Carlos N. Talavera-López

The paper introduces NicheCompass, a graph deep learning method for characterizing cell niches in spatial omics data, showing improved performance in identifying and analyzing niches across multiple biological contexts.

This paper uses spatial transcriptomics and single-cell data to identify cellular niches in the human heart, revealing new insights into cardiac function and potential drug targets.

The paper introduces expiMap, a deep learning model that enables interpretable reference mapping of single-cell transcriptome data by incorporating biologically meaningful gene programs.

The paper identifies candidate diagnostic coproantigens for Strongyloides stercoralis using transcriptomic and proteomic data combined with computational analysis.

scvi-tools provides a Python package for probabilistic analysis of single-cell omics data, offering standardized interfaces and extensible tools for various analysis tasks.

The paper introduces an interactive scRNA-Seq analysis environment based on Galaxy, integrating ~70 functions for scalable and accessible single-cell data analysis.

The paper presents an improved genome assembly of Trypanosoma cruzi and reveals a unique genomic structure involving subtelomeric regions and retrotransposons contributing to antigenic variation.

The paper shows that parenchymal astrocytes can undergo neurogenesis when Notch signaling is reduced, mimicking neural stem cell behavior in both striatal and cortical regions.

Across 1,176,683 airway and lung cells from 22 datasets, ACE2 rises with age, in males and in smokers' multiciliated cells, and is lowest in young children.

The paper characterizes the cellular composition and gene expression of the adult human heart using single-cell transcriptomics, identifying distinct cell types and their functional roles.