I want to use rpart (a R package) to build a decision tree model. The data is a high-dimensional expression matrix, with ~50,000 predictors and ~500 samples. The response is a categorical variable.
I cannot run rpart directly because it is too resource-consuming (the process will get killed by the system). So I have to find a way to reduce the dimension first. There are some RF based FS packages like Boruta and VSURF. But still, running them directly is beyond my resource or time limit.
So is there something I can do to decrease the number of predictors at the beginning? If I can reduce the dimension below 2000, it will be much easier for me to handle. I'm thinking of using ANOVA to eliminate irrelevant variables , but I'm not sure whether it's appropriate.