1 citations · 1 across the 5 of their papers we have counts for
5 papers
Spatially Regularized Gaussian Mixtures for Clustering Spatial Transcriptomic Data
Andrea Sottosanti, Davide Risso, Francesco Denti
Spatial transcriptomics measures the expression of thousands of genes in a tissue sample while preserving its spatial structure. This class of technologies has enabled the investig…
Multiomics Tissue Segmentation via Spatially-Informed Nested Biclustering Methods
Francesco Denti, Cecilia Balocchi, Vanna Denti +1
Matrix-Assisted Laser Desorption/Ionisation Mass Spectrometry Imaging (MSI) is a powerful technique for spatially resolved molecular profiling and cancer biomarker discovery. Recen…
sanba: An R Package for Bayesian Clustering of Distributions via Shared Atoms Nested Models
Francesco Denti, Laura D'Angelo
Nested data structures arise when observations are grouped into distinct units, such as patients within hospitals or students within schools. Accounting for this hierarchical organ…
Decoding Neuronal Ensembles from Spatially-Referenced Calcium Traces: A Bayesian Semiparametric Approach
Laura D'Angelo, Francesco Denti, Antonio Canale +1
Understanding how neurons coordinate their activity is a fundamental question in neuroscience, with implications for learning, memory, and neurological disorders. Calcium imaging h…
A finite-infinite shared atoms nested model for the Bayesian analysis of large grouped data
Laura D'Angelo, Francesco Denti
The use of hierarchical mixture priors with shared atoms has recently flourished in the Bayesian literature for partially exchangeable data. Leveraging on nested levels of mixtures…