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Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps
BACKGROUND: A range of spatially resolved transcriptomic methods has recently emerged as a way to spatially characterize the molecular and cellular diversity of a tissue. As a consequence, an increasing number of computational techniques are developed to facilitate data analysis. There is also a nee...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325818/ https://www.ncbi.nlm.nih.gov/pubmed/34332548 http://dx.doi.org/10.1186/s12859-021-04302-5 |
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author | Marco Salas, Sergio Gyllborg, Daniel Mattsson Langseth, Christoffer Nilsson, Mats |
author_facet | Marco Salas, Sergio Gyllborg, Daniel Mattsson Langseth, Christoffer Nilsson, Mats |
author_sort | Marco Salas, Sergio |
collection | PubMed |
description | BACKGROUND: A range of spatially resolved transcriptomic methods has recently emerged as a way to spatially characterize the molecular and cellular diversity of a tissue. As a consequence, an increasing number of computational techniques are developed to facilitate data analysis. There is also a need for versatile user friendly tools that can be used for a de novo exploration of datasets. RESULTS: Here we present MATLAB-based Analysis toolbox for in situ sequencing (ISS) expression maps (Matisse). We demonstrate Matisse by characterizing the 2-dimensional spatial expression of 119 genes profiled in a mouse coronal section, exploring different levels of complexity. Additionally, in a comprehensive analysis, we further analyzed expression maps from a second technology, osmFISH, targeting a similar mouse brain region. CONCLUSION: Matisse proves to be a valuable tool for initial exploration of in situ sequencing datasets. The wide set of tools integrated allows for simple analysis, using the position of individual reads, up to more complex clustering and dimensional reduction approaches, taking cellular content into account. The toolbox can be used to analyze one or several samples at a time, even from different spatial technologies, and it includes different segmentation approaches that can be useful in the analysis of spatially resolved transcriptomic datasets. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04302-5. |
format | Online Article Text |
id | pubmed-8325818 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83258182021-08-02 Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps Marco Salas, Sergio Gyllborg, Daniel Mattsson Langseth, Christoffer Nilsson, Mats BMC Bioinformatics Software BACKGROUND: A range of spatially resolved transcriptomic methods has recently emerged as a way to spatially characterize the molecular and cellular diversity of a tissue. As a consequence, an increasing number of computational techniques are developed to facilitate data analysis. There is also a need for versatile user friendly tools that can be used for a de novo exploration of datasets. RESULTS: Here we present MATLAB-based Analysis toolbox for in situ sequencing (ISS) expression maps (Matisse). We demonstrate Matisse by characterizing the 2-dimensional spatial expression of 119 genes profiled in a mouse coronal section, exploring different levels of complexity. Additionally, in a comprehensive analysis, we further analyzed expression maps from a second technology, osmFISH, targeting a similar mouse brain region. CONCLUSION: Matisse proves to be a valuable tool for initial exploration of in situ sequencing datasets. The wide set of tools integrated allows for simple analysis, using the position of individual reads, up to more complex clustering and dimensional reduction approaches, taking cellular content into account. The toolbox can be used to analyze one or several samples at a time, even from different spatial technologies, and it includes different segmentation approaches that can be useful in the analysis of spatially resolved transcriptomic datasets. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04302-5. BioMed Central 2021-07-31 /pmc/articles/PMC8325818/ /pubmed/34332548 http://dx.doi.org/10.1186/s12859-021-04302-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Marco Salas, Sergio Gyllborg, Daniel Mattsson Langseth, Christoffer Nilsson, Mats Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps |
title | Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps |
title_full | Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps |
title_fullStr | Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps |
title_full_unstemmed | Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps |
title_short | Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps |
title_sort | matisse: a matlab-based analysis toolbox for in situ sequencing expression maps |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325818/ https://www.ncbi.nlm.nih.gov/pubmed/34332548 http://dx.doi.org/10.1186/s12859-021-04302-5 |
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