publications

2026

1. Cellmates: a method for single-cell phylogeny reconstruction with proper evolutionary distances from copy numbers

   Vittorio Zampinetti , Harald Melin , Marzie Abdolhamdi , and 1 more author

   bioRxiv, 2026

   Also presented at Bertinoro Computational Biology Meeting 2026, Italy

   Code
2. Scalable and robust phylogenetic tree reconstruction from copy-number data with Sparse Rooted Neighbor Joining

   Vittorio Zampinetti , Harald Melin , Alva Hallin , and 1 more author

   bioRxiv, 2026

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   Background Phylogenetic tree reconstruction from single cell data based on copy-number alterations (CNAs) is an important problem in cancer genomics. Methods have been developed to address this problem by computing pairwise distances between copy-number profiles and employing a tree reconstruction algorithm. Despite the tight interplay between distance estimation and tree reconstruction, these two steps are often treated as separate problems, with the choice of the reconstruction algorithm receiving little attention. Most methods rely on classical Neighbor Joining (NJ), an algorithm designed for unrooted phylogenies that does not account for the fixed diploid root inherent to copy-number evolution.Results We identify the Deepest Least Common Ancestor NJ (DLCA–NJ), not previously applied in this context, as the appropriate algorithm for phylogenies from copy-number data. By leveraging the known diploid root, it consistently outperforms standard NJ on simulated benchmarks across all evaluated metrics, with the most pronounced improvement in root placement accuracy. Building on these findings, we introduce Sparse Rooted Neighbor Joining (SRNJ), a scalable adaptation of DLCA–NJ. SRNJ significantly reduces running time while trading off only a minor loss in accuracy. We provide theoretical and empirical evidence of robustness to mutation rate using both synthetic and real biological datasets.Conclusions Rooted NJ variants offer a principled way to exploit the known diploid root when reconstructing phylogenies from copy-number data, and SRNJ extends this advantage to datasets whose size places the full distance matrix out of reach. The gains are clearest where distances are reliable, as on simulated data, while on real data accuracy appears to be constrained by distance estimation rather than by the reconstruction algorithm, leaving room for improvement as callers advance.Competing Interest StatementThe authors have declared no competing interest.Swedish Research Council, https://ror.org/03zttf063, 2022-06725

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 = {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.

   Abs Bib HTML PDF Code

   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,
   }