PMID- 34332548 OWN - NLM STAT- MEDLINE VI - 22 IP - 1 TI - Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps. PG - 391 CI - © 2021. The Author(s). LA - eng PT - Journal Article PL - England TA - Bmc Bioinformatics JT - BMC bioinformatics JID - 100965194 IS - 1471-2105 (Electronic) LID - 10.1186/s12859-021-04302-5 [doi] FAU - Marco Salas, Sergio AU - Marco Salas S AD - Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65, Solna, Sweden. FAU - Gyllborg, Daniel AU - Gyllborg D AD - Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65, Solna, Sweden. FAU - Mattsson Langseth, Christoffer AU - Mattsson Langseth C AD - Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65, Solna, Sweden. FAU - Nilsson, Mats AU - Nilsson M AUID- ORCID: http://orcid.org/0000-0001-9985-0387 AD - Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65, Solna, Sweden. mats.nilsson@scilifelab.se. IS - 1471-2105 (Linking) SB - IM MH - Animals MH - *Brain MH - Cluster Analysis MH - Mice MH - *Transcriptome OTO - NOTNLM OT - Analysis toolbox OT - In situ sequencing OT - Probabilistic cell typing OT - Spatially resolved transcriptomics PMC - PMC8325818 DCOM- 20210803 LR - 20211125 DP - 2021 Jul 31 DEP - 20210731 AB - 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.