The Geometry of Single-Cell Biology: Geodesics, Metrics, and Parallaxes

The power of single-cell genomics is the ability to measure each cell in a tissue. What can be measured varies: transcript counts, how many of the transcripts are spliced, the accessible locations of the chromatin, and so on. We can even make two sets of measurements at the same time! Each measurement tells us a different aspect of the cell's biology, and together, they offer a revolutionary way to understand cellular development, differentiation and disease.

However, the challenge is interpreting the measurements: the data is almost always high-dimensional, noisy, and sparse. In this talk, I will present a set of algorithms that reveal new biology by considering the geometric aspects of these measurements. By exploiting the structure of the Riemannian manifold of gene co-expression, we introduce a powerful new way of discovering gene programs that drive cell fate commitment. To integrate multiple measurement modalities into a cohesive whole, we construct a kernel-based metric learning formalization that can be efficiently solved with quadratic programming. To identify the causal drivers of cell differentiation and disease, we exploit the parallax between leading and lagging measurements in a single-cell snapshot. This allows us to introduce the first generalization of Granger causal inference to directed acyclic graphs. We demonstrate its power by using it to uncover new genes affected in Schizophrenia.


Zoom link: https://mit.zoom.us/j/93513735220

Location: 32 G-575

Refreshments will be available.