Could you please provide me main principles for validation in microarray-based gene expression meta-analysis studies? First of all, I personally think that meta-analysis itself is a validation study. Therefore, I think it is not necessary to perform validation tests for this kind of studies. However, my colleagues suggest me that we still need validation. I had a look around already but I still cannot make up my mind.
- Some papers used RT-PCR to validate their results. (1)
- Some papers used the similarity between their data sets with another large data set. However, I wonder is it better to perform this way than combined them as once meta-analysis data set? (2)
- Some papers divided their data into training set and testing set or used Leave-one-out cross-validation (LOOCV). (3)
Some papers combined (3) and (1). As my understanding, they divide their study into 'statistical validation' and 'experimental validation'. Does it make sense if they conducted studies on human sample and validate by cell line gene expression data?