Skok Gibbs, C. | Search bioRxiv

bioRxiv2022

Probabilisitic Matrix Factorization for Gene Regulatory Network Inference

Inferring gene regulatory networks (GRNs) from single cell data is challenging due to heuristic limitations and a lack of uncertainty estimates in existing methods. To address this, we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships, incorporating experimental evidence via prior distributions. By utilizing variational inference, we facilitate hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using single cell datasets from Saccharomyces cerevisiae, human Peripheral Blood Mononuclear Cells (PBMCs), and BEELINE synthetic data. We discover that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates for additional interpretability.

genomics↗

bioRxiv2021

Single-cell gene regulatory network inference atscale: The Inferelator 3.0

MotivationGene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above. ResultsIn this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data. AvailabilityThe inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/).

systems biology↗