Guido Sanguinetti

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.

TopOmics: Topic Modelling for All Omics

TopOmics introduces a flexible topic modeling framework for multi-omics data, demonstrating competitive performance and interpretability across diverse datasets.

CRAK-Velo: Chromatin Accessibility Kinetics integration improves RNA Velocity estimation

CRAK-Velo improves RNA velocity estimation by integrating chromatin accessibility data, enabling accurate cell-type deconvolution and insights into gene-chromatin interactions.

Extending differential gene expression testing to handle genome aneuploidy in cancer

DeConveil extends differential gene expression analysis to account for genome aneuploidy by integrating CNV data, improving gene classification and biomarker discovery in cancer.

Pervasive binding of the stem cell transcription factor SALL4 shapes the chromatin landscape

This study shows SALL4 binds across the genome to shape chromatin and gene expression in stem cells.

Modelling transcription with explainable AI uncovers context-specific epigenetic gene regulation at promoters and gene bodies

The paper uses explainable AI to model transcriptional regulation, revealing context-specific epigenetic gene regulation at promoters and gene bodies using chromatin-associated protein data.

SHARE-Topic: Bayesian Inerpretable Modelling of Single-Cell Multi-Omic Data

SHARE-Topic is a Bayesian model for multi-omic single-cell data that identifies interpretable patterns of co-variation between gene expression and epigenomic layers, demonstrating its utility in B-cell lymphoma analysis.

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.

De novo prediction of RNA-protein interactions with Graph Neural Networks

This paper introduces a graph neural network-based method to predict RNA-protein interactions using CLIP-seq data, showing it can reconstruct interaction networks from minimal information.

Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models

The paper introduces a synthetic model-based approach for uncertainty quantification in stochastic gene expression models, showing improved performance over existing methods.

SCRaPL: hierarchical Bayesian modelling of associations in single cell multi-omics data

SCRaPL improves statistical power in single-cell multi-omics correlation analysis by modeling experimental noise, achieving higher sensitivity with similar false positive rates.

Computational identification of splicing phenotypes from single cell transcriptomic experiments

This paper introduces BRIE2, a method to identify splicing phenotypes from single-cell transcriptomic data, showing it can detect differential alternative splicing and improve RNA velocity analysis.

Differential BUM-HMM: a robust statistical modelling approach for detecting RNA flexibility changes in high-throughput structure probing data

This paper introduces diffBUM-HMM, a statistical model for detecting RNA flexibility changes in high-throughput structure probing data, showing higher sensitivity and fewer false positives than existing methods.

scMET: Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

scMET quantifies DNA methylation heterogeneity at single-cell resolution using a Bayesian model, identifying variable features and enabling differential analysis between cell groups.

Quantitative analysis questions the role of MeCP2 as a global regulator of alternative splicing

This study uses machine learning to show that MeCP2 has minimal effects on alternative splicing across three systems and is indifferent to developmental DNA methylation changes.

Model-based tumor subclonal reconstruction

This paper introduces a model-based subclonal reconstruction method that integrates evolutionary theory with machine learning to improve accuracy in tumor subclonal analysis.

Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings

The paper develops a method to infer latent neural states from large-scale neuronal recordings using neural field models and moment closure, applied to mouse retinal data to reveal refractory states in retinal waves.

Melissa: Bayesian clustering and imputation of single cell methylomes

Melissa clusters and imputes single-cell methylomes using a Bayesian method, providing accurate results on simulated and real data.

Single cell multi-omics profiling reveals a hierarchical epigenetic landscape during mammalian germ layer specification

The paper uses single cell triple-omics analysis to reveal how epigenetic changes drive germ layer specification in mouse embryos, showing distinct epigenetic priming and remodeling in different lineages.

Detecting repeated cancer evolution in human tumours from multi-region sequencing data

This paper introduces a machine learning method to detect repeated cancer evolution patterns across patients using multi-region sequencing data, identifying prognostic patient subgroups.

scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells

This paper introduces scNMT-seq, a method for joint profiling of chromatin accessibility, DNA methylation, and transcription in single cells, and validates it in differentiating mouse embryonic stem cells.

Transcriptome-wide splicing quantification in single cells

This paper introduces BRIE, a Bayesian model for accurate splicing quantification in single-cell RNA-seq, enabling differential isoform analysis between datasets.

Domains of methylated CAC and CG target MeCP2 to tune transcription in the brain

The paper shows MeCP2 binds to mCG and mCAC sequences in mouse brain, with binding density correlating with transcriptional regulation of neuronal genes.