International Journal of Molecular Medical Science, 2024, Vol.14, No.5, 293-304 http://medscipublisher.com/index.php/ijmms 298 Figure 3 Results from spatialGE analysis of a melanoma stage IIIc using spatial transcriptomics [Patient 1, Sample 2 (Thrane et al., 2018)] (Adopted from Ospina et al., 2022) Imagine caption: (A) Quilt plot showing log-expression at each spot for immune gene CD74, with squares and crosses indicating tumor and stromal spot classifications respectively. (B) Transcriptomic surface of CD74 expression allows for a clearer visualization of the tissue heterogeneity, with squares indicating tumor compartment. (C) The inferred abundance of B cells in the tissue can be visualized by the predicted surface. (D) Scatter plots showing the relationships between spatial statistics calculated for CD74 and the survival of patients in months from Thrane et al. (2018). The colored arrows provide a guide for the interpretation of the spatial statistics. The dot with a border indicates the tissue section from Patient 1 featured in the other panels in Figure 2. (E) Tumor/stroma assignments and spatially informed clusters with STclust (using a spatial weight of 0.025) are shown. Cluster 1 and cluster 2 represent tumor and stroma regions of the tissue, respectively. Cluster 3 likely correspond to spots showing immune activity given the topological match with CD74 (B) and B cell scores (C) (Adopted from Ospina et al., 2022) 5.3 Computational Tools for Data Analysis The analysis of spatial transcriptomic data requires sophisticated computational tools to handle the complexity and volume of the data. These tools facilitate the identification of spatial patterns, cell types, and gene expression gradients within the TME. 5.3.1 Image-Based transcriptomics Image-based transcriptomics combines high-resolution imaging with transcriptomic data to map gene expression at the cellular level. Techniques such as MERFISH and FISSEQ allow for the visualization and quantification of RNA molecules within tissue sections, providing insights into the spatial organization of the TME (Wang et al., 2021; Price et al., 2022). Deep learning models, trained on histological images and spatial transcriptomic data, can predict cell type distributions and gene expression patterns, enhancing the accuracy of spatial mapping (Choi et al., 2022; Fatemi et al., 2023). 5.3.2 Statistical and machine learning approaches Statistical and machine learning approaches are essential for analyzing spatial transcriptomic data. Tools like spatialGE provide visualizations and quantification of TME heterogeneity through gene expression surfaces and spatial heterogeneity statistics (Ospina et al., 2022). Machine learning algorithms, such as convolutional neural networks, can infer cell types and spatial relationships from histological images, enabling the integration of spatial transcriptomic data with other omics data (Choi et al., 2022; Fatemi et al., 2023). Additionally, R packages like SPIAT offer a suite of tools for spatial data processing, quality control, and analysis, facilitating the extraction of meaningful insights from spatial transcriptomic datasets (Trigos et al., 2020).
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