This is a supervised learning problem. Ideally would like to work in R due to having an easy way to pre-process the input data, but could work around that as well.
For each sample, input consists of tens of thousands of features. These are genomics data and will likely need to be reduced to a manageable amount, somehow, before being used to train the classifier.
Supervisory signal consists of 4 dependent continuous values, representing relative composition of the sample.
e.g. continuous between 0 and 1, all 4 summing to 1 for each sample:
Sub012 0.5940594 0.26732673 0.07920792 0.059405941 Sub013 0.5102041 0.34693878 0.08163265 0.061224490 Sub014 0.6521739 0.20652174 0.07608696 0.065217391
Wanted: a regression function capable of predicting the relative composition of a sample in terms of those same 4 dependent continuous values.
The constraints on the supervisory signal are what is causing me pause: the dependence of the variables, being constrained between 0-1 and summing to 1. I was hoping someone might have attempted something similar and could point me in the right direction - packages or approaches which may work or definitely won't work - all thoughts welcomed.