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.
Full spectrum flow cytometric analysis of isolated intestinal immune cells during the course of H. polygyrus infection. FlowSOM (top) and manual (bottom) analysis of live CD45+ cells isolated from naive, day 7, day 14 and day 28 infected duodenal segments stained with 23 surface and intracellular antibodies. Ferrer-Font L. et al. eLife 2020
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.