# Synthetic Data Generation for the Development of 2D Gel Electrophoresis Protein Spot Models
## 2 Materials and Methods
### Generation of Synthetic Protein Spot Images
#### 2.1.2 Method for Generating the Synthetic Spot Image Dataset  
In this section, the proposed method for synthetic protein spot image generation is described in detail. An overview of the workflow is presented in Figure 3, with an extension in Figure 4, where the first step of the main workflow is explained in more detail. Figure 3: Overview of the generation process of the synthetic protein spot dataset. Outcomes of the workflow steps (image samples) are presented on the side and linked by the curvy dashed lines. The whole data generation workflow may be divided into two basic stages: offline data generation and online data augmentation. The offline stage is more computationally demanding and is performed before training the neural network (NN)-based protein spot model. The online augmentation step is performed during the training process. The whole workflow provides training samples consisting of image pairs: an image corrupted by various noise input and a clean target image. The target images may be of two types, depending on [PERSON]’s defined task: with background (if [PERSON] must learn to reconstruct spots with the background retained) or without background (if [PERSON] must learn to reconstruct spots without the background). The entire image generation process may be split into two separate stages: offline data generation and online data augmentation. Offline data generation is performed before the training of the artificial neural network (ANN, NN)-based protein spot model and includes steps that are more computationally demanding. Online data augmentation is performed during the training process and includes computations that are relatively fast and are more worthwhile to perform on the fly rather than keeping precomputed. So the described stages emerge from the efforts to balance the computational load in such a way that the total computational load (or required time) to develop a neural network model for the spot reconstruction task would be minimal. The dataset of the protein spot synthetic images comprises image pairs of an input image and a target image. The input image is a mixture of one primary spot of protein (the signal) and different numbers of secondary spots, high-frequency noise, and varying background (the noise). Input images should be as similar to real 2 DE images as possible. The target image contains only the signal part of the input image. The target image is one of two types: including the constant background or without the background. These different targets are used for the development and comparison of slightly different spot reconstruction methods, where one is trained to reconstruct spots with background, and the other is trained to reconstruct spots without background (trained to remove the background). The steps of the dataset generation workflow (Figure 3) are explained in more detail below. Figure 4: Overview of the generation process of the protein spot prototype (primary spot). Outcomes of the workflow steps (image samples) are presented on the side and linked by the dashed lines. The color of line indicates the same 1D protein spot edge template in the plots. 1. Creation of the protein spot 2D prototype (primary spot). In this step, a primary protein spot is generated. The details of spot creation are summarized in Figure 4 and discussed in Section 2.1.3. The prototype of the protein spot serves as a seed for generating final spots. The final appearance of the spot is determined during the next step, where geometric transformations are randomly selected and applied. 2. Implement shift, scale, rotate, and warping augmentations. This step creates the final appearance of the primary spot (or the signal), and only the maximum intensity of the spot is randomly scaled during the online augmentation. The remaining steps are used for the generation of the noise component of the 2 DE image sample. At this step, all augmentations that fall under the geometric transformations category are performed.