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MIA Talks

Primer: Inference of high-dimensional dynamics

May 8, 2019
Dept. of Systems Biology, Harvard Medical School

Snapshots of single-cell gene expression at a single moment in time encode information about cell state dynamics. But there are challenges to inference: multiple dynamic processes could give rise to the same static snapshot, and the sparsity and high dimensionality of single-cell data make calculations difficult. Using the principle of population balance, we explore the different sources of ambiguity that limit inference from static snapshots, describe the conditions under which dynamics can be determined uniquely, and present an inference algorithm that can calculate these dynamics for sparse high-dimensional data based on spectral graph theory. A key lesson from this approach is that there exists a correspondence between graph-based inference algorithms and models of cell dynamics, which emerges from the correspondence between graph Laplacians and differential operators.