# Synthetic Data Generation for the Development of 2D Gel Electrophoresis Protein Spot Models
###### Abstract  
Two-dimensional electrophoresis gels (2 DE, 2 DEG) are the result of the procedure of separating, based on two molecular properties, a protein mixture on gel. Separated similar proteins concentrate in groups, and these groups appear as dark spots in the captured gel image. Gel images are analyzed to detect distinct spots and determine their peak intensity, background, integrated intensity, and other attributes of interest. One of the approaches to parameterizing the protein spots is spot modeling. Spot parameters of interest are obtained after the spot is approximated by a mathematical model. The development of the modeling algorithm requires a rich, diverse, representative dataset. The primary goal of this research is to develop a method for generating a synthetic protein spot dataset that can be used to develop 2 DEG image analysis algorithms. The secondary objective is to evaluate the usefulness of the created dataset by developing a neural-network-based protein spot reconstruction algorithm that provides parameterization and denoising functionalities. In this research, a spot modeling algorithm based on autoencoders is developed using only the created synthetic dataset. The algorithm is evaluated on real and synthetic data. Evaluation results show that the created synthetic dataset is effective for the development of protein spot models. The developed algorithm outperformed all baseline algorithms in all experimental cases. two-dimensional gel electrophoresis; 2 DEG; gel image analysis; bioinformatics; protein spot model; spot detection; quantification; synthetic data; autoencoder +
Footnote †: journal: Medical  
the spot identification data, provides insights into the biological processes happening in the source of the protein mixture sample. Analysis of 2 DGE images consists of spot detection and image alignment (if spots are detected after 2 DGE image alignment) or spot matching (if 2 DGE images are aligned after spot detection and spot coordinates are used to find corresponding spots) tasks [22; 23; 24; 25; 26; 27]. The importance of the task of protein spot detection in the 2 DGE images is that the accuracy of the detection information (spot position, area, and integrated intensity) determines the conclusions that are drawn about the biological processes that interest the researchers [28; 29; 30]. In order to maximize the quality of information gathered on the biological processes, the accuracy of the protein spot detection must be maximized. A straightforward approach to improving protein spot quantification (estimation of the spot's integrated intensity) is to develop a better segmentation algorithm that finds the boundaries of the spot and accurately removes noise (background and other intensity distortions) [31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. Spot modeling can accomplish both of these tasks simultaneously. By fitting a mathematical model to the image patch where the spot is located, high-frequency noise can be canceled, and the resulting model can be decomposed into the spot's intensity and background's intensity. The integration of the spot's intensity in the region of the spot provides an estimate of integrated spot intensity. The most common models for modeling protein spots are Gaussian [41; 42; 43; 44] and diffusion [45]. Several other models have been proposed to better approximate spots of more complex shapes [46; 47]. An algorithm for protein spot modeling or reconstruction can be based on machine learning (ML) models [48]. The development of ML model-based image analysis algorithms requires a large amount of labeled training data. The acquisition of high-quality training data has its own costs. The shortcomings of real datasets are: the time-consuming data gathering and labeling process, errors or inaccuracies in label data, a limited number of samples in the dataset, a lack of diversity, difficulties in obtaining rare but crucial corner cases, and uneven representation of the features. These shortcomings may be overcome by creating synthetic datasets [44].