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Analysis and Visualization of Spatial Transcriptomic Data

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 techn...

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Detalles Bibliográficos
Autores principales: Liu, Boxiang, Li, Yanjun, Zhang, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8829434/
https://www.ncbi.nlm.nih.gov/pubmed/35154244
http://dx.doi.org/10.3389/fgene.2021.785290
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author Liu, Boxiang
Li, Yanjun
Zhang, Liang
author_facet Liu, Boxiang
Li, Yanjun
Zhang, Liang
author_sort Liu, Boxiang
collection PubMed
description 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.
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spelling pubmed-88294342022-02-11 Analysis and Visualization of Spatial Transcriptomic Data Liu, Boxiang Li, Yanjun Zhang, Liang Front Genet Genetics 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. Frontiers Media S.A. 2022-01-27 /pmc/articles/PMC8829434/ /pubmed/35154244 http://dx.doi.org/10.3389/fgene.2021.785290 Text en Copyright © 2022 Liu, Li and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Liu, Boxiang
Li, Yanjun
Zhang, Liang
Analysis and Visualization of Spatial Transcriptomic Data
title Analysis and Visualization of Spatial Transcriptomic Data
title_full Analysis and Visualization of Spatial Transcriptomic Data
title_fullStr Analysis and Visualization of Spatial Transcriptomic Data
title_full_unstemmed Analysis and Visualization of Spatial Transcriptomic Data
title_short Analysis and Visualization of Spatial Transcriptomic Data
title_sort analysis and visualization of spatial transcriptomic data
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8829434/
https://www.ncbi.nlm.nih.gov/pubmed/35154244
http://dx.doi.org/10.3389/fgene.2021.785290
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