Lara Nonell

This paper provides a computational workflow for analyzing T cell state transitions in the tumor microenvironment using single-cell RNA sequencing data, integrating trajectory inference and gene regulatory network analysis.

This study uses gene regulatory network and trajectory inference to analyze single-cell RNA-seq data from squamous cell carcinoma, revealing how PD-1 blockade reprograms T cell states and identifies key transcriptional regulators.

This study evaluates homologous recombination repair status in metastatic prostate cancer using next-generation sequencing and RAD51 immunofluorescence assays, finding strong correlation between genomic and functional HRR loss.

This study shows that EWS-FLI1 expression in human embryonic mesenchymal stem cells leads to an ES-like transcriptome and tumor formation, despite not preferentially binding promoters.

The paper develops a machine learning model using transcriptomic data to predict chemotherapy response in muscle-invasive bladder cancer with high accuracy.

This study identifies shared transcriptomic changes between Alzheimer's disease and Major Depression in mice, suggesting Major Depression may be an early manifestation of Alzheimer's.

The paper tests if DNA methylation beta-values follow a simplex distribution and recommends regression models based on sample size and data type.