A minimal descriptor of an ancestral recombinations graph

Background

Processing cDNA microarray images is a crucial step in gene expression analysis, since any errors in early stages affect subsequent steps, leading to possibly erroneous biological conclusions. When processing the underlying images, accurately separating the sub-grids and spots is extremely important for subsequent steps that include segmentation, quantification, normalization and clustering.

Results

We propose a parameterless and fully automatic approach that first detects the sub-grids given the entire microarray image, and then detects the locations of the spots in each sub-grid. The approach, first, detects and corrects rotations in the images by applying an affine transformation, followed by a polynomial-time optimal multi-level thresholding algorithm used to find the positions of the sub-grids in the image and the positions of the spots in each sub-grid. Additionally, a new validity index is proposed in order to find the correct number of sub-grids in the image, and the correct number of spots in each sub-grid. Moreover, a refinement procedure is used to correct possible misalignments and increase the accuracy of the method.

Conclusions

Extensive experiments on real-life microarray images and a comparison to other methods show that the proposed method performs these tasks fully automatically and with a very high degree of accuracy. Moreover, unlike previous methods, the proposed approach can be used in various type of microarray images with different resolutions and spot sizes and does not need any parameter to be adjusted.     

Geometric algorithms for the analysis of 2D-electrophoresis gels.

In proteomics, two-dimensional gel electrophoresis (2-DE) is a separation technique for proteins. The resulting protein spots can be identified either by using picking robots and subsequent mass spectrometry or by visual cross inspection of a new gel image with an already analyzed master gel. Difficulties especially arise from inherent noise and irregular geometric distortions in 2-DE images. Aiming at the automated analysis of large series of 2-DE images, or at the even more difficult interlaboratory gel comparisons, the bottleneck is to solve the two most basic algorithmic problems with high quality: Identifying protein spots and computing a matching between two images. For the development of the analysis software CAROl at Freie Universit?t Berlin, we have reconsidered these two problems and obtained new solutions which rely on methods from computational geometry. Their novelties are: 1. Spot detection is also possible for complex regions formed by several "merged" (usually saturated) spots; 2. User-defined landmarks are not necessary for the matching. Furthermore, images for comparison are allowed to represent different parts of the entire protein pattern, which only partially "overlap." The implementation is done in a client server architecture to allow queries via the internet. We also discuss and point at related theoretical questions in computational geometry.     

Simple method for quantification of fast plasma membrane movements.

We present a method for the quantification of the fast plasma membrane movements that are involved in ruffling, blebbing, fast shape change, and fast translocation. The method is based on the Kontron Vidas image analysis computer program. Video images from cells viewed through an inverted microscope were transmitted to the computer. The procedure was as follows: 4 consecutive video images were averaged (image 1); 28 s later a second set of 4 video images was averaged (image 2); image 2 was subtracted from image 1 and the grey level of each pixel of the resulting image was increased with 128 grey level units, resulting in the subtraction image, showing a uniform grey background speckled with brighter and darker spots corresponding to areas of movement. These spots were discriminated and turned into white objects against a black background. Interactive editing was used to delete artefacts that resulted from floating debris. The total area of the discriminated objects was measured, and the parameter motile area in micron2 per cell was calculated. We have applied our method to the study of motility induced in epithelial cell lines by the tumor promoter 12-O-tetradecanoyl-phorbol-13-acetate and by epidermal growth factor.     

A Combinational Clustering Based Method for cDNA Microarray Image Segmentation

Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing. However, they usually face unsatisfactory results because the real microarray image contains noise, artifacts and spots that vary in size, shape and contrast. To improve the segmentation accuracy, in this article we present a combination clustering based segmentation approach that may be more reliable and able to segment spots automatically. First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality. Then, an automatic gridding based on the maximum between-class variance is applied to separate the spots into independent areas. Next, among each spot region, the moving k-means clustering is first conducted to separate the spot from background and then the k-means clustering algorithms are combined for those spots failing to obtain the entire boundary. Finally, a refinement step is used to replace the false segmentation and the inseparable ones of missing spots. In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets. Experiments on six different data sets, 1) Stanford Microarray Database (SMD), 2) Gene Expression Omnibus (GEO), 3) Baylor College of Medicine (BCM), 4) Swiss Institute of Bioinformatics (SIB), 5) Joe DeRisi’s individual tiff files (DeRisi), and 6) University of California, San Francisco (UCSF), indicate that the improved approach is more robust and sensitive to weak spots. More importantly, it can obtain higher segmentation accuracy in the presence of noise, artifacts and weakly expressed spots compared with the other four methods.     

Antibody accumulation in small tissue samples: Assessment by quantitative autoradiography

Uptake of radiolabeled ¹²⁵I monoclonal antibodies in small metastases can only be characterized by autoradiographic techniques. To obtain quantitative data out of autoradiographic images, a transformation of the essentially two-dimensional signal into the Bq per unit volume information is needed. Part of the calibration problem could be solved by using tissue-equivalent standard preparations. However, when aiming at a quantification of radioactivity in small areas (⩽- 2 mm diameter), special criteria had to be expanded upon for the reconstruction of the area in the dose matrix and for the correct integration of the radioactivity content.     

