Under Disk Drive, find the find the USB drive and double-click on itĥ. Click the “Hardware” tab, then “Device Manager”ģ. Right-click on My Computer, then click “Properties”Ģ. Click “Desktop View”, then “file Explorer”ġ. The serial ID is everything after the last slash mark (“\”)ġ. Choose “Details”, then “Choose Device Instance Path”ĥ. Right-click for Properties, then click “Hardware”Ĥ. Navigate to Computer, then click on the dongle driveĢ. The dongle ID will appear as the last part of a line, everything after the slash mark (“\”)ġ. Select “Device Instance Path” from the drop-down listĤ. Double-click the icon in the lower-right task bar that says “safely remove hardware”ģ. Click on the Storage tab, then click on Disk Utilityġ. Under USB HighSpeed Bus, click on USB DiskĦ. ImmPort Galaxy is a web tool for exploration of data through this introductory video below.5. An introductory video can be found on their website. It has relatively strict requirements for data formatting and annotation. ImmPort: ImmPort is a more comprehensive data warehouse, designed to accommodate many data types, including but not limited to flow cytometry and CyTOF data.This makes uploading and downloading data and annotations easy, especially for those who already use Cytobank. Flow Repository: Flow Repository has the advantage that it is designed like Cytobank, specifically for flow or CyTOF data.Major repositories that can accept CyTOF data include: An introduction presentation introducing these repositories can be found here. These databases in turn become resources for those who would like to re-use data for new purposes. Public data repositories: Depositing flow and CyTOF data in the public domain has become more common, and is increasingly being required by funding agencies and/or journals. UMAP (Becht et al., Nature Biotechnology, 2018) ViSNE (Amir et al., Nature Biotechnology, 2013)įlowSOM (Van Gassen et al., Journal of Quantitative Cell Science, 2015) SPADE (Qiu et al., Nature Biotechnology, 2011) Quick links to publications of some CyTOF data analysis tools: Standardizing immunophenotyping data for the human immunology project (Maecker et al., Nature Reviews, 2012)Ī Universal Live Cell Barcoding-Platform for Multiplexed Human Single Cell Analysis (Hartmann et al., Scientific Reports, 2018) 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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