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...

Full description

Bibliographic Details
Main Authors: Gyllborg, D. (Author), Marco Salas, S. (Author), Mattsson Langseth, C. (Author), Nilsson, M. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02989nam a2200553Ia 4500
001 10.1186-s12859-021-04302-5
008 220427s2021 CNT 000 0 und d
020 |a 14712105 (ISSN) 
245 1 0 |a Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps 
260 0 |b BioMed Central Ltd  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1186/s12859-021-04302-5 
520 3 |a 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. © 2021, The Author(s). 
650 0 4 |a Analysis toolbox 
650 0 4 |a animal 
650 0 4 |a animal experiment 
650 0 4 |a Animals 
650 0 4 |a article 
650 0 4 |a brain 
650 0 4 |a Brain 
650 0 4 |a Brain 
650 0 4 |a brain region 
650 0 4 |a Cellular diversity 
650 0 4 |a cluster analysis 
650 0 4 |a Cluster Analysis 
650 0 4 |a Comprehensive analysis 
650 0 4 |a Computational technique 
650 0 4 |a data analysis software 
650 0 4 |a Dimensional reduction 
650 0 4 |a gene expression 
650 0 4 |a In situ sequencing 
650 0 4 |a Initial exploration 
650 0 4 |a male 
650 0 4 |a Mammals 
650 0 4 |a Mice 
650 0 4 |a mouse 
650 0 4 |a nonhuman 
650 0 4 |a Probabilistic cell typing 
650 0 4 |a Spatial expressions 
650 0 4 |a Spatial technologies 
650 0 4 |a Spatially resolved 
650 0 4 |a Spatially resolved transcriptomics 
650 0 4 |a transcriptome 
650 0 4 |a Transcriptome 
650 0 4 |a transcriptomics 
700 1 |a Gyllborg, D.  |e author 
700 1 |a Marco Salas, S.  |e author 
700 1 |a Mattsson Langseth, C.  |e author 
700 1 |a Nilsson, M.  |e author 
773 |t BMC Bioinformatics