Spatially aware dimension reduction for spatial transcriptomics

• Published in: Nature communications Vol. 13; no. 1; pp. 7203 - 22
• Main Authors: Shang, Lulu, Zhou, Xiang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.11.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2397; 631/114/2415; 631/208/212/2019; 631/67/2329; 631/67/327; Data processing; Exome Sequencing; Gene expression; Gene Expression Profiling - methods; Genome; Humanities and Social Sciences; Immunology; Localization; Metastases; Microenvironments; multidisciplinary; Science; Science (multidisciplinary); Software; Spatial analysis; Spatial data; Spatial discrimination; Tissues; Transcriptome - genetics; Transcriptomics; Tumor Microenvironment; Tumorigenesis; Tumors
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-34879-1

Spatial transcriptomics are 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. Here, we develop a spatially-aware dimension reduction method, 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 analysis of spatial transcriptomics. We illustrate the benefits of SpatialPCA for spatial domain detection and explores its utility for trajectory inference on the tissue and for high-resolution spatial map construction. In the real data applications, SpatialPCA identifies key molecular and immunological signatures in a 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. Spatial transcriptomics analyses can be affected by noise and spatial correlation across tissue locations. Here, the authors develop SpatialPCA, a spatially-aware dimensionality reduction method that explicitly models spatial correlation structures, and show its application to the analysis of healthy and tumour tissues.