GARP

R codes for graph-aligned random partition (GARP) model.

Authors: Giovanni Rebaudo and Peter Müller.

Overview

This repository is associated with the article Rebaudo, G. and Müller, P. (2024) Graph-aligned random partition model (GARP). Journal of the American Statistical Association (T & M), in press. The key contribution of the paper is outlined below.

[...] Motivated by single-cell RNA applications we develop a novel dependent mixture model to jointly perform cluster analysis and align the clusters on a graph. Our flexible graph-aligned random partition model (GARP) exploits Gibbs-type priors as building blocks, allowing us to derive analytical results on the graph-aligned random partition's probability mass function (pmf). We derive a generalization of the Chinese restaurant process from the pmf and a related efficient and neat MCMC algorithm to perform Bayesian inference.

This repository provides codes to replicate the results in Rebaudo, G. and Müller, P. (2024) Graph-aligned random partition model (GARP). Journal of the American Statistical Association (T & M), in press.

In particular, we provide the R code to implement the MCMC to perform posterior inference under the GARP model.

The repository contains the following:

1. GARP_main.R code to reproduce the main results in the article;
2. GARP_fcts.R functions needed to run the main code;
3. Data-and-Results folder with data and results of the analyses.

Questions or bugs

For bug reporting purposes, e-mail Giovanni Rebaudo (rebaudo.giovanni@gmail.com)

Citation

Please cite the following publication if you use this repository in your research: Rebaudo, G. and Müller, P. (2024) Graph-aligned random partition model (GARP). Journal of the American Statistical Association (T & M), in press.