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We design high-dimensional data analysis pipelines to reduce the complexity of datasets and better understand what our data is telling us.

As the number of parameters that we can measure in cytometry increases, so too does the complexity of the data we are able to generate. Manual data analysis methods are not always suitable for the investigation of these datasets. For data collected on our cytometers, the Hugh Green Cytometry Centre can create customised high-dimensional data analysis pipelines that range from data preparation, batch-to-batch normalisation, data analysis (dimensionality reduction and clustering algoritms), to statistics and reporting.


The example below is a high-dimensional analysis on helminth-infected digested gut samples, comparing different infection time points. This data was analysed using the clustering algorithm flowSOM.

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FlowJo software is the most common flow cytometry data analysis software used at the Hugh Green Cytometry Centre. When starting out with FlowJo, we recommend the following webpage to learn the basics.

For the high-dimensional flow cytometry data analysis software we also use OMIQ software. When starting out with OMIQ, we recommend the following OMIQ tutorial.

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