publications

2025

1. S&P Letters 2024

   Sampling random spanning arborescences in graphs with low conductance

   Vittorio Zampinetti , Harald Melin , and Jens Lagergren

   Statistics & Probability Letters, 2025

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   Sampling random spanning arborescences in directed graphs is critical for applications in network analysis, optimization, and machine learning. While many state-of-the-art methods perform well on graphs with high conductance, they often fail or generalize poorly on low-conductance graphs. Inspired by Wilson’s algorithm, we propose a novel sampling approach that overcomes this limitation by using dynamic programming to compute random walk probabilities. This avoids both inefficient walk simulations and numerically unstable Laplacian determinant calculations. Our method demonstrates superior efficiency and sampling quality in simulations, and is the only one to handle low-conductance graphs effectively.

   @article{treesampling2005,
     title = {Sampling random spanning arborescences in graphs with low conductance},
     journal = {Statistics & Probability Letters},
     volume = {226},
     pages = {110481},
     year = {2025},
     issn = {0167-7152},
     doi = {https://doi.org/10.1016/j.spl.2025.110481},
     url = {https://www.sciencedirect.com/science/article/pii/S0167715225001269},
     author = {Zampinetti, Vittorio and Melin, Harald and Lagergren, Jens},
     keywords = {Random tree sampling, Bayesian inference, Random walk, Wilson’s algorithm},
     language = {English},
     bibtex_show = true,
   }

2024

1. RECOMB 2024

   VICTree - A Variational Inference Method for Clonal Tree Reconstruction

   Harald Melin , Vittorio Zampinetti , Andrew McPherson , and 1 more author

   In Research in Computational Molecular Biology , May 2024

   Also presented as a poster at SIS conference 2024 in Bari, Italy.

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   Clonal tree inference brings crucial insights to the analysis of tumor heterogeneity and cancer evolution. Recent progress in single cell sequencing has prompted a demand for more advanced probabilistic models of copy number evolution, coupled with inference methods which can account for the noisy nature of the data along with dependencies between adjacent sites in copy number profiles. We present VICTree, a variational inference based algorithm for joint Bayesian inference of clonal trees, together with a novel Tree-structured Mixture Hidden Markov Model (TSMHMM) which combines HMMs related through a tree with a mixture model. For the tree inference, we introduce a new algorithm, LARS, for sampling directed labeled multifurcating trees. To evaluate our proposed method, we conduct experiments on simulated data and on samples of multiple myeloma and breast cancer. We demonstrate VICTree’s capacity for reliable clustering, clonal tree reconstruction, copy number evolution and the utility of the ELBO for model selection. Lastly, VICTree’s results are compared in terms of quality and speed of inference to other state-of-the-art methods. The code for VICTree is available on GitHub: github.com/Lagergren-Lab/victree and the full paper on bioRxiv.

   @inproceedings{victree2024,
     author = {Melin, Harald and Zampinetti, Vittorio and McPherson, Andrew and Lagergren, Jens},
     editor = {Ma, Jian},
     title = {VICTree - A Variational Inference Method for Clonal Tree
                Reconstruction},
     booktitle = {Research in Computational Molecular Biology},
     month = may,
     year = {2024},
     publisher = {Springer Nature Switzerland},
     address = {Cham},
     pages = {429--433},
     isbn = {978-1-0716-3989-4},
     biorxiv = {https://www.biorxiv.org/content/early/2024/02/16/2024.02.14.580312},
     language = {English},
     note = {Also presented as a poster at SIS conference 2024 in Bari, Italy.},
     bibtex_show = true,
   }