Salvatore Milite

Scalable, fast and accurate differential gene expression testing from millions of cells of multiple patients

This paper introduces a Bayesian framework for differential gene expression analysis that scales to over 10 million cells and integrates data from multiple patients.

Tumour evolution as ground truth for cancer whole-genome sequencing

This paper introduces SCOUT, a synthetic whole-genome sequencing resource for benchmarking cancer genomic analysis, revealing performance issues in complex tumours and highlighting the importance of evolutionary ground truth.

BASCULE: Bayesian inference and clustering of mutational signatures leveraging biological priors

BASCULE uses a Bayesian framework to standardize and cluster mutational signatures across cancer types, identifying prognostic subtypes in over 8,000 samples.

Model-based tumour subclonal deconvolution accounting for spatio-temporal sampling biases

This paper introduces MOBSTERm, a Bayesian framework for subclonal deconvolution that accounts for spatio-temporal sampling biases in cancer sequencing data, improving the analysis of tumour evolution.

Deep Archetypal Analysis for interpretable multi-omic data integration based on biological principles

This paper introduces MIDAA, a framework for multi-omic data integration using deep learning and biological principles, showing it identifies interpretable and biologically relevant patterns better than existing methods.

Clinical application of tumour-in-normal contamination assessment from whole genome sequencing

The paper develops a tool to assess tumour contamination in normal samples using whole-genome sequencing data, finding contamination in various cancer types and highlighting risks to variant calling.

NeuroVelo: interpretable learning of cellular dynamics from single-cell transcriptomic data

NeuroVelo reconstructs cellular dynamics and identifies gene regulatory networks from single-cell transcriptomic data using dynamical systems theory.

A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing

CONGAS+ is a Bayesian method that maps single-cell RNA and ATAC data to copy number clones, revealing subclonal architectures and genotype-phenotype relationships in cancer.

A Bayesian method to cluster single-cell RNA sequencing data using Copy Number Alterations

CONGAS clusters single-cell RNA sequencing data using copy number alterations to infer subclonal composition and RNA phenotypes in cancer data.

Computational validation of clonal and subclonal copy number alterations from bulk tumour sequencing

This paper introduces CNAqc, a toolset for validating clonal and subclonal copy number alterations in bulk tumor sequencing by addressing tumor purity, ploidy, and heterogeneity using simulations and single-cell data.