Francesca D. Ciccarelli

Kandinsky identifies and analyzes cell neighbourhoods in spatial omics data to reveal functional interactions across biological contexts.

The paper investigates how CDKN2A and other 9p21 gene losses influence oesophageal cancer progression, finding context-dependent effects on survival and immune infiltration.

Rarity improves the detection of rare cell populations in single-cell imaging data through a Bayesian latent variable model.

SIMPLI is a software tool for analyzing multiplexed imaging data to enable spatially resolved single-cell tissue phenotyping and marker expression quantification.

ImmunoCluster provides an R package for analyzing high-dimensional cytometry data, enabling non-specialists to profile cellular heterogeneity through scalable analysis and visualization tools.

The paper identifies kinase-specific vulnerabilities and resistances in response to DNA damaging agents using kinase-deficient cell lines and validates gene-drug interactions.

This study identifies patient-specific cancer genes in esophageal adenocarcinoma using machine learning, revealing recurrent process perturbations that suggest therapeutic targets.

The paper introduces conliga, a method for inferring high-resolution SCNA profiles from a low-cost and simple assay, addressing the need for affordable SCNA analysis in clinical settings.