The overall project aim is to identify predictors of response to chemo-radiotherapy in patients with colorectal cancer. We have collected blood samples pre- and post- treatment from >100 patients with colorectal cancer and analysed these using multi-parameter flow cytometry to determine whether subtypes of immune cells present in peripheral blood are associated with treatment response.

Principal investigator

Melanie McCoy
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Area of science


Systems used


Applications used

Unsupervised hierarchical clustering algorithms through R including Citrus, Phenograph and UMAP
Partner Institution: The University of Western Australia | Project Code: Chemo-radiotherapy

The Challenge

Traditional methods of flow cytometry data analysis are time consuming and require a priori knowledge of cell populations of interest.

The Solution

Unsupervised hierarchical clustering algorithms enable faster and more objective analysis of large flow cytometry data sets.

The Outcome

Such clustering algorithms require large amounts of memory & processing power. It would not be possible to perform these analyses for this project without use of the Nimbus service.