B1253
Title: Spatially aware dimension reduction for spatial transcriptomics
Authors: Lulu Shang - MD Anderson (United States) [presenting]
Xiang Zhou - University of Michigan (United States)
Abstract: Spatial transcriptomics is a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. A spatially-aware dimension reduction method is developed, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored and novel analysis of spatial transcriptomics. The benefits of SpatialPCA are illustrated for spatial domain detection, and its utility for trajectory inference on the tissue and high-resolution spatial map construction is explored. In real data applications, SpatialPCA identifies key molecular and immunological signatures in a newly detected tumor surrounding microenvironment, including a tertiary lymphoid structure that shapes the gradual transcriptomic transition during tumorigenesis and metastasis. In addition, SpatialPCA detects the past neuronal developmental history that underlies the current transcriptomic landscape across tissue locations in the cortex.
