TreeStar’sremains a mainstay but its days of dominance may be numbered.
The most consequential changes inover the past decade or so were conversion from hardware to digital compensation, adoption of the logicle scale ( ), and the introduction of . While FlowJo and its ilk could take the first two in their stride, newer data analysis approaches are necessary as it gets ever easier to assess more cellular parameters simultaneously.
After all, the ‘old’ way of analyzing flow cytometry data has considerable drawbacks.
- Subjective manual gating (sequential partitioning of cell populations) during analysis.
- Multi-center studies show it’s one of the most important causes of experimental variability ( ).
- Fosters confirmation bias.
- Hierarchical analysis. Multi-dimensional data is already artificially squished into 2 dimensions (2D) by these old approaches, with associated loss of information. Imposing artificial hierarchy just adds insult to that injury.
- Technological improvements increase the number of markers that can be assessed in one go. For example, mass cytometry is capable of simultaneously assessing ~40 parameters per cell. The conventional approach of analyzing flow cytometry data across two dimensions at a time just won’t cut the mustard.
All this to say that flow cytometry is presently going through perhaps its most consequential transition phase in decades as it starts to routinely handle previously unimagined complexity. Being in a period of flux makes it foolhardy to judge any of the newer options best. While bioinformaticians have developed several open source computational approaches, many
- Aren’t user-friendly and require end-users with some programming knowledge.
- Are published in specialist bioinformatics journals with little or no outreach to the immunology community.
Thus, a bunch of such programs are floating around and some get tested by some niche users. However, getting one or more of them into mainstream use requires the hard work of running them through their paces and validating them through multi-center field trials, which means comparing their output to that of the current norm. That entails considerable effort.
In recent years, the FlowCAP (Flow Cytometry: Critical Assessment of Population Identification Methods) consortium has made considerable strides in validating some open source automated gating algorithms. In a 2016 paper (), they found open source OpenCyto ( ) performed adequately in a head-to-head comparison with FlowJo. Substantial advantages of approaches such as OpenCyto are
- Replacing subjective manual with more objective automated gating.
- Better mitigation of hierarchical analysis.
Bioconductor () is the best known repository of such open source flow cytometry data analysis software. It has ~42 packages ( ) for handling various aspects of flow cytometry data. Some recent papers ( , 6, 7) discuss pros and cons of the relatively more widely used R, MatLab or Python-based packages such as Citrus ( , ), Phenograph ( , ), SPADE (10), t-SNE ( ), Wanderlust ( ).
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6. Newell, Evan W., and Yang Cheng. “Mass cytometry: blessed with the curse of dimensionality.” Nature Immunology 17.8 (2016): 890-895.
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