Biology subjects

Billato, I.

Publications and source records attributed to Billato, I..

3 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

Orchestrating Spatial Transcriptomics Analysis with Bioconductor

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

bioinformatics↗

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↗