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Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps.

Sergio Marco Salas1, Daniel Gyllborg1, Christoffer Mattsson Langseth1

  • 1Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65, Solna, Sweden.

BMC Bioinformatics
|August 1, 2021
PubMed
Summary

Matisse is a MATLAB toolbox for analyzing spatial transcriptomics data from in situ sequencing (ISS) and other methods. It enables de novo exploration, clustering, and dimensional reduction for diverse datasets.

Keywords:
Analysis toolboxIn situ sequencingProbabilistic cell typingSpatially resolved transcriptomics

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatially resolved transcriptomic methods reveal tissue molecular and cellular diversity.
  • Computational tools are increasingly needed for analyzing this complex data.
  • User-friendly, versatile tools are essential for de novo dataset exploration.

Purpose of the Study:

  • To introduce Matisse, a MATLAB-based analysis toolbox for in situ sequencing (ISS) expression maps.
  • To provide a user-friendly platform for exploring and analyzing spatial transcriptomics data.
  • To facilitate both simple and complex analyses of spatially resolved transcriptomic datasets.

Main Methods:

  • Development of a MATLAB-based toolbox named Matisse.
  • Characterization of 2D spatial gene expression using Matisse on mouse coronal sections.
  • Analysis of expression maps from in situ sequencing (ISS) and osmFISH technologies.

Main Results:

  • Matisse effectively characterizes spatial gene expression patterns.
  • The toolbox was demonstrated on 119 genes in a mouse brain coronal section.
  • Comparative analysis included data from both ISS and osmFISH technologies.

Conclusions:

  • Matisse is a valuable tool for the initial exploration of in situ sequencing datasets.
  • The toolbox supports analyses from individual read positions to complex clustering and dimensional reduction.
  • Matisse can analyze multiple samples, including those from different spatial technologies, and offers various segmentation approaches.