Biology subjects

Romualdi, C.

Publications and source records attributed to Romualdi, C..

13 recordsLinked to original sources

bioRxiv2026

A Pan-Cancer Multi-Omic Analysis of Copy Number Signature Clusters and Genomic Instability

Copy number signatures provide compact representations of the processes that shape cancer genomes, but signatures derived with different feature encodings are often interpreted as if they were interchangeable. We established a matched-sample pan-cancer benchmark of three major copy number signature compendia, comparing their activity structure, cross-framework concordance, patient stratification, outcome associations, and predictability from non-copy-number molecular data. Signature- level concordance was sparse and concentrated in a limited set of biologically related patterns. Clustering of high-activity signatures produced distinct patient partitions with limited overlap between compendia, although one cluster in each framework showed a directionally favorable outcome association after accounting for cancer-type-specific baseline hazards. Prediction from gene expression, DNA methylation, somatic mutations, age, and tumor purity was strongly framework dependent: test-set F1 scores were 0.93 for Drews, 0.80 for Steele, and 0.24 for Tao. Gene expression provided the largest contribution and largely retained the performance of the full models. These results show that compendium choice is an analytical decision rather than an interchangeable preprocessing step. The benchmark provides a reproducible framework for selecting and interpreting copy number signature representations in pan-cancer studies.

cancer biology↗

bioRxiv2025

Benchmarking large-scale single-cell RNA-seq analysis

The increasing size of single-cell RNA sequencing (scRNA-seq) datasets poses major computational challenges. This work benchmarks the scalability, efficiency, and accuracy of five widely used analysis frameworks (Seurat, OSCA, scrap-per, Scanpy, and rapids singlecell), focusing on the impact of algorithmic and infrastructural choices on performance. We performed a systematic comparison of these workflows using representative datasets, including a 1.3 million mouse brain cell dataset for scalability and three smaller datasets (BE1, scMixology, and cord blood CITE-seq) with ground truth labels to assess clustering accuracy. Principal Component Analysis (PCA) was used as a paradigmatic step to evaluate the computational performance of six SVD algorithms (exact, ARPACK, IRLBA, randomized, Jacobi, and incremental PCA) across multiple data representations (dense, sparse, HDF5) and hardware configurations (CPU vs GPU). All methods showed high concordance in PCA results, with negligible loss of accuracy in truncated approaches. GPU-based computation using rapids singlecell provided a 15x speed-up over the best CPU methods, with moderate memory usage. On CPU, ARPACK and IRLBA were the most efficient for sparse matrices, while randomized SVD performed best for HDF5-backed data. Among full pipelines, rapids singlecell was the fastest, whereas OSCA and scrapper achieved the highest clustering accuracy (ARI up to 0.97) in datasets with known cell identities. Performance differences were largely driven by the choice of highly variable genes (HVGs) and PCA implementation. The study highlights that scalability in scRNA-seq analysis depends critically on both algorithmic and infrastructural factors. GPU acceleration and optimized BLAS/LAPACK configurations markedly enhance performance, while Bioconductor-based pipelines remain robust in accuracy. The provided benchmarks offer practical guidelines for efficient and reliable analysis of large-scale single-cell datasets.

bioinformatics↗

bioRxiv2025

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

Despite the abundance of multi-omics integration methods, few adopt a hierarchical approach, which is crucial for understanding how different genomic alterations influence downstream molecular processes. We present gINTomics, a hierarchical computational framework that captures directional biological information flow across genomic, transcriptomic, and epigenomic layers. Unlike existing meta-dimensional approaches, gINTomics follows a multi-staged paradigm reflecting biological hierarchy, modeling molecular modifications by linking genomic alterations to downstream transcriptional consequences. The framework offers flexible integration strategies for different omics combinations and tailored statistical modeling with optional Random Forest for variable selection. A key feature is the comprehensive interactive Shiny application that provides intuitive visualization through interactive plots and circos diagrams, enabling users to explore complex regulatory relationships across multiple analytical perspectives. Validation on TCGA ovarian cancer data confirmed the frameworks effectiveness in identifying biologically relevant regulatory mechanisms and prognostic biomarkers. Available as a Bioconductor package, gINTomics provides researchers with a powerful and accessible tool for mechanistic multi-omics integration and biomarker discovery.

bioinformatics↗

bioRxiv2024

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

Shallow whole-genome sequencing (sWGS) offers a cost-effective approach to detect copy number alterations (CNAs). However, there remains a gap for a standardized workflow specifically designed for sWGS analysis. To address this need, in this work we present SAMURAI a bioinformatics pipeline specifically designed for analyzing CNAs from sWGS data in a standardized and reproducible manner. SAMURAI is built using established community standards, ensuring portability, scalability, and reproducibility. The pipeline features a modular design with independent blocks for data pre-processing, copy number analysis, and customized reporting. Users can select workflows tailored for either solid or liquid biopsy analysis (e.g., circulating tumor DNA), with specific tools integrated for each sample type. The final report generated by SAMURAI provides detailed results to facilitate data interpretation and potential downstream analyses.To demonstrate its robustness, SAMURAI was validated using simulated and real-world data sets. The pipeline achieved high concordance with ground truth data and maintained consistent performance across various scenarios. By promoting standardization and offering a versatile workflow, SAMURAI empowers researchers in diverse environments to reliably analyze CNAs from sWGS data. This, in turn, holds promise for advancements in precision medicine.

