[Antonio]

Algorithms such as t-SNE, FlowSOM, etc. work better on non-compensated data. Indeed, populations are better resolved. The problem is that to give a biological meaning to a population you need compensated data. One possible workflow is to compensate a dataset, run the algorithm on the uncompensated axis, and use the compensated one to characterize biologically the non-compensated data. If you use a deterministic algorithm you can in theory not compensate your data after assigning a biological relevant population to a region in the multidimensional space.

There is a manuscript in which they use spectral flow cytometry data and apply t-SNE (?) on the raw data (64 channels, without unmixing). I should search for it.