The role of bioinformatics in two-dimensional gel electrophoresis

Over the last two decades, two-dimensional electrophoresis (2-DE) gel has established itself as the de facto approach to separating proteins from cell and tissue samples. Due to the sheer volume of data and its experimental geometric and expression uncertainties, quantitative analysis of these data with image processing and modelling has become an actively pursued research topic. The results of these analyses include accurate protein quantification, isoelectric point and relative molecular mass estimation, and the detection of differential expression between samples run on different gels. Systematic errors such as current leakage and regional expression inhomogeneities are corrected for, followed by each protein spot in the gel being segmented and modelled for quantification. To assess differential expression of protein spots in different samples run on a series of two-dimensional gels, a number of image registration techniques for correcting geometric distortion have been proposed. This paper provides a comprehensive review of the computation techniques used in the analysis of 2-DE gels, together with a discussion of current and future trends in large scale analysis. We examine the pitfalls of existing techniques and highlight some of the key areas that need to be developed in the coming years, especially those related to statistical approaches based on multiple gel runs and image mining techniques through the use of parallel processing based on cluster computing and the grid technology.     

Analysis of the variability of human normal urine by 2D-GE reveals a "public" and a "private" proteome

The characterization of the normal urinary proteome is steadily progressing and represents a major interest in the assessment of clinical urinary biomarkers. To estimate quantitatively the variability of the normal urinary proteome, urines of 20 healthy people were collected. We first evaluated the impact of the sample conservation temperature on urine proteome integrity. Keeping the urine sample at RT or at +4°C until storage at -80°C seems the best way for long-term storage of samples for 2D-GE analysis. The quantitative variability of the normal urinary proteome was estimated on the 20 urines mapped by 2D-GE. The occurrence of the 910 identified spots was analysed throughout the gels and represented in a virtual 2D gel. Sixteen percent of the spots were found to occur in all samples and 23% occurred in at least 90% of urines. About 13% of the protein spots were present only in 10% or less of the samples, thus representing the most variable part of the normal urinary proteome. Twenty proteins corresponding to a fraction of the fully conserved spots were identified by mass spectrometry. In conclusion, a "public" urinary proteome, common to healthy individuals, seems to coexist with a "private" urinary proteome, which is more specific to each individual.     

Object and scene analysis by saccadic eye-movements: an investigation with higher-order statistics

Based on an information theoretical approach, we investigate feature selection processes in saccadic object and scene analysis. Saccadic eye movements of human observers are recorded for a variety of natural and artificial test images. These experimental data are used for a statistical evaluation of the fixated image regions. Analysis of second-order statistics indicates that regions with higher spatial variance have a higher probability to be fixated, but no significant differences beyond these variance effects could be found at the level of power spectra. By contrast, an investigation with higher-order statistics, as reflected in the bispectral density, yielded clear structural differences between the image regions selected by saccadic eye movements as opposed to regions selected by a random process. These results indicate that nonredundant, intrinsically two-dimensional image features like curved lines and edges, occlusions, isolated spots, etc. play an important role in the saccadic selection process which must be integrated with top-down knowledge to fully predict object and scene analysis by human observers.     

Automatic analysis of DNA microarray images using mathematical morphology

MOTIVATION: DNA microarrays are an experimental technology which consists in arrays of thousands of discrete DNA sequences that are printed on glass microscope slides. Image analysis is an important aspect of microarray experiments. The aim of this step is to reduce an image of spots into a table with a measure of the intensity for each spot. Efficient, accurate and automatic analysis of DNA spot images is essential in order to use this technology in laboratory routines. RESULTS: We present an automatic non-supervised set of algorithms for a fast and accurate spot data extraction from DNA microarrays using morphological operators which are robust to both intensity variation and artefacts. The approach can be summarised as follows. Initially, a gridding algorithm yields the automatic segmentation of the microarray image into spot quadrants which are later individually analysed. Then the analysis of the spot quadrant images is achieved in five steps. First, a pre-quantification, the spot size distribution law is calculated. Second, the background noise extraction is performed using a morphological filtering by area. Third, an orthogonal grid provides the first approach to the spot locus. Fourth, the spot segmentation or spot boundaries definition is carried out using the watershed transformation. And fifth, the outline of detected spots allows the signal quantification or spot intensities extraction; in this respect, a noise model has been investigated. The performance of the algorithm has been compared with two packages: ScanAlyze and Genepix, showing its robustness and precision.     

Statistical models of shape for the analysis of protein spots in two-dimensional electrophoresis gel images

In image analysis of two-dimensional electrophoresis gels, individual spots need to be identified and quantified. Two classes of algorithms are commonly applied to this task. Parametric methods rely on a model, making strong assumptions about spot appearance, but are often insufficiently flexible to adequately represent all spots that may be present in a gel. Nonparametric methods make no assumptions about spot appearance and consequently impose few constraints on spot detection, allowing more flexibility but reducing robustness when image data is complex. We describe a parametric representation of spot shape that is both general enough to represent unusual spots, and specific enough to introduce constraints on the interpretation of complex images. Our method uses a model of shape based on the statistics of an annotated training set. The model allows new spot shapes, belonging to the same statistical distribution as the training set, to be generated. To represent spot appearance we use the statistically derived shape convolved with a Gaussian kernel, simulating the diffusion process in spot formation. We show that the statistical model of spot appearance and shape is able to fit to image data more closely than the commonly used spot parameterizations based solely on Gaussian and diffusion models. We show that improvements in model fitting are gained without degrading the specificity of the representation.     

