Research

I aim to contribute to minimizing the suffering caused by cancer by developing computational methods that extract actionable information from tumors.

Describing the heterogeneous populations of cells that constitute tumors is an important step towards designing effective cancer therapies. With the development of single-cell sequencing technologies, this heterogeneity can be accessed at very high resolution and used to understand tumor progression. I develop statistical models and learning algorithms to describe the heterogeneity of tumors from high-throughput single-cell sequencing data. During my PhD, I designed and implemented three computational tools for single-cell RNA and DNA sequencing data analysis, and applied these methods to discover subgroups within a large cohort of metastatic melanoma patients. My doctoral thesis is available here and the cover features a beautiful drawing by Inês Viegas Oliveira.

I am enthusiastic about sharing my research in conferences and meetings. You can find a list of some of my talks and poster presentations below.

I do my best to provide constructive criticism to authors of papers submitted to journals and conferences. I reviewed submissions to Nature Communications, Cell Reports Methods, Genome Research, Genome Biology, Bioinformatics, BMC Bioinformatics, PLoS Computational Biology, RECOMB and ISMB.

Selected papers

Please see my Google Scholar page for a complete list.

• Jack Kuipers * , Mustafa A. Tuncel * , Pedro F. Ferreira * , Katharina Jahn, Niko Beerenwinkel. Single-cell copy number calling and event history reconstruction. 2025.
• Pedro F. Ferreira, Jack Kuipers, Niko Beerenwinkel. Identifying hierarchical cell states and gene signatures with deep exponential families for single-cell transcriptomics. 2024.
• Pedro F. Ferreira, Jack Kuipers, Niko Beerenwinkel. Mapping single-cell transcriptomes to copy number evolutionary trees. 2021.

Talks, lectures and posters

• EMBO workshop in Spatial Biology of Cancer, London, UK. Hierarchical cell states in single-cell and spatial transcriptomics data from tumors. (poster)
• FARO Seminar in Oncology, Basel, CH. Tumor single-cell genomic and transcriptomic data analysis and integration with applications to melanoma.
• ICSDS 2023 session on “Omics data analysis”, Lisbon, PT. Deep exponential families for single-cell data analysis. (invited talk)
• Basel Computational Biology Conference (BC2), Basel, CH. Deep exponential families for single-cell data. (poster)
• OLISSIPO workshop, INESC-ID, Lisbon, PT. Analysis of single-cell data from tumors. (workshop)
• D-BSSE internal seminar, Basel, CH. Understanding tumor evolution through single-cell data.
• Raphael group, Princeton University, NJ, USA. Understanding tumor heterogeneity through single-cell data.
• Statistics seminar, UBC, Vancouver, CA. Understanding tumor heterogeneity through single-cell data.
• CytoData Symposium, Allen Institute for Cell Science, Seattle, WA, USA. Understanding tumor heterogeneity through single-cell data. (invited talk)
• RECOMB 2022, San Diego, CA, USA. Mapping single-cell transcriptomes to copy number evolutionary trees. (contributed talk)
• OLISSIPO exchange, Lisbon, Portugal. Mapping single-cell transcriptomes to copy number evolutionary trees.
• Ascona workshop, Ascona, CH. Mapping single-cell transcriptomes to copy number evolutionary trees.
• Cancer evolution. Bad Honnef, DE. Mapping single-cell transcriptomes to copy number evolutionary trees. (poster)
• D-BSSE VMB seminar, Basel, CH. Matrix factorization in single-cell data.
• SIB PhD student retreat, Davos, CH. Mapping single-cell transcriptomes to copy number evolutionary trees.