Ricard Argelaguet

TL;DR: Multi‐Omics Factor Analysis (MOFA) infers a set of (hidden) factors that capture biological and technical sources of variability that disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities.

TL;DR: This work reports the first single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling and validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.

TL;DR: This work presents Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data that reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints.

TL;DR: A single-cell multi-omics map of chromatin accessibility, DNA methylation and RNA expression during the onset of gastrulation in mouse embryos shows characteristic epigenetic changes that accompany formation of the primary germ layers.

TL;DR: Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omic datasets, infers a set of (hidden) factors that capture biological and technical sources of variability and disentangles axes of heterogeneity.