Dear Biostars Community,
I want to address a specific data integration procedure I would like to implement in R/Bioconductor via any relative R package/methodology. In detail, I have acquired clinical data accompanying my gene expression microarray data (which I have preprocessed and analyzed: affymetrix colorectal cancer datasets-60 samples:paired cancer and control samples). In particular, the clinical data represents Positron Emission Tomography(PET) measurements on each sample (of each patient, both cancer and adjacent control), such as SUV(Standardized Uptake Value), Fractal Dimension(FD) and other kinetic parameters[in total 8 "variables"-parameters with continuous measurements(numbers with units). A very small subset of these clinical data just for illustration are presented below (the presented variables are
Parameter Unit Sample_1 Sample_2 Sample_3 SUV 8.085 10.255 3.2744 VB 0.00595 0.063967 0.032291 FD 1.3546 1.3923 1.2349 K1 ml/ml Tiss/min 0.6953 0.4653 0.3942........
Thus, my crucial question is if there is an appropriate methodology implemented in any package in R, in order to perform appropriate integration and subsequent analysis of my gene expression data with the corresponding PET data, in order to search for any interesting correlations or patterns identified? And also to be able to perform any necessary transformation to the above data(maybe scaling or normalization of the above continuous variables, and also removal of any samples with a lot of missing values) .The only package I have noticed is the FactoMineR R package, but as I have no experience in any similar kind of analysis I don't know if could be used for my specific purposes. I insist on R/Bioconductor, because in R I have analyzed my gene expression data, and so I would like to use this platform/language to implement also my above goals.
Any suggestions, comments or help would be beneficial!