![]() ![]() An R-derived FlowSOM process to analyze unsupervised clustering of normal and malignant human bone marrow classical flow cytometry data. Development of a comprehensive antibody staining database using a standardized analytics pipeline. Web-based analysis and publication of flow cytometry experiments. CytoNorm: a normalization algorithm for cytometry data. Van Gassen, S., Gaudilliere, B., Angst, M. diffcyt: differential discovery in high-dimensional cytometry via high-resolution clustering. ShinySOM: graphical SOM-based analysis of single-cell cytometry data. Kratochvíl, M., Bednárek, D., Sieger, T., Fišer, K. Human monocyte heterogeneity as revealed by high-dimensional mass cytometry. Recent advances in computer-assisted algorithms for cell subtype identification of cytometry data. Algorithmic clustering of single-cell cytometry data-how unsupervised are these analyses really? Cytometry A 97, 219–221 (2020). A comparison framework and guideline of clustering methods for mass cytometry data. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Ultrafast clustering of single-cell flow cytometry data using FlowGrid. flowClust: a Bioconductor package for automated gating of flow cytometry data. Rapid cell population identification in flow cytometry data. Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE). flowCore: flowCore: basic structures for flow cytometry data. Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species. A computational pipeline for the diagnosis of CVID patients. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Novel full-spectral flow cytometry with multiple spectrally-adjacent fluorescent proteins and fluorochromes and visualization of in vivo cellular movement. Mass cytometry: single cells, many features. OMIP-051 – 28-color flow cytometry panel to characterize B cells and myeloid cells. Flow cytometry: basic principles and applications. ![]() An average FlowSOM analysis takes 1–3 h to complete, though quality issues can increase this time considerably.Īdan, A., Alizada, G., Kiraz, Y., Baran, Y. We demonstrate the complete workflow, starting from data preparation (such as compensation, transformation and quality control), including detailed discussion of the different FlowSOM parameters and visualization options, and concluding with how the results can be further used to answer biological questions, such as statistical comparison between groups of interest. The protocol provides clearly annotated R code, and is therefore relevant for all scientists interested in computational high-dimensional analyses without requiring a strong bioinformatics background. Where the original paper focused mainly on the algorithm description, this protocol offers user guidelines on how to implement the procedure, detailed parameter descriptions and troubleshooting recommendations. Since the original FlowSOM publication (2015), we have validated the tool on a wide variety of datasets, and to write this protocol, we made use of this experience to improve the user-friendliness of the package (e.g., comprehensive functions replacing commonly required scripts). FlowSOM is used to distinguish cell populations from cytometry data in an unsupervised way and can help to gain deeper insights in fields such as immunology and oncology. The field is therefore slowly moving toward more automated approaches, and in this paper we describe the protocol for analyzing high-dimensional cytometry data using FlowSOM, a clustering and visualization algorithm based on a self-organizing map. ![]() The dimensionality of cytometry data has strongly increased in the last decade, and in many situations the traditional manual downstream analysis becomes insufficient. ![]()
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