Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO.

Factor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known.

Identifying the Potential Mechanism of Action of SNPs Associated With Breast Cancer Susceptibility With GVITamIN.

In the last decade, a large number of genome-wide association studies have uncovered many single-nucleotide polymorphisms (SNPs) that are associated with complex traits and confer susceptibility to diseases, such as cancer. However, so far only a few heritable traits with medium-to-high penetrance have been identified. The vast majority of the discovered variants only leads to disease in combination with other still unknown factors.

MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data.

Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data.

Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis.

Technological advances enable assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing molecular variations in physiological contexts. While these methods are increasingly accessible, computational approaches for studying the interplay of the spatial structure of tissues and cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data.

Structural rearrangements generate cell-specific, gene-independent CRISPR-Cas9 loss of fitness effects.

Background: CRISPR-Cas9 genome editing is widely used to study gene function, from basic biology to biomedical research. Structural rearrangements are a ubiquitous feature of cancer cells and their impact on the functional consequences of CRISPR-Cas9 gene-editing has not yet been assessed.

Results: Utilizing CRISPR-Cas9 knockout screens for 250 cancer cell lines, we demonstrate that targeting structurally rearranged regions, in particular tandem or interspersed amplifications, is highly detrimental to cellular fitness in a gene-independent manner.

Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.

Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets.

Coupled Motions in β2AR:Gαs Conformational Ensembles.

G protein-coupled receptors (GPCRs) act as conduits in the plasma membrane, facilitating cellular responses to physiological events by activating intracellular signal transduction pathways. Extracellular signaling molecules can induce conformational changes in GPCR, which allow it to selectively activate intracellular protein partners such as heterotrimeric protein G. However, a major unsolved problem is how GPCRs and G proteins form complexes and how their interaction results in G protein activation.