I have problems with choosing which model / link function should I use for my analysis.
My response: numbers from -100% to +500% (increase of tumor after therapy, may switch to ratios or log-ratios, so -50% will become 1/2 or -1)
My predictors: numbers N from 0% to 100% which denote "mutation of gene X happened in N% of tumor". The problem is - major part of patients do not have mutations in any particular gene, so for most samples it will be 0%. Data matrix look like: a looot of 0s (more than 95%) and sometimes a number from 0% to 100%. So none of "classic" models (I denote "classic model" as "model that I know") can be applied due to violated assumptions and really not clear distribution of residuals. So it is like "zero inflation in predictors".
I have tried random forest...negative R^2 XD
I have approx 100 genes to investigate and approx 100 samples, if it matters. Could you give any direction on where to look?