In simplest terms, ROC curve measures the quality of a binary classifier based on sorted predictions. The predictions can be on any scale, which means that your data can be used to make a ROC curve as is, or it can be scaled to a [0,1] range which is where most binary classifiers will predict their values.

I will post a short Python code below that will show what I mean. Let's say that your qRT-PCR fold changes are in the range [0,25] and that you have 25 gold standards measurements. I will simulate that by creating 25 random numbers in [0,25] range. These are the numbers I got (rounded to 2 decimal places), though yours will be different each time you run the code.

[12.42 16.92 24.03 19.01 18.19 11.82 5.78 4.09 24.32 15.09 21.28 18.16 11.03 14.01 7.8 13.1 20.78 21.51 7.25 19.3 15.62 9.59 4.8  23.62 21.56]

Let's say that for each of those 25 experiments you have an array of 0 or 1 numbers, where 1s mean true positive and 0s mean true negative. Again, I will make 25 random numbers, which for my experment look like these:

[0 0 1 1 0 0 0 0 1 0 1 1 0 1 1 0 1 0 1 1 0 0 1 1 0]

When you calculate the ROC-AUC score for these two arrays of numbers, it comes out as 0.6730769230769231. Now, you can scale the fold change numbers to be in [0,1] range if you wish, because binary classifiers would typically give you numbers like that. Scaled fold change looked like this in my case:

[0.41 0.63 0.99 0.74 0.7 0.38 0.08 0. 1. 0.54 0.85 0.7 0.34 0.49 0.18 0.45 0.82 0.86 0.16 0.75 0.57 0.27 0.03 0.97 0.86]

As I said before, the scale of measurements/predictions doesn't matter, as the ROC-AUC score for the scaled array of numbers vs. original classes still comes out as 0.6730769230769231

The only thing you need to do is make a column of fold change numberss, and next to each of those numbers enter either 0 or 1 which you should know because this is a gold standard set. That's all the data you need, and then you need something that can plot a ROC curve, for example the roc_curve function in sklearn.

import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler(feature_range=(0, 1))
random_fold = np.random.uniform(low=0, high=25, size=25)
print(np.around(random_fold, 2))
random_class = np.random.randint(low=0, high=2, size=25)
print(random_class)
print(roc_auc_score(random_class, random_fold))
scaled_random_fold = scaler.fit_transform(random_fold.reshape(-1, 1))
print(np.around(scaled_random_fold, 2).flatten())
print(roc_auc_score(random_class, scaled_random_fold))