Chiara Romualdi

Pan-cancer benchmarking reveals complementary copy number signatures with distinct multi-omic predictability

This study benchmarks three copy number signature compendia across cancers, showing their distinct predictability from multi-omic data and highlighting framework-dependent performance.

Benchmarking large-scale single-cell RNA-seq analysis

This paper benchmarks five scRNA-seq analysis frameworks on scalability, efficiency, and accuracy using large and small datasets, finding GPU acceleration and optimized algorithms improve performance.

From Genomics Alterations to Expression Dynamics: A Hierarchical Multi-Omics Integration Framework with gINTomics

gINTomics integrates multi-omics data hierarchically to model genomic alterations' impact on transcriptional processes, identifying regulatory mechanisms and biomarkers in ovarian cancer.

SAMURAI: Shallow Analysis of copy nuMber alterations Using a Reproducible And Integrated bioinformatics pipeline

SAMURAI is a bioinformatics pipeline for analyzing copy number alterations from shallow whole-genome sequencing data, validated with simulated and real-world datasets showing high concordance with ground truth.

signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data

signifinder is an R package that collects, implements, and compares cancer-related gene expression signatures across bulk, single-cell, and spatial transcriptomic data to improve reproducibility and usability.

NewWave: a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data

Allelic expression analysis of Imprinted and X-linked genes from bulk and single-cell transcriptomes

BrewerIX analyzes allelic expression of imprinted and X-linked genes from bulk and single-cell transcriptomes to identify aberrant expression patterns in various biological contexts.

PsiNorm: a scalable normalization for single-cell RNA-seq data

PsiNorm is a scalable normalization method for scRNA-seq data that performs well in accuracy and computational efficiency compared to other methods.

Assessment of statistical methods from single cell, bulk RNA-seq and metagenomics applied to microbiome data

The paper benchmarks statistical methods for microbiome data against RNA-seq methods, finding that univariate RNA-seq methods perform as well as microbiome-specific methods.

Lipid utilization in skeletal muscle cells is modulated in vitro and in vivo by specific miRNAs

The paper identifies miR-27a-3p and miR-142-3p as key regulators of lipid utilization in skeletal muscle by analyzing single-cell mRNA and miRNA signatures.