Hello everyone,
I am currently working on modeling and sensitivity analysis using Polynomial Chaos Expansion (PCE) in UQlab. My model has about 180 input variables, many of which are discrete (either 0 or 1). I have several questions and would appreciate any guidance:

Is it feasible to use PCE in UQlab for this problem? If so, how should I handle the discrete variables? What should the
IOpts.Marginals.Type
be set to? Is it suitable to use arbitrary PCE in UQlab? 
Is setting the PCE degree to 2 appropriate given the high number of input variables? Higher degrees might lead to computational difficulties.

How many input samples should I use? Currently, I have set it to 1000 samples due to the high computational cost for each run. Is this sufficient?

I have also been using the method from “Datadriven uncertainty quantification using the arbitrary polynomial chaos expansion” (S. Oladyshkin, W. Nowak, 2012) to construct the orthogonal polynomial basis. Then I employed the
uq_lar
program for sparsity and coefficient calculation. However, the final error(LOO) is quite large. i understand the method is not used in UQlab, but do you have any suggestions on how to improve this?
Thank you for your help!
Best regards,
Harvey