# Synthetic Data Generation for the Development of 2D Gel Electrophoresis Protein Spot Models
## 3 Results and Discussion  
This work presents and evaluates a method for the creation of a synthetic dataset of 2 DGE image patches that are needed for the development of 2 DGE protein spot modeling and reconstruction algorithms. The generation of realistic synthetic datasets is becoming an increasingly important task in the field of machine learning (ML). Real data are costly to acquire. There are situations when real data are very scarce, rare, or hard to acquire and use due to privacy issues. The important property of synthetic datasets is that accurate ground-truth labels are available inherently, so it costs nothing to generate labels once the appropriate input images are generated. The increasing capability of generating realistic synthetic data boosts the performance of ML models. The use of purely synthetic data, or a mix of real and synthetic data, is gaining traction, and it is predicted that such uses will overtake the usage of datasets containing only real data. The developed algorithm for protein spot reconstruction, modeling, and parameterization is evaluated in two major aspects. The first is the reconstruction performance of the synthetic and real 2 DGE spot data (Figure 8). The second is the estimation accuracy of the spot key parameters (peak intensity and integrated spot intensity (volume)) (Figure 9). The terms \"reconstruction\", \"modeling\", and \"parameterization\" can be interpreted as follows: reconstruction--the algorithm reconstructs the spot without noise; modeling--the AE algorithm creates an internal representation of the spot (features from the bottleneck layer), and the spot image can be generated from these features; parameterization--the algorithm can predict parameters of the protein spot. To acquire a better view of the algorithm's performance in different situations when dealing with spot image distortions, several experimental setups are prepared. Thus, the new and baseline algorithms for spot modeling are tested in the following situations. * Reconstruction of synthetic images:
* input image may contain only the primary spot or the primary spot surrounded by secondary spots. These cases are marked by \"=1 spot\" or \">1 spot\", respectively, in the graphs of Figure 8a;
* during the model fitting and the evaluation of reconstruction residual error, the mask of the primary spot may or may not be used. The baseline algorithms use a mask to fit a model only in the mask area. AE does not use the mask as additional input data. All algorithms use the mask for residual error evaluation if the mask is provided in a particular experiment. These cases are marked by \"+Mask\" or \"-Mask\", respectively, in the graphs of Figure 8a;
* input images may be corrupted by Gaussian noise (marked by \"+Noise\"), by slowly varying background (marked by \"+Bckg\"), or neither (no additional marking);
* two AE models are trained and evaluated--\"AE-B\" is the model that reconstructs the image with background eliminated and \"AE-B\" reconstructs the image with the constant background. * Reconstruction of real images:
* due to the fact that in real 2 DGE images the ground-truth spots are unknown, the experimental setup is slightly different. The ground-truth images are simulated, in one experimental case, by smoothing original spot images using a median filter (marked by \"\(-\)Noise\" in Figure 8b). In the other case, the ground-truth images are the same, but the input images are generated by adding Gaussian and Salt & Pepper noise to the ground-truth images (noted by \"+Noise\" in the legend);
* during the fitting of the baseline models, the mask of the main spot may or may not be used. The mask is always used when evaluating residual errors. Cases are marked by \"+Mask\" or \"-Mask\", respectively, in the graphs of Figure 8b;
the true background intensity is difficult to estimate, so the AE was used to reconstruct spots with the background (\"AE+B\" algorithm). Figure 8: Protein spot reconstruction results: experiments using synthetic (**a**) and real (**b**) images of protein spots.