Cell populations are complex
As a scientific community we now routinely include 10+ markers in flow cytometry analyses and with newer platforms such as the Aurora and CyToF easily reaching 20+. This allows us to study cell populations in their true complexity.
However, the extent at which we can analyse this data manually is fairly limited, and extremely time consuming. In order to get the most out of our data we have to use machine learning approaches. This not only ensures identification of all relevant results, but also vastly reduces the time required to analyse a dataset.
Directing and streamlining future or existing approaches
Developing pipelines to be used by your own lab members
Developing scripts to generate tailored outputs from your inputs
Taking your raw data and providing a full analysis and report
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