bioinformatics↗

bioRxiv2023

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

Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.

bioinformatics↗

bioRxiv2022

Spatial compartmentalization of signalling imparts source-specific functions on secreted factors

Efficient regeneration requires multiple cell types acting in a coordination. To better understand the intercellular networks involved and how they change when regeneration fails, we profiled the transcriptome of hematopoietic, stromal, myogenic, and endothelial cells over 14 days following acute muscle damage. A time-resolved computational model of interactions was generated, and VEGFA-driven endothelial engagement was identified as a key differentiating feature in models of successful and failed regeneration. In addition, it revealed that the majority of secreted signals, including VEGFA, are simultaneously produced by multiple cell types. To test whether the cellular source of a factor determines its function, we deleted VEGFA from two cell types residing in close proximity, stromal and myogenic progenitors. By comparing responses to different types of damage, we found that myogenic and stromal VEGFA have distinct functions in regeneration. This suggests that spatial compartmentalization of signaling plays a key role in intercellular communication networks. HighlightsO_LILigand-receptor signaling redundancy during skeletal muscle regeneration C_LIO_LIInflammatory cells, and muscle and fibro/adipogenic progenitors produce VEGFA C_LIO_LIVEGFA from muscle progenitors control their proliferation after muscle damage C_LIO_LIVEGFA from FAP controls angiogenesis only after ischemic damage C_LI eTOC blurbGroppa et al. performed a novel time-resolved bioinformatics analysis that revealed extensive ligand-receptor redundancy among the cell types contributing to skeletal muscle regeneration. They focused on one of these pathways, and showed that VEGFA from different cell types has distinct roles in regeneration.

cell biology↗

bioRxiv2021

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

SummaryWe present NewWave, a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA sequencing data. To achieve scalability, NewWave uses mini-batch optimization and can work with out-of-memory data, enabling users to analyze datasets with millions of cells. Availability and implementationNewWave is implemented as an open-source R package available through the Bioconductor project at https://bioconductor.org/packages/NewWave/ Supplementary informationSupplementary data are available at Bioinformatics online.

genomics↗

bioRxiv2021

The most comprehensive annotation of the Krill transcriptome provides new insights for the study of physiological processes and environmental adaptation

The krill species Euphausia superba plays a critical role in the food chain of the Antarctic ecosystem, as the abundance of its biomass affects trophic levels both below it and above. Major changes in climate conditions observed in the Antarctic Peninsula region in the last decades have already altered the distribution of the krill population and its reproductive dynamics. A deeper understanding of the adaptation capabilities of this species, and of the molecular mechanisms behind it are urgently needed. The availability of a large body of RNA-seq assays gave us the opportunity to extend the current knowledge of the krill transcriptome, considerably reducing errors and redundancies. Our study covered the entire developmental process, from larval stages to adult individuals, providing information of central relevance for ecological studies. Here we describe the KrillDB2 database, a resource combining the latest annotation of the krill transcriptome with a series of analyses specifically targeting genes and molecular processes relevant to krill physiology. KrillDB2 provides in a single resource the most complete collection of experimental data and bioinformatic annotations: it includes an extended catalog of krill genes; an atlas of their expression profiles over all RNA-seq datasets publicly available; a study of differential expression across multiple conditions such as developmental stages, geographical regions, seasons, and sexes. Finally, it provides initial indications about non-coding RNAs, a class of molecules whose contribute to krill physiology has never been reported before.

ecology↗

bioRxiv2021

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

Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurements at single-cell resolution providing a comprehensive view of the compositions and dynamics of tissue and organism development. The evolution of scRNA-seq protocols has led to a dramatic increase of cells throughput, exacerbating many of the computational and statistical issues that previously arose for bulk sequencing. In particular, with scRNA-seq data all the analyses steps, including normalization, have become computationally intensive, both in terms of memory usage and computational time. In this perspective, new accurate methods able to scale efficiently are desirable. Here we propose PsiNorm, a between-sample normalization method based on the power-law Pareto distribution parameter estimate. Here we show that the Pareto distribution well resembles scRNA-seq data, independently of sequencing depths and technology. Motivated by this result, we implement PsiNorm, a simple and highly scalable normalization method. We benchmark PsiNorm with other seven methods in terms of cluster identification, concordance and computational resources required. We demonstrate that PsiNorm is among the top performing methods showing a good trade-off between accuracy and scalability. Moreover PsiNorm does not need a reference, a characteristic that makes it useful in supervised classification settings, in which new out-of-sample data need to be normalized. PsiNorm is available as an R package available at https://github.com/MatteoBlla/PsiNorm

bioinformatics↗

bioRxiv2021

Molecular diagnosis and prognosis of cancers of unknown-primary (CUPs): progress from a microRNA-based droplet digital PCR assay

