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MARMOT: A multifaceted R pipeline for analysing spectral flow cytometry data from subcutaneously growing murine
Lydia Kirsche1, Jiazhuo He1, Anne Müller2
1Institute of Molecular Cancer Research, University of Zürich, Zürich, Switzerland.
The analysis of murine immune cell types is a critical component of immunological research, necessitating precise and reproducible methodologies. Here, we present a comprehensive protocol and pipeline designed to streamline the process from murine gastric organoid transplant sample preparation to figure generation. This pipeline includes a detailed staining panel tailored for murine immune cells, ensuring accurate and comprehensive identification of various cell types. Additionally, it integrates an R-based analysis script (MARMOT Pipeline), encompassing data processing and visualisation. A key feature of this pipeline is its ability to produce publication-quality figures with minimal direct R coding, thus making advanced data analysis accessible to researchers with limited programming experience. Additionally, figures can be customised using a provided Shiny application. This approach both enhances the efficiency of data analysis and enables the reproducibility required for high-quality scientific research.
The analysis of murine immune cell types is a critical component of immunological research, necessitating precise and reproducible methodologies. Here, we present a comprehensive protocol and pipeline designed to streamline the process from murine gastric organoid transplant sample preparation to figure generation. This pipeline includes a detailed staining panel tailored for murine immune cells, ensuring accurate and comprehensive identification of various cell types. Additionally, it integrates an R-based analysis script (MARMOT Pipeline), encompassing data processing and visualisation. A key feature of this pipeline is its ability to produce publication-quality figures with minimal direct R coding, thus making advanced data analysis accessible to researchers with limited programming experience. Additionally, figures can be customised using a provided Shiny application. This approach both enhances the efficiency of data analysis and enables the reproducibility required for high-quality scientific research.
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