Differential Analysis of 2D Gel Images. Computer Methods, Part C. Elsevier; 2011. pp. 595-609. [DOI: 10.1016/b978-0-12-381270-4.00021-4]

A custom grow-cut based scheme for 2D-gel image segmentation

This work introduces a novel method for the detection and segmentation of protein spots in 2D-gel images. A multi-thresholding approach is utilized for the detection of protein spots, while a custom grow-cult algorithm combined with region growing and morphological operators is used for the segmentation process. The experimental evaluation against four state-of-the-art 2D-gel image segmentation algorithms demonstrates the superiority of the proposed approach and indicates that it constitutes an advantageous and reliable solution for 2D-gel image analysis.

2D-gel spot detection and segmentation based on modified image-aware grow-cut and regional intensity information

BACKGROUND

Proteomics, the study of proteomes, has been increasingly utilized in a wide variety of biological problems. The Two-Dimensional Gel Electrophoresis (2D-PAGE) technique is a powerful proteomics technique aiming at separation of the complex protein mixtures. Spot detection and segmentation are fundamental components of 2D-gel image analysis but remain arduous and difficult tasks. Several software packages and academic approaches are available for 2D-gel image spot detection and segmentation. Each one has its respective advantages and disadvantages and achieves a different level of success in dealing with the challenges of 2D-gel image analysis. A common characteristic of the available methods is their dependency on user intervention in order to achieve optimal results, a process that can lead to subjective and non-reproducible results. In this work, the authors propose a novel spot detection and segmentation methodology for 2D-gel images.

METHODS

This work introduces a novel spot detection and spot segmentation methodology that is based on a multi-thresholding scheme applied on overlapping regions of the image, a custom grow-cut algorithm, a region growing scheme and morphological operators. The performance of the proposed methodology is evaluated on real as well as synthetic 2D-gel images using well established statistical measures, including precision, sensitivity, and their weighted measure, F-measure, as well as volumetric overlap, volumetric error and volumetric overlap error.

RESULTS

Experimental results show that the proposed methodology outperforms state-of-the-art software packages and methods proposed in the literature and results in more plausible spot boundaries and more accurate segmentation. The proposed method achieved the highest F-measure (94.8%) for spot detection and the lowest volumetric overlap error (8.3%) for the segmentation process.

CONCLUSIONS

Evaluation against state-of-the-art 2D-gel image analysis software packages and techniques proposed in the literature, including Melanie 7, Delta2D, PDQuest and Scimo, demonstrates that the proposed approach outperforms the other methods evaluated in this work and constitutes an advantageous and reliable solution for 2D-gel image analysis.

Spot quantification in two dimensional gel electrophoresis image analysis: comparison of different approaches and presentation of a novel compound fitting algorithm

Background

Various computer-based methods exist for the detection and quantification of protein spots in two dimensional gel electrophoresis images. Area-based methods are commonly used for spot quantification: an area is assigned to each spot and the sum of the pixel intensities in that area, the so-called volume, is used a measure for spot signal. Other methods use the optical density, i.e. the intensity of the most intense pixel of a spot, or calculate the volume from the parameters of a fitted function.

Results

In this study we compare the performance of different spot quantification methods using synthetic and real data. We propose a ready-to-use algorithm for spot detection and quantification that uses fitting of two dimensional Gaussian function curves for the extraction of data from two dimensional gel electrophoresis (2-DE) images. The algorithm implements fitting using logical compounds and is computationally efficient. The applicability of the compound fitting algorithm was evaluated for various simulated data and compared with other quantification approaches. We provide evidence that even if an incorrect bell-shaped function is used, the fitting method is superior to other approaches, especially when spots overlap. Finally, we validated the method with experimental data of urea-based 2-DE of Aβ peptides andre-analyzed published data sets. Our methods showed higher precision and accuracy than other approaches when applied to exposure time series and standard gels.

Conclusion

Compound fitting as a quantification method for 2-DE spots shows several advantages over other approaches and could be combined with various spot detection methods.

The algorithm was scripted in MATLAB (Mathworks) and is available as a supplemental file.

An effective approach for detection and segmentation of protein spots on 2-D gel images

Two-dimensional gel image analysis is widely recognized as a particularly challenging and arduous process in proteomics field. The detection and segmentation of protein spots are two significant stages of this process as they can considerably affect the final biological conclusions of a proteomic experiment. The available techniques and commercial software packages deal with the existing challenges of 2-D gel images in a different degree of success. Furthermore, they require extensive human intervention which not only limits the throughput but unavoidably questions the objectivity and reproducibility of results. This paper introduces a novel approach for the detection and segmentation of protein spots on 2-D gel images. The proposed approach is based on 2-D image histograms as well as on 3-D spots morphology. It is automatic and capable to deal with the most common deficiencies of existing software programs and techniques in an effective manner. Experimental evaluation includes tests on several real and synthetic 2-D gel images produced by different technology setups, containing a total of ∼ 21,400 spots. Furthermore, the proposed approach has been compared with two commercial software packages as well as with two state-of-the-art techniques. Results have demonstrated the effectiveness of the proposed approach and its superiority against compared software packages and techniques.

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.

Region-based Statistical Analysis of 2D PAGE Images

A new comprehensive procedure for statistical analysis of two-dimensional polyacrylamide gel electrophoresis (2D PAGE) images is proposed, including protein region quantification, normalization and statistical analysis. Protein regions are defined by the master watershed map that is obtained from the mean gel. By working with these protein regions, the approach bypasses the current bottleneck in the analysis of 2D PAGE images: it does not require spot matching. Background correction is implemented in each protein region by local segmentation. Two-dimensional locally weighted smoothing (LOESS) is proposed to remove any systematic bias after quantification of protein regions. Proteins are separated into mutually independent sets based on detected correlations, and a multivariate analysis is used on each set to detect the group effect. A strategy for multiple hypothesis testing based on this multivariate approach combined with the usual Benjamini-Hochberg FDR procedure is formulated and applied to the differential analysis of 2D PAGE images. Each step in the analytical protocol is shown by using an actual dataset. The effectiveness of the proposed methodology is shown using simulated gels in comparison with the commercial software packages PDQuest and Dymension. We also introduce a new procedure for simulating gel images.

RegStatGel: proteomic software for identifying differentially expressed proteins based on 2D gel images

Image analysis of two-dimensional gel electrophoresis is a key step in proteomic workflow for identifying proteins that change under different experimental conditions. Since there are usually large amount of proteins and variations shown in the gel images, the use of software for analysis of 2D gel images is inevitable. We developed open-source software with graphical user interface for differential analysis of 2D gel images. The user-friendly software, RegStatGel, contains fully automated as well as interactive procedures. It was developed and has been tested under Matlab 7.01.

AVAILABILITY

The database is available for free at http://www.mediafire.com/FengLi/2DGelsoftware.