Via a confluence of genomic technology and computational developments the possibility of network inference methods that automatically learn large comprehensive models of cellular regulation is closer than ever. This talk will focus on enumerating the elements of computational strategies that, when coupled to appropriate experimental designs, can lead to accurate large-scale models of chromatin-state and transcriptional regulatory structure and dynamics. We highlight four research questions that require further investigation in order to make progress in network inference: using overall constraints on network structure like sparsity, use of informative priors and data integration to constrain individual model parameters, estimation of latent regulatory factor activity under varying cell conditions, and new methods for learning and modeling regulatory factor interactions. I will contrast two recent studies from the lab that focus on inference from single-cell and spatial transcriptomics aimed at healthy and diseased brain and spinal tissues.
- Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data.
- Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling.
- Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments.
- Spatiotemporal dynamics of molecular pathology
Winter 2021 workshopw will be held remotely via Zoom. Contact Katie Kanagawa (email@example.com) for Zoom dial-in details.
Bio: Richard Bonneau, Professor of Biology and Computer Science, and Director, Center for Genomics and Systems Biology, New York University