Metastasis is responsible for the majority of cancer-related deaths. Particularly challenging is the management of metastatic cancer of unknown primary site (CUP), whose tissue-of-origin (TOO) remains undetermined even after expensive investigations. CUP therapy is rather unspecific and poorly effective. Molecular approaches developed to identify CUPs potential tissue-of-origin, can overcome some of these issues. In this study, we applied a pre-determined set of microRNAs (miRNAs) to infer the TOO of 53 metastatic cancers of unknown or uncertain origin. We designed a molecular assay to quantify 89 miRNAs at the copy number level, using EvaGreen-based Droplet Digital PCR. We assessed miRNA expression in 159 samples including primary tumors from 17 tumor classes (reference set), metastases of known and unknown origin. We applied two different statistical models for class prediction to obtain CUPs most probable TOOs. Specifically, we used the shrunken centroids using PAMR (Prediction Analysis of Microarrays for R) and the least absolute shrinkage and selection operator (LASSO) models. The molecular test was successfully applied to FFPE samples and provided a site-of-origin identification within one-week from the biopsy procedure. The most frequently predicted origins were gastrointestinal, pancreas, breast and lung. The assay was applied to multiple metastases from the same CUP, collected from different metastatic sites: the molecular prediction revealed an impressive agreement in site-of-origin prediction, intrinsically validating our assay. The final prediction was compared with the clinico-pathological hypothesis of primary site. Moreover, a panel of 14 miRNAs proved to have prognostic value and being associated with overall survival. Our study demonstrated that miRNA expression profiling in CUP samples could be employed as diagnostic and prognostic test. Our molecular analysis can be performed on-request, concomitantly with standard diagnostic workup and in association with genetic profiling, to offer valuable indication about the possible primary site, thereby supporting treatment decisions.

pathology↗

bioRxiv2020

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

Genomic imprinting and X chromosome inactivation (XCI) are two prototypical epigenetic mechanisms whereby a set of genes is expressed mono-allelically in order to fine-tune their expression levels. Defects in genomic imprinting have been observed in several neurodevelopmental disorders, in a wide range of tumours and in induced pluripotent stem cells (iPSCs). Single Nucleotide Variants (SNVs) are readily detectable by RNA-sequencing allowing determination of whether imprinted or X-linked genes are aberrantly expressed from both alleles, although standardised analysis methods are still missing. We have developed a tool, named BrewerIX, that provides comprehensive information about the allelic expression of a large, manually-curated set of imprinted and X-linked genes. BrewerIX does not require programming skills, runs on a standard personal computer, and can analyze both bulk and single-cell transcriptomes of human and mouse cells directly from raw sequencing data. BrewerIX confirmed previous observations regarding the bi-allelic expression of some imprinted genes in naive pluripotent cells and extended them to preimplantation embryos. BrewerIX identified also misregulated imprinted genes in breast cancer cells and in human organoids and identified new genes escaping XCI in human somatic cells. We believe BrewerIX will be useful for the study of genomic imprinting and XCI during development and reprogramming, and for detecting aberrations in cancer, iPSCs and organoids. Due to its ease of use to non-computational biologists, its implementation could become standard practice during sample assessment, thus raising robustness and reproducibility of future studies.

bioinformatics↗

bioRxiv2020

Assessment of single cell RNA-seq statistical methods on microbiome data

BackgroundThe correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking. ResultsHere, we compare methods developed for single cell, bulk RNA-seq, and microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, and power. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing. ConclusionsThe multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.

bioinformatics↗

bioRxiv2018

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

Skeletal muscle is composed by different myofiber types that can preferentially use glycolysis or lipids for ATP production. How fuel preference is specified in these post-mitotic cells is unknown. Here we show that miRNAs are important players in defining the myofiber metabolic profile. mRNA and miRNA signatures of all myofiber types obtained at single cell level unveiled fiber-specific regulatory networks and identified two master miRNAs that coordinately control myofiber fuel preference and mitochondrial morphology. Our work provides a complete and integrated myofiber type-specific catalogue of genes and miRNAs expressed and establishes miR-27a-3p and miR-142-3p as key regulators of lipid utilization in skeletal muscle.\n\nHIGHLIGHTSO_LITranscriptional networking in single cells distinguished myofibers based on glycolytic or oxidative metabolism, regulated by specific miRNAs\nC_LIO_LImiR-27a-3p and -142-3p influence mitochondrial morphology\nC_LIO_LImiR-27a-3p improves lipid utilization and increases glycogen storage both in vitro and in vivo\nC_LIO_LImiR-142-3p reduces lipid utilization both in vitro and in vivo\nC_LI

genomics↗