Minimizing batch effects in mass cytometry data (Schuyler et al., Frontiers in Immunology, 2019)Īcquisition, processing and quality control of mass cytometry data (Lee et al., Mass Cytometry, 2019) Related: A data scientist’s primer to analysis of mass cytometry dataĪ beginner’s guide to analyzing and visualizing mass cytometry data (Kimball et al., Journal of Immunology, 2018) , Journal of Quantitative Cell Science, 2018)Ī comparison of CyTOF analysis methods (Weber and Robinson, Cytometry 2016) The anatomy of single cell mass cytometry data (Olsen et al. In the Cytobank support page, you can find a detailed summary of how to do the analysis. Bead normalization and data transformations are also reviewed in the biosurf tutorial.īasic gating in FlowJo: A tutorial on gating in FlowJo can be found or through this tutorial (from Boston Children Flowlab).Ĭytobank gating and automated analysis: Cytobank has a collection of videos on basic gating as well use of their embedded automated algorithms. Resources below review the most common platforms and algorithms, and also point to public sources of data.Īn overview of CyTOF data analysis (Kimball and al., J.Immunol., 2018)Īn extensive review (Olsen and al., Journal of Quantitative Cell Science, 2018)ĭata pre-processing resources: The Clambey lab has linked guides to signal intensity normalization, debarcoding, and gating on live intact singlets. However, it is difficult if not impossible to visualize all aspects of 40+ parameter data using manual gating so automated clustering and visualization tools have become common. Manual gating of CyTOF data is most commonly done using FlowJo or Cytobank. Review of files for technical quality is also a good idea, either by an automated method or manual gating. ![]() CyTOF data generally requires some pre-processing, for example normalization for signal intensity and debarcoding of individual samples from a composite file.
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