Simone Tiberi

DESpace2 identifies differential spatial patterns in spatial omics data by comparing gene expression across experimental conditions.

The paper provides an open-source online book for analyzing spatial transcriptomics data using Bioconductor in R, with reproducible code and datasets.

This study shows that analyzing genetic variants at the isoform level reveals significant associations with human phenotypes and rare variants, highlighting the importance of isoform-resolved analysis in genetics.

IsoBayes infers protein isoform presence and abundance using Bayesian methods integrating proteomics and transcriptomics data, showing good sensitivity and specificity in simulations and real datasets.

DifferentialRegulation identifies differentially regulated genes by analyzing unspliced mRNA abundance across experimental conditions using a Bayesian hierarchical model.

DESpace detects spatially variable genes by testing differential expression between spatial clusters across biological replicates, showing good performance and computational efficiency.

distinct identifies differential distribution patterns in single-cell data by comparing empirical cumulative distribution functions, outperforming existing methods in detecting subtle changes.

This paper explains trajectory inference and RNA velocity methods for analyzing single-cell RNA-seq data to study cellular dynamics.

The paper measures transcriptional bursting using Bayesian methods and shows that polymerase shuttling affects burst size and frequency, with implications for transcriptional noise.

BANDITS is a Bayesian method for differential splicing analysis using RNA-seq data, showing strong performance in simulated and experimental datasets.