Alignment and Integration of Spatiotemporal Transcriptomics Data

Music City Center 

Spatial transcriptomics technologies measure RNA expression at thousands of locations in a 2D tissue slice providing information about the spatial distribution of cell types and the spatial variation in gene expression across a tissue. However, these measurements are typically sparse with high rates of missing data. This talk will describe approaches to align and integrate spatial transcriptomics data from multiple tissues slices using optimal transport, a framework for computing maps between probability distributions. One method, DeST-OT aligns slices from pairs of timepoints from a development process, using semi-relaxed optimal transport to derive rates of cell growth/death. The second method Hidden Markov OT finds consistent low rank representations across multiple timepoints from a developmental process enabling the derivation of cell differentiation maps between cell types. Application of these methods to spatial transcriptomics data from multiple species will be described.

Spatiotemporal Transcriptomics Data