Region-based Statistical Analysis of 2D PAGE Images

Synthetic Data Generation for the Development of 2D Gel Electrophoresis Protein Spot Models

Two-dimensional electrophoresis gels (2DE, 2DEG) 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 2DEG 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.

Decoding 2-D Maps by Autocovariance Function

This chapter describes a mathematical approach based on the study of the 2-D autocovariance function (2-D ACVF) useful for decoding the complex signals resulting from the separation of protein mixtures. The method allows to obtain fundamental analytical information hidden in 2-D PAGE maps by spot overlapping, such as the number of proteins present in the sample and the mean standard deviation of the spots, describing the separation performance. In addition, it is possible to identify ordered patterns potentially present in spot positions, which can be related to the chemical composition of the protein mixture, such as post-translational modifications.The procedure was validated on computer-simulated maps and successfully applied to reference maps obtained from literature sources.

COMPARISON OF TWO ACADEMIC SOFTWARE PACKAGES FOR ANALYZING TWO-DIMENSIONAL GEL IMAGES

One of the key limitations for proteomic studies using two-dimensional (2D) gel is the lack of automatic, fast, robust, and reliable methods for detecting, matching, and quantifying protein spots. Although there are commercial software packages for 2D gel image analysis, extensive human intervention is still needed for spot detection and matching, which is time-consuming and error-prone. Moreover, the commercial software packages are usually expensive and non–open source. Thus, it is very beneficial for researchers to have free software that is fast, fully automatic, and robust. In this paper, we review and compare two recently developed and publicly available software packages, RegStatGel and Pinnacle, for analyzing 2D gel images. These two software packages share some common features and also have some fundamental difference in the aspects of spot detection and quantification. Based on our experience, RegStatGel is much better in terms of spot detection and matching. It also contains more advanced statistical tools and is more user-friendly. In contrast, Pinnacle is quite sensitive to background noise and relies on external statistical software packages for statistical analysis.

Analyzing 2D gel images using a two-component empirical Bayes model

Background

Two-dimensional polyacrylomide gel electrophoresis (2D gel, 2D PAGE, 2-DE) is a powerful tool for analyzing the proteome of a organism. Differential analysis of 2D gel images aims at finding proteins that change under different conditions, which leads to large-scale hypothesis testing as in microarray data analysis. Two-component empirical Bayes (EB) models have been widely discussed for large-scale hypothesis testing and applied in the context of genomic data. They have not been implemented for the differential analysis of 2D gel data. In the literature, the mixture and null densities of the test statistics are estimated separately. The estimation of the mixture density does not take into account assumptions about the null density. Thus, there is no guarantee that the estimated null component will be no greater than the mixture density as it should be.

Results

We present an implementation of a two-component EB model for the analysis of 2D gel images. In contrast to the published estimation method, we propose to estimate the mixture and null densities simultaneously using a constrained estimation approach, which relies on an iteratively re-weighted least-squares algorithm. The assumption about the null density is naturally taken into account in the estimation of the mixture density. This strategy is illustrated using a set of 2D gel images from a factorial experiment. The proposed approach is validated using a set of simulated gels.

Conclusions

The two-component EB model is a very useful for large-scale hypothesis testing. In proteomic analysis, the theoretical null density is often not appropriate. We demonstrate how to implement a two-component EB model for analyzing a set of 2D gel images. We show that it is necessary to estimate the mixture density and empirical null component simultaneously. The proposed constrained estimation method always yields valid estimates and more stable results. The proposed estimation approach proposed can be applied to other contexts where large-scale hypothesis testing occurs.