Damien Arnol

MEFISTO enables spatio-temporal dimensionality reduction and pattern separation in multi-modal genomic data, revealing conserved and diverged developmental programs and microbiome heterogeneity.

MOFA+ integrates structured single-cell multi-modal data by modeling group and view structures, revealing coordinated gene expression and epigenetic changes during mouse gastrulation.

This paper introduces SVCA, a method to analyze spatial molecular data and quantify the impact of cell-cell interactions on gene expression in breast cancer tissue.

The paper shows that targeting tandem amplified regions in CRISPR-Cas9 screens reduces cellular fitness, while chromosomal duplications have minimal effects.

MOFA, a sparse Bayesian factor model with mixed likelihoods, separates shared from modality-specific multi-omic variation, recovering IGHV, trisomy 12 and oxidative-stress axes in 200 leukaemia samples.