Adaptive color basis transformation. An aid in image segmentation

Multispectral images of stained cells enable the use of color differences to segment and/or to discriminate between image components, such as cell types and cellular subcomponents. When the spectral characteristics of the image components do not change over the area of a slide or from slide to slide, one can create a constant weighted linear combination of spectral images to generate one-dimensional or two-dimensional images that have the desired contrast between the image components that must be discriminated. However, when the spectral characteristics are not constant, i.e., when they vary from image to image, a constant weighted linear combination cannot be employed; instead, an appropriate solution must be found for each selected image. This is usually a time-consuming, manual procedure that cannot be employed in a fully automated process of discriminating and segmenting stained cells. This paper describes an algorithm that uses principal components decomposition basis vectors to generate a nonstatic weighted linear combination of color images that can be used by an automated system. This algorithm relies on a semiconstant relationship between the areas (sizes) of the image components that are to be discriminated and/or segmented. The technique has been successfully applied as an aid in the segmentation of images of stained cervical smears; the images were acquired with a three-chip CCD camera that generates three broad-band color images.     

Using standard positions and image fusion to create proteome maps from collections of two-dimensional gel electrophoresis images

Databases for two-dimensional protein gels pose new challenges in extracting meaningful information from large numbers of experiments. In order to create expression profiles, positions of corresponding protein spots across all gel images have to be established. In larger gel sets errors may accumulate rapidly during this spot matching process, effectively limiting the number of samples available for data mining. Here we present a novel approach for organizing spot data based on the concept of a standard position for a protein species. Standard positions are meaningful average positions that are determined using all occurrences of a protein species. They can be extended to spots that are not annotated via interpolation. The standard position of a spot can serve as a unifying index across all gels in a database, thus allowing creation and analysis of expression profiles that span the whole collection. The standard position gives a much more accurate estimation of a spot's position on a gel than can be obtained using theoretical isoelectric point and molecular weight. Positional indexing is a complement to a priori identifications (e.g. by mass spectrometry or Edman degradation). Moreover it can be used in advance to select spots that are worth identifying because they show relevant expression profiles. Furthermore, we show how to combine all spots that occur on any of the gels into one synthetic but nevertheless realistic-looking image. This composite image is produced such that all spots have their standard positions. It can serve as a proteome reference map for an organism. As an application, we have computed a reference map from 23 gel images of Bacillus subtilis, using an enhanced prerelease version of the gel analysis software Delta2D (DECODON, Greifswald, Germany).     

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.     

Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review

Various methods and specialized software programs are available for processing twodimensional gel electrophoresis(2-DGE)images.However,due to the anomalies present in these images,a reliable,automated,and highly reproducible system for 2-DGE image analysis has still not been achieved.The most common anomalies found in 2-DGE images include vertical and horizontal streaking,fuzzy spots,and background noise,which greatly complicate computational analysis.In this paper,we review the preprocessing techniques applied to 2-DGE images for noise reduction,intensity normalization,and background correction.We also present a quantitative comparison of non-linear?ltering techniques applied to synthetic gel images,through analyzing the performance of the?lters under speci?c conditions.Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size,intensity,and degradation.Three types of noise were added to the images:Gaussian,Rayleigh,and exponential,with signal-to-noise ratios(SNRs)ranging 8–20 decibels(d B).We compared the performanceof wavelet,contourlet,total variation(TV),and wavelet-total variation(WTTV)techniques using parameters SNR and spot ef?ciency.In terms of spot ef?ciency,contourlet and TV were more sensitive to noise than wavelet and WTTV.Wavelet worked the best for images with SNR ranging 10–20 d B,whereas WTTV performed better with high noise levels.Wavelet also presented the best performance with any level of Gaussian noise and low levels(20–14 d B)of Rayleigh and exponential noise in terms of SNR.Finally,the performance of the non-linear?ltering techniques was evaluated using a real 2-DGE image with previously identi?ed proteins marked.Wavelet achieved the best detection rate for the real image.     

Using statistical image models for objective evaluation of spot detection in two-dimensional gels

Protein spot detection is central to the analysis of two-dimensional electrophoresis gel images. There are many commercially available packages, each implementing a protein spot detection algorithm. Despite this, there have been relatively few studies comparing the performance characteristics of the different packages. This is in part due to the fact that different packages employ different sets of user-adjustable parameters. It is also partly due to the fact that the images are complex. To carry out an evaluation, "ground truth" data specifying spot position, shape and intensities needs to be defined subjectively on selected test images. We address this problem by proposing a method of evaluation using synthetic images