Speaker
Description
Inferring gene regulatory interactions from transcriptomic data is crucial for understanding gene networks. It is made difficult by technical noise, cell size and other confounding factors which introduce spurious correlations and inference errors. Focusing on regulation via coding and non-coding RNA interactions, we generalise correlation tests in the presence of technical noise to allow accurate inference. Moment estimates from single cell RNA sequencing data are combined with moment equations from interacting telegraph models to form a computationally tractable optimization problem including semidefinite moment constraints, which can be efficiently solved using a cutting plane approach. We demonstrate the approach using total-RNA-sequencing in single cells, which allows us to identify and model interactions between non-coding RNA and their targets. The resulting network across the non-coding genome recovers causal relations that underlie the true gene-gene correlations in the data.
