Single Cell Data Analysis Algorithms

National Institute Of Diabetes And Digestive And Kidney Diseases

Investigators

Abstract

With advances in single-cell techniques, collecting a large quantity of data has become more accessible and efficient. In contrast, the increased complexity of data has made it more challenging to unravel underlying biological mechanisms. Thus, it is critical to develop novel computational methods capable of dealing with such complexity and of providing some predictive deductions from the data. Following up on our developing Drosophila network inference, we are tackling larger developmental datasets. there are two steps to this. First, we are developing methods for inferring a pseudotime hand-in-hand with a dynamical system for measured gene expression values in single-cells. We are basing this work on the two point Hamilton-Jacobi equation from classical mechanics, extended to the stochastic domain. Second, we are developing methods to make robust low-dimensional approximations for high-dimensional sparse single-cell data that are invertible, so that dynamics inferred in low dimensionos can be projected back to the original gene expression sapce.