Analysis and Visualization of Spatial Transcriptomic Data

Abstract

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. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.

Computational solutions for spatial transcriptomics

“…In multicellular resolution spatial barcoding, each spot can contain transcripts from multiple cells. The cell compositions and regional enrichment of different cell types and cell stages in the spots can be resolved with computational deconvolution, mapping, enrichment, and data-integration-based methods [83] , [84] . For instance, SPOTlight [85] uses non-negative matrix factorization (NNMF) and SpatialDecon [86] log-normal regression for deconvolution of the transcriptomics data, whereas Cell2Location is based on a Bayesian model [87] and Tangram is a deep learning framework to resolve cell types.…”

“…We focus on methods specialized for ST data analysis. While a detailed description of all the methods mentioned here is beyond the scope of this review, more detailed descriptions of the ST data analysis methods can be found in the original research articles or the recent reviews [27] , [28] , [29] , [83] , [101] , [102] .

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“…The data imported into the ST data analysis frameworks (Giotto, Squidpy, Seurat, STUtility, SPATA2, scvi-tools, stLearn, and GeoMx) at a minimum consists of cell/spot-by-gene matrix and cell/spot-by-location matrix, and optional associated image data of the sample tissue ( Table 2 ). All the ST frameworks include the common preprocessing tools for normalization, filtering, and dimensional reduction used to ensure the quality and consistency of the ST data [83] , [110] . There is no single solution for all datasets as the data may contain different levels of variation from the confounding sources.…”

“…Of these, BayesSpace offers capabilities to infer higher resolution delineation of the spatial domains by leveraging spatial information in clustering and IloReg optimizes genes used for clustering with a probabilistic feature extraction step before clustering. Intuitive and clear descriptions of many clustering algorithms useful in ST data analysis can be found in [83] . With multicell resolution spatial barcoding data, the deconvolution tools discussed in Section 3.2.3. provide probabilistic cell type compositions of the spots that can be used to divide the sample into cellular regions and determine cell neighborhood composition.…”

spSeudoMap: Cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data

“…Spatial transcriptomics has been widely adopted as a tool to explore genome-wide spatial RNA expression in various tissues 1 . It paves the way to thoroughly investigate the spatial context of cells and their interactions in an unbiased manner 2 . One of the limitations of spatial transcriptomics data is the fact that spots are not directly interpreted as cells.…”