Data from the CyTOF is exported as flow cytometry standard (*.fcs) files and can be opened and analysed in third party programmes such as Flowjo.
However the multidimensional nature of the data often mean that users are better utilising clustering algorithms to look at their data. The facility has a cloud based solution to this in place for the initial MRC funding period of four years. We have subscribed to a solution called Cytobank.
Cytobank have posted two helpful blogs on how to use and what to consider when using these cluster analysis tools - How to Configure and Run a viSNE Analysis and Fine-Tune viSNE to Get the Most of Your Single-Cell Data Analysis
With Cytobank we have access to all of the current and future analysis solutions developed by the Nolan Lab in Stanford. These tools are based around cluster analysis algorithms that have been developed to identify low frequency cell events in multidimensional mass cytometry data.
SPADE, viSNE and heat maps
An integrated mass cytometry data analysis pipeline that enables simultaneous illustration of cellular diversity and progression.
This runs within R and offers a user friendly interface. Additionally the output includes an PhenoGraph visualisation tool for analysing the data, as well as cluster maps.
Cytofkit can be downloaded from GitHub or Bioconductor
Chen et al (2016). “Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline.” PLOS Computational Biology, 12(9).
A standalone tool for exploratory analysis of high-dimensional single-cell data such as that generated by Mass Cytometry and can be downloaded from the ACCENSE web page to run on Windows or Mac platforms
K Shekhar, et al (2014) Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE) PNAS 111:202-7