Matteo Osella

This study applies intrinsic dimension analysis to single-cell transcriptomic data to capture expression trajectories and define a cell potency score across species and differentiation processes.

The paper introduces IDEAS, a tool for computing intrinsic dimensionality from scRNA-seq data to assess cell potency without clustering, and applies it to reprogramming datasets to study potency changes and trajectories.

This paper compares topic modeling methods on transcriptomic data to identify tissue structure and distinguish healthy from cancerous tissues, showing their effectiveness as embedding spaces for classification.

This paper introduces a hierarchical Stochastic Block Modeling approach to identify universal gene expression patterns in the human brain using the Allen Human Brain Atlas dataset.

The paper analyzes how extrinsic noise affects protein concentration variability during gene activation or inactivation using a stochastic model and simulations.

The paper identifies emergent statistical laws in single-cell transcriptomic data that mirror patterns in linguistics, ecology, and genomics, providing a framework to distinguish biological variability from statistical effects.

The paper shows that protein degradation balances ribosome allocation at slow growth, maintaining active ribosomes even as growth rate diminishes.

This paper creates a 3D spatial transcriptomics atlas of the mouse olfactory mucosa, revealing spatial gene distribution patterns and supporting the chromatographic theory of olfaction.

This study examines how two rounds of whole genome duplication in vertebrates shaped human gene regulatory networks, showing increased complexity and redundancy from WGD events.