Eugenia Flores Figueroa

The paper identifies four epithelial transcription states in pancreatic cancer and links their origins to early developmental processes.

This study characterizes morphological classes in pancreatic cancer using transcriptomic, genomic, and clinical data to identify molecular associations and prognostic implications.

ClumPyCells corrects cell size biases in spatial -omics data to accurately measure cell and marker aggregation in tissues.

This study analyzes ctDNA shedding in pancreatic cancer using paired tissue and plasma whole genomes, finding that subclones contribute significantly to early dissemination and ctDNA levels.

NEST detects cell-cell communication patterns using a deep learning-based attention mechanism, outperforming existing tools and identifying biologically relevant communication in various tissues and diseases.