ANALYSIS Protocols and workflows
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. As such, manual methods of analysis (such as 'gating') are not always suitable for the analysis of these datasets. Various computational tools can be harnessed to aid in the analysis of these datasets, but the use of these tools needs to be well considered.
We have a variety of protocols and workflows available to facilitate data management and preparation, clustering (e.g. FlowSOM, PhenoGraph), dimensionality reduction (e.g. tSNE, UMAP), plotting, and visualisation. Many of these protocols we execute using R, including in our analysis pipeline ‘CAPX’, but we also provide protocols for many of these in other programs, such as FlowJo.
Many of our computational analysis workflows are run in R, and a number of helpful R scripts have been provided at www.github.com/sydneycytometry. Instructions and tutorials of many of these scripts are provided on the same page. We also have an introductory tutorial to using R in RStudio here: using R scripts in R studio.
Training and assistance
We provide training in advanced data analysis, which registered users can request at the link below. Additionally, users may engage one of the team to assist them with data analysis, or have one of the team perform analysis for them at cost, using the consultation link below (rates available here, under 'Operator Assistance'). For more information, please email firstname.lastname@example.org.
*Protocol access for non-registered users:
If you are not a registered facility user, you can gain access to some of our analysis protocols. To do so, please fill in your details here. These are only used for our internal productivity metrics and won’t be used for marketing or other purposes. We may email you from time to time if substantial updates to the protocols are made.