f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq.

Single-cell RNA-sequencing (scRNA-seq) allows studying heterogeneity in gene expression in large cell populations. Such heterogeneity can arise due to technical or biological factors, making decomposing sources of variation difficult. We here describe f-scLVM (factorial single-cell latent variable model), a method based on factor analysis that uses pathway annotations to guide the inference of interpretable factors underpinning the heterogeneity. Our model jointly estimates the relevance of individual factors, refines gene set annotations, and infers factors without annotation. In applications to multiple scRNA-seq datasets, we find that f-scLVM robustly decomposes scRNA-seq datasets into interpretable components, thereby facilitating the identification of novel subpopulations.

Open Targets: a platform for therapeutic target identification and validation.

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation.

The growing availability of multiomic data provides a highly comprehensive view of cellular processes at the levels of mRNA, proteins, metabolites, and reaction fluxes. However, due to probabilistic interactions between components depending on the environment and on the time course, casual, sometimes rare interactions may cause important effects in the cellular physiology. To date, interactions at the pathway level cannot be measured directly, and methodologies to predict pathway cross-correlations from reaction fluxes are still missing. Here, we develop a multiomic approach of flux-balance analysis combined with Bayesian factor modeling with the aim of detecting pathway cross-correlations and predicting metabolic pathway activation profiles. Starting from gene expression profiles measured in various environmental conditions, we associate a flux rate profile with each condition. We then infer pathway cross-correlations and identify the degrees of pathway activation with respect to the conditions and time course using Bayesian factor modeling. We test our framework on the most recent metabolic reconstruction of Escherichia coli in both static and dynamic environments, thus predicting the functionality of particular groups of reactions and how it varies over time. In a dynamic environment, our method can be readily used to characterize the temporal progression of pathway activation in response to given stimuli.

Exploring the complexity of pathway-drug relationships using latent Dirichlet allocation.

Analysis of cellular responses to diverse stimuli enables the exploration in the complexity of functional genomics. Typically, high-throughput microarray data allow us to identify genes that are differentially expressed under a phenomenon of interest. To extract the meanings from the long list of those differentially expressed genes, we present a new method "pathway-based LDA" to determine pathways/gene sets that are perturbed after exposure to different chemicals. In this study, a pathway is defined as a group of functionally related genes. Specifically, we have implemented a probabilistic Latent Dirichlet Allocation (LDA) model to learn drug-pathway-gene relations by taking known gene-pathway memberships as prior knowledge. We applied the pathway-based LDA model and 236 known pathways in order to determine pathway responsiveness to gene expression data of 1169 drugs. Our method yielded a better predictive performance on pathway responsiveness to drug treatments than the existing methods. Moreover, the pathway-based LDA also revealed genes contributing the most in each pre-defined pathway through a probabilistic distribution of genes. In achieving that, our method could provide a useful estimator of the pathway complexity of a genome.

Pathway-based Bayesian inference of drug-disease interactions.

Drug treatments often perturb the activities of certain pathways, sets of functionally related genes. Examining pathways/gene sets that are responsive to drug treatments instead of a simple list of regulated genes can advance our understanding about such cellular processes after perturbations. In general, pathways do not work in isolation and their connections can cause secondary effects. To address this, we present a new method to better identify pathway responsiveness to drug treatments and simultaneously to determine between-pathway interactions. Firstly, we developed a Bayesian matrix factorisation of gene expression data together with known gene-pathway memberships to identify pathways perturbed by drugs. Secondly, in order to determine the interactions between pathways, we implemented a Gaussian Markov Random Field (GMRF) under the matrix factorization framework. Assuming a Gaussian distribution of pathway responsiveness, we calculated the correlations between pathways. We applied the combination of the Bayesian factor model and the GMRF to analyse gene expression data of 1169 drugs with 236 known pathways, 66 of which were disease-related pathways. Our model yielded a significantly higher average precision than the existing methods for identifying pathway responsiveness to drugs that affected multiple pathways. This implies the advantage of the between-pathway interactions and confirms our assumption that pathways are not independent, an aspect that has been commonly overlooked in the existing methods. Additionally, we demonstrate four case studies illustrating that the between-pathway network enhances the performance of pathway identification and provides insights into disease comorbidity, drug repositioning, and tissue-specific comparative analysis of drug treatments.