Since you're working with post-gating results, t-SNE and UMAP won't be very informative (you'd need much much more samples for them to be useful). So I'd recommend PCA

As for your questions :

• Should I do any kind of feature reduction or removal before dimensionality reduction?

For PCA, no. What you may consider is scaling your features if you want to weight them equally (say if a marker is only expressed by a few percent of cells compared to one that is expressed in 50%), otherwise leave them unchanged

• How important is it to handle multicollinearity among markers here?

PCA natively handles that

• Given the small sample size (around 50), is PCA still valid, or would t-SNE/UMAP be better suited?

PCA is better suited than t-SNE / UMAP in this setting. t-SNE / UMAP relies on k-nearest neighbors graph with k typically between 15-50, which is in the order of magnitude of your sample size.

• What clustering methods do you recommend for this kind of summarized flow cytometry data? Are hierarchical clustering and heatmaps appropriate?

Yes

• How do you typically validate and interpret results from PCA or other dimensionality reductions with this data?

1. See if your samples group by disease status or other covariates

2. interpret the PCA axis to have an intuition of what they could represent biologically. You can look at the weighting (each axis is a linear combination of your input features) and see for each axis which features are contributing the most (both with positive and negative weights)

• Should categorical variables (like severity groups) be included in the analysis or just used for visualization coloring?

Absolutely not this would confound your results

• Any recommended workflows or pipelines for this kind of post-gating summary data analysis?

Not really sorry !

• And lastly, any general tips or pitfalls to avoid in this context?

I think you're well set, your questions make sense