Lara Nonell

Integrating trajectory inference and gene regulatory network analysis to resolve transcriptional programs of T cell state transitions in the tumor microenvironment

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.

Transcriptional reprogramming of tumor-infiltrating T cells during PD-1 blockade revealed through gene regulatory network and trajectory inference in squamous cell carcinoma

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.

EWS-FLI1 Expression in Human Embryonic MSCs Leads to Transcriptional Reprograming, Defective DNA Damage Repair and Ewing Sarcoma

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.

Transcriptomics-Driven Machine Learning Models Accurately Predict Chemotherapy Response in Muscle-invasive Bladder Cancer

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

Behavioural and transcriptomic characterization of the comorbidity between Alzheimer s disease and Major Depression

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.

Are methylation beta-values simplex distributed?

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