Britta Velten

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: It is found that the breadth of gene expression and the extent of purifying selection gradually decrease during development, whereas the amount of positive selection and expression of new genes increase during development.

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: 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.

TL;DR: This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care.