Question: Appropriate To Use Roc To Evaluate Validation Of Microarrays With Qpcr?
gravatar for Adam Cornwell
6.3 years ago by
Adam Cornwell420
United States
Adam Cornwell420 wrote:


Somewhere before I think I have seen ROC used as a method of evaluating the success of validating a microarray experiment via qPCR. It seems like this could work, if you view the PCR results as a source of true positives/true negatives. That's not necessarily the case though, and so I have some reservations about apply ROC in such a manner. Does it make sense to use it that way? I typically just look at correlations between the array and the qPCR results, but AUC from ROC would similarly be a nice summary statistic.


microarray pcr • 1.5k views
ADD COMMENTlink modified 5.4 years ago by Biostar ♦♦ 20 • written 6.3 years ago by Adam Cornwell420

I don't think it's a good idea to lose information by forcing a continuous variable to a true/false. What is the problem with using correlation?

ADD REPLYlink written 6.3 years ago by brentp23k

In the end though, expression arrays are used for hypothesis testing. It would be interesting to generate a surface representing the ROC over the space of p-value and absolute fold change.

At first glance, I don't think there would be anything out of normal you would have to do. Pick a set of genes and assign them to the classes "changed" and "unchanged" given your qPCR data, this is your control set. Go, back to the microarray data and make calls on the same genes. Calculate the TP/FP/TN/FN rates. Repeat this a few times across a range of thresholds, and you have your ROC data.

ADD REPLYlink written 6.3 years ago by pld4.8k
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