Efficient inference for sparse latent variable models of transcriptional regulation

Multi-study inference of regulatory networks for more accurate models of gene regulation

Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets.

Due to increasing availability of biological data, methods to properly integrate data generated across the globe become essential for extracting reproducible insights into relevant research questions. In this work, we developed a framework to reconstruct gene regulatory networks from expression datasets generated in separate studies—and thus, because of technical variation (different dates, handlers, laboratories, protocols etc…), challenging to integrate. Since regulatory mechanisms are often shared across conditions, we hypothesized that drawing conclusions from various data sources would improve performance of gene regulatory network inference. By transferring knowledge among regulatory models, our method is able to detect weaker patterns that are conserved across datasets, while also being able to detect dataset-unique interactions. We also allow incorporation of prior knowledge on network structure to favor models that are somewhat similar to the prior itself. Using two model organisms, we show that joint network inference outperforms inference from a single dataset. We also demonstrate that our method is robust to false edges in the prior and to low condition overlap across datasets, and that it can outperform current data integration strategies.