Research Interests
The Risso Lab has extensive expertise in the analysis of high-throughput biological data, both from a method development and a collaborative perspective. Currently, our main focus is on single-cell and spatial omics, including transcriptomics, epigenomics and proteomics.
A diverse network of collaborators allows us to work with real data, tackling problems of significance for biomedical research, while being embedded in a statistics department allows us to leverage the latest developments in statistical methodology and computational techniques.
Here is a selected list of our latest work in our main areas of interest.
Spatial transcriptomics
Sottosanti, Denti, Risso (2025) Spatially Regularized Gaussian Mixtures for Clustering Spatial Transcriptomic Data. Journal of Classification.
Sottosanti, Risso (2023) Co-clustering of spatially resolved transcriptomic data. Annals of Applied Statistics.
Righelli, Sottosanti, Risso (2023) Designing spatial transcriptomics experiments. Nature Methods.
Single-cell omics
Billato, Pagés, Carey, Waldron, Sales, Romualdi, Risso (2025) Benchmarking large-scale single-cell RNA-seq analysis. bioRxiv.
Castiglione, Segers, Clement, Risso (2024) Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data. arXiv.
Corbetta, Finos, Geistlinger, Risso (2024) Conformal inference for cell type annotation with graph-structured constraints. arXiv.
Bioconductor software
Orchestrating Spatial Transcriptomics Analysis with Bioconductor
Selected Bioconductor packages developed in the Risso Lab: SingleCellExperiment, SpatialExperiment, SpaceTrooper, RUVSeq, zinbwave, scone.
Collaborative work in neurobiology, development, and cancer biology
Ford, Zuin, Righelli, Medina, Schoch, Singletary, Muheim, Frank, Hicks, Risso, Peixoto (2024) A global transcriptional atlas of the effect of acute sleep deprivation in the mouse frontal cortex. iScience.
Van den Berge, Bakalar, Chou, Kunda, Risso, Street, Purdom, Dudoit, Ngai, Heavner (2026) A Latent Activated Olfactory Stem Cell State Revealed by Single-Cell Transcriptomic and Epigenomic Profiling. Stem Cell Reports.
Modzelewski, Shao, Chen, Lee, Qi, Noon, Tjokro, Sales, Biton, Anand, Speed, Xuan, Wang, Risso, He (2021) A mouse-specific retrotransposon drives a conserved Cdk2ap1 isoform essential for development. Cell.
Graphical models
Nguyen, Chiogna, Risso, Banzato (2025) Guided structure learning of DAGs for count data. Statistical Modelling.
Nguyen, Van den Berge, Chiogna, Risso (2023) Structure learning for zero-inflated counts with an application to single-cell RNA sequencing data. Annals of Applied Statistics.
Banzato, Chiogna, Djordjilović, Risso (2023) A Bartlett-type correction for likelihood ratio tests with application to testing equality of Gaussian graphical models. Statistics and Probability Letters.