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Analysis and visualization of spatial transcriptomic data.
Boxiang Liu,Yanjun Li +1 more
TL;DR: Spatial transcriptomics as mentioned in this paper is a recent technological innovation that measures transcriptomic information while preserving spatial information, and it can be generated in several ways, such as in situ sequencing, in situ hybridization, or spatial barcoding.
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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 either 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 in the expression domain, spatial domain, and interaction domain.
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Citations
Applications of single-cell RNA sequencing in drug discovery and development
Bram Van de Sande,Joon Sang Lee,Euphemia Mutasa-Gottgens,W. Bacon,Jonathan M. Manning,Jack Pollard,Jon D. Hill,Namit Kumar,Mugdha Khaladkar,Ji-Rong Wen,Andrew R. Leach,Edgardo Ferran +11 more
TL;DR: How scRNA-seq methods are being applied in key steps in drug discovery and development is illustrated, and ongoing challenges for their implementation in the pharmaceutical industry are discussed.
157
Computational solutions for spatial transcriptomics
TL;DR: In this paper , a review of the currently available spatial transcriptomics methods and platforms and their strengths and limitations is presented. But, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies.
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Understanding tumour endothelial cell heterogeneity and function from single-cell omics.
Mira Mousa,Aisha Shigna Nadukkandy,Lies Franssens,Halima Alnaqbi,Fatima Yousif Alshamsi,Peter Carmeliet +5 more
TL;DR: The recent single-cell omics studies that have revealed the heterogeneity of human tumour endothelial cells are described and it is demonstrated that the phenotypes of these cells extend beyond that of simply being angiogenic, an observation that could be translated into the clinic to improve upon the success rate of current anti-angiogenic therapies.
72
Spatial Transcriptomic Technologies
Tsai-Ying Chen,Li You,Jose A. Hardillo,Miao-Ping Chien +3 more
TL;DR: How spatial transcriptomics data can be integrated with other omics modalities, complementing other methods in deciphering cellar interactions and phenotypes within tissues as well as providing novel insight into tissue organization is described.
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spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data
Sungwoo Bae,Dong Soo Lee +1 more
TL;DR: In this article , a method, spSeudoMap, was developed to create virtual cell mixtures that closely mimic the gene expression of spatial data and train a domain adaptation model for predicting spatial cell compositions.
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•Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.