Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. RNA molecules are measured by in situ sequencing, in situ hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. Scientists at Baidu Research discuss the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.
An overview of spatial transcriptomic tasks
(A) Spatial transcriptomic datasets map gene expression measurements to their respective locations. (B) A spatial transcriptomic dataset can be analyzed in gene expression space, irrespective of spatial locations. Tasks such as clustering and cell-type identification fall into this category. (C) Spatial information can be used jointly with gene expression to detect spatial expression patterns and spatial domains. (D) These two sources of information can also be used to detect cell-cell and gene-gene interactions.