Advice for automated cell classification workflow between datasets with different number of parameters

[denvercal1234GitHub]

Hi there,

Thanks again for the tool.

I have 1 FCS that was acquired by the Aurora and 1 FCS that was acquired by Fortessa with much less number of markers. I aim to use the Aurora dataset to annotate the Fortessa dataset.

All markers in Fortessa are included in the Aurora.

My goal is to see whether a subset of my cells detected in the Aurora can be mapped onto any of the cells in the Fortessa data.

Q1. Should I first remove markers that are not present in the Fortessa for this workflow (https://immunedynamics.io/spectre/Automated-classification.html)?

Q2. If the Aurora was analysed by Catalyst and not Spectre, is it advisable to reanalyse the Aurora first with Spectre, or perhaps, convert the output somehow?

Thank you.

[ghar1821]

Q1: Yes I would remove markers that are not present in the Fortessa. The markers have to exist in both datasets for the classification to work.

Q2: I don't think it matters what tools you used to analyse the Aurora data. Just convert the SCE object into a data.table (https://immunedynamics.io/spectre/tutorials/datatable_interoperability/sce_support.html) object, and run the workflow.

Before you run the classification workflow, please integrate ("batch correct") the Fortessa and the Aurora datasets first as these are acquired by different instruments and will most likely have batch effect on them. You can use Harmony to do the alignment (https://github.com/ImmuneDynamics/Spectre/blob/master/R/run.harmony.R).