About this Class

Single cell chromatin accessibility, measured by ATAC-seq, and single cell gene expression, measured by RNA-seq provide two views on a cell’s state. From chromatin accessibility it is possible to infer information about the regulation of genes such as the location of putative cis-regulatory elements near a gene and the transcription factors that bind to these regions. Rigorously comparing gene expression and chromatin accessibility in the same single cells could illuminate the logic of how coupling or decoupling of these mechanisms regulates fate commitment and cancer cell state plasticity. MIRA: Probabilistic Multimodal Models for Integrated Regulatory Analysis, is a comprehensive methodology that systematically contrasts transcription and chromatin accessibility to infer the regulatory circuitry driving cells along developmental trajectories. MIRA leverages topic modeling of cell states and regulatory potential modeling of individual gene loci. MIRA thereby represents cell states in an efficient and interpretable latent space and infers high fidelity lineage trees. To determine the key regulators of cell fate decisions MIRA uses a probabilistic in silico deletion method based on DNA sequence motifs and the Cistrome DB compendium of transcription factor binding sites. Applied to epidermal maintenance differentiation and embryonic brain development from two different multimodal platforms, MIRA revealed important insights into the transcriptional regulation of these systems. Speaker: Cliff Meyer Ph.D., Dana-Farber Cancer Institute