Institute of Genetics and Cancer,

2XU, Crewe Road South,

Edinburgh

I am post doctoral cross disciplinary fellow (XDF) at the University of Edinburgh working in machine learning and statistical modelling of single cell genomics. My aim is to pursue cross-disciplinary research in the intersection of data science and biomedicine.

I am working with Catalina Vallejos and collaborate closely with Neil Henderson’s lab where we use multi-modal single cell technologies to decode the molecular mechanisms regulating liver fibrosis and regeneration. I did my PhD at the School of Informatics (University of Edinburgh) under the supervision of Guido Sanguinetti.

Selected publications

1. Bioinformatics

   Higher order methylation features for clustering and prediction in epigenomic studies

   Kapourani, Chantriolnt-Andreas, and Sanguinetti, Guido

   Bioinformatics 2016

   Abs Bib URL

   Motivation: DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-understood methylation-switch at CpG islands. Results: Here we use probabilistic machine learning to extract higher order features associated with the methylation profile across a defined region. These features quantitate precisely notions of shape of a methylation profile, capturing spatial correlations in DNA methylation across genomic regions. Using these higher order features across promoter-proximal regions, we are able to construct a powerful machine learning predictor of gene expression, significantly improving upon the predictive power of average DNA methylation levels. Furthermore, we can use higher order features to cluster promoter-proximal regions, showing that five major patterns of methylation occur at promoters across different cell lines, and we provide evidence that methylation beyond CpG islands may be related to regulation of gene expression. Our results support previous reports of a functional role of spatial correlations in methylation patterns, and provide a mean to quantitate such features for downstream analyses. Availability: https://github.com/andreaskapou/BPRMeth

   @article{Kapourani2016,
     archiveprefix = {arXiv},
     author = {Kapourani, Chantriolnt-Andreas and Sanguinetti, Guido},
     doi = {10.1093/bioinformatics/btw432},
     eprint = {1603.08386},
     issn = {14602059},
     journal = {Bioinformatics},
     number = {17},
     pages = {i405--i412},
     pmid = {27587656},
     title = {{Higher order methylation features for clustering and prediction in epigenomic studies}},
     url = {https:/doi.org/10.1093/bioinformatics/btw432},
     volume = {32},
     year = {2016}
   }

2. GBIO

   Melissa: Bayesian clustering and imputation of single-cell methylomes

   Kapourani, Chantriolnt-Andreas, and Sanguinetti, Guido

   Genome biology 2019

   Abs Bib URL

   Measurements of single-cell methylation are revolutionizing our understanding of epigenetic control of gene expression, yet the intrinsic data sparsity limits the scope for quantitative analysis of such data. Here, we introduce Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to cluster cells based on local methylation patterns, discovering patterns of epigenetic variability between cells. The clustering also acts as an effective regularization for data imputation on unassayed CpG sites, enabling transfer of information between individual cells. We show both on simulated and real data sets that Melissa provides accurate and biologically meaningful clusterings and state-of-the-art imputation performance.

   @article{kapourani2019melissa,
     author = {Kapourani, Chantriolnt-Andreas and Sanguinetti, Guido},
     journal = {Genome biology},
     number = {1},
     pages = {61},
     publisher = {BioMed Central},
     title = {{Melissa: Bayesian clustering and imputation of single-cell methylomes}},
     url = {https://doi.org/10.1186/s13059-019-1665-8},
     volume = {20},
     year = {2019}
   }

3. GBIO

   scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

   Kapourani, Chantriolnt-Andreas, Argelaguet, Ricard, Sanguinetti, Guido, and Vallejos, Catalina A.

   Genome Biology 2021

   Abs Bib URL

   High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.

   @article{Kapourani2021,
     author = {Kapourani, Chantriolnt-Andreas and Argelaguet, Ricard and Sanguinetti, Guido and Vallejos, Catalina A.},
     doi = {10.1186/s13059-021-02329-8},
     issn = {1474760X},
     journal = {Genome Biology},
     keywords = {DNA methylation,Epigenetic heterogeneity,Hierarchical Bayes,Single-cell},
     number = {1},
     pages = {1--21},
     pmid = {33879195},
     publisher = {Genome Biology},
     title = {{scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution}},
     url = {https://doi.org/10.1186/s13059-021-02329-8},
     volume = {22},
     year = {2021}
   }

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