Statistics for proteomics: Experimental design and 2-DE differential analysis☆

Two-dimensional gel electrophoresis (2DE) is a powerful technique to examine post-translational modifications of complexly modulated proteins. Currently, spot detection is a necessary step to assess relations between spots and biological variables. This often proves time consuming and difficult when working with non-perfect gels. We developed an analysis technique to measure correlation between 2DE images and biological variables on a pixel by pixel basis. After image alignment and normalization, the biological parameters and pixel values are replaced by their specific rank. These rank adjusted images and parameters are then put into a standard linear Pearson correlation and further tested for significance and variance. We validated this technique on a set of simulated 2DE images, which revealed also correct working under the presence of normalization factors. This was followed by an analysis of p53 2DE immunoblots from cancer cells, known to have unique signaling networks. Since p53 is altered through these signaling networks, we expected to find correlations between the cancer type (acute lymphoblastic leukemia and acute myeloid leukemia) and the p53 profiles. A second correlation analysis revealed a more complex relation between the differentiation stage in acute myeloid leukemia and p53 protein isoforms. The presented analysis method measures relations between 2DE images and external variables without requiring spot detection, thereby enabling the exploration of biosignatures of complex signaling networks in biological systems.

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Correlation analysis of two-dimensional gel electrophoretic protein patterns and biological variables

We present a novel application of independent component analysis (ICA), an exploratory data analysis technique, to two-dimensional electrophoresis (2-DE) gels, which have been used to analyze differentially expressed proteins across groups Unlike currently used pixel-wise statistical tests, ICA is a data-driven approach that utilizes the information contained in the entire gel data We also apply ICA on wavelet-transformed 2-DE gels to address the high dimensionality and noise problems typically found in 2-DE gels Also, we use an analysis-of-variance (ANOVA) approach as a benchmark for comparison Using simulated data, we show that ICA detects the group differences accurately in both the spatial and wavelet domains We also apply these techniques to real 2-DE gels ICA proves to be much faster than ANOVA, and unlike ANOVA it does not depend on the selection of a threshold Application of principal component analysis reduces the dimensionality and tends to improve the performance by reducing the noise

Independent component analysis of 2-D electrophoresis gels

Two-dimensional polyacrylomide gel electrophoresis remains a popular and powerful tool for identifying proteins that are differentially expressed across treatment conditions. Due to the overwhelming number of proteins and the tremendous variation shown in gel images, the differential analysis of 2D gel images is challenging. While commercial software packages are available for such analysis, they require considerable human intervention for spot detection and matching. Moreover, the quantitative comparison across groups of gels is based on simple classical tests that often do not fully account for the experimental design. We developed software with a graphical user interface, RegStatGel, which implements a novel statistical algorithm for identifying differentially expressed proteins. Unlike current commercial software packages, it is free, open-source, easy to use and almost fully automated. It also provides more advanced statistical tools. More importantly, by using a master watershed map, RegStatGel bypasses the spot-matching procedure, which is a time-consuming bottleneck in gel image analysis. The software is freely available for academic use and has been tested in Matlab 7.01 under Windows XP. Detailed instructions on how to use RegStatGel to analyze 2D gel images are provided.

Differential analysis of 2D gel images.

2-DE is a powerful technique to discriminate post-translationally modified protein isoforms. However, all steps of 2-DE preparation and gel-staining may introduce unwanted artefacts, including inconsistent variation of background intensity over the entire 2-DE gel image. Background intensity variations limit the accuracy of gel orientation, overlay alignment and spot detection methods. We present a compact and efficient denoising algorithm that adaptively enhances the image contrast and then, through thresholding and median filtering, removes the gray-scale range covering the background. Applicability of the algorithm is demonstrated on immunoblots, isotope-labeled gels, and protein-stained gels. Validation is performed in contexts of (i) automatic gel orientation based on Hough transformation, (ii) overlay alignment based on cross correlation and (iii) spot detection. In gel stains with low background variability, e.g. Sypro Ruby, denoising will lower the spot detection sensitivity. In gel regions with high background levels denoising enhances spot detection. We propose that the denoising algorithm prepares images with high background for further automatic analysis, without requiring manual input on a gel-to-gel basis.

Adaptive contrast enhancement of two-dimensional electrophoretic protein gel images facilitates visualization, orientation and alignment.

mainly come from computer scientists. The existingmethodologies forgel analysis are mostly in the form of computer algorithms that in many cases have been implemented in software packages developed specificallyfor analyzing twodimensional gel images. Early examples ofautomated gelanalysistechniques can be found in Appel et al. (1991) and references therein. Figure I shows a typical image of a two-dimensional gel. Just by glancing at it, the reader can imagine how hard a task it is for any automated algorithm to accurately identify hundreds of protein spots among the various kinds of noise, and also to compare and match proteins over several gels when presented with multiple copies of gels 300

/pdf/analyzing-two-dimensional-gel-images-1ddtt7s08f.pdf

Analyzing Two-Dimensional Gel Images

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Analyzing Two-Dimensional Gel Images