Alignment and integration of spatial transcriptomics data
TLDR
In this paper , a probabilistic alignment of ST experiments (PASTE) is proposed to align and integrate multiple adjacent tissue slices using an optimal transport formulation that models both transcriptional similarity and physical distances between spots.Abstract:
Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a tissue slice while recording the two-dimensional (2D) coordinates of each spot. We introduce probabilistic alignment of ST experiments (PASTE), a method to align and integrate ST data from multiple adjacent tissue slices. PASTE computes pairwise alignments of slices using an optimal transport formulation that models both transcriptional similarity and physical distances between spots. PASTE further combines pairwise alignments to construct a stacked 3D alignment of a tissue. Alternatively, PASTE can integrate multiple ST slices into a single consensus slice. We show that PASTE accurately aligns spots across adjacent slices in both simulated and real ST data, demonstrating the advantages of using both transcriptional similarity and spatial information. We further show that the PASTE integrated slice improves the identification of cell types and differentially expressed genes compared with existing approaches that either analyze single ST slices or ignore spatial information. read more
Citations
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Methods and applications for single-cell and spatial multi-omics
TL;DR: In this article , the authors highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers.
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OUP accepted manuscript
TL;DR: Wang et al. as discussed by the authors proposed a method to decompose cell types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics, which can be used to integrate successive sections and reconstruct the 3D architecture of tissues.
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Single-cell analyses of axolotl telencephalon organization, neurogenesis, and regeneration
Katharina Lust,Ashley Maynard,Tomás Pires de Carvalho Gomes,Jonas S. Fleck,J. Gray Camp,Elly M. Tanaka,Barbara Treutlein +6 more
TL;DR: These analyses yield insights into the organization, evolution, and regeneration of a tetrapod nervous system as well as inferred transcriptional dynamics and gene regulatory relationships of postembryonic, region-specific neurogenesis and unraveled conserved differentiation signatures.
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BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies
Zhengrong Li,Xiang Zhou +1 more
TL;DR: In this article , a Bayesian hierarchical modeling framework is proposed to perform cell type clustering at the single-cell scale and spatial domain detection at the tissue regional scale, with the two tasks carried out simultaneously within a hierarchical model.
Journal ArticleDOI
BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies
Zhengrong Li,Xiang Zhou +1 more
TL;DR: In this paper , a Bayesian hierarchical modeling framework is proposed to perform cell type clustering at the single-cell scale and spatial domain detection at the tissue regional scale, with the two tasks carried out simultaneously within a hierarchical model.
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