptive shrinkage. It learns covariance patterns from supplied effect estimates and their standard errors, then computes posterior summaries. A native Student-t likelihood and direct outcome-regression models extend this workflow. Fitting uses NumPy and SciPy and does not require R.

Installation

pip install jointshrink

For Matplotlib plots and pandas tables:

pip install "jointshrink[plot,table]"

From GitHub:

pip install "git+https://github.com/nilson01/jointshrink.git"

From a local checkout:

pip install .

Python 3.10 or later is required.

Fit effects across conditions

from jointshrink import set_data, fit

data = set_data(
    [[2, 1.7], [-2, -1.4], [0, .1], [.3, .5], [-.5, -.2], [1, .8]],
    standard_errors=.5,
    condition_names=["A", "B"],
)
result = fit(data, scales=[.2, 1, 2])
print(result.posterior_mean[:2].round(4))

[[ 1.6444  1.6444]
 [-1.5111 -1.5111]]

This constructed example favors a shared-effect pattern. Its intervals and sign probabilities condition on the fitted patterns and mixture weights.

Choose an analysis

The Gaussian summary route follows Urbut et al. (2019). Direct regression samples coefficients and mixture weights under separate Bayesian likelihood models. Check chain diagnostics before interpreting results. Residual SD, count dispersion, ordinal thresholds and Weibull shape are supplied and held fixed.

Documentation and citation

Documentation website

• Installation, tutorials and grouped API.
• Methods and limits, numerical reference checks.
• Reference manual (PDF).

Cite the method paper and record the JointShrink version used. Numerical comparisons describe the checked cases and their limits. See NOTICE and LICENSE for credit and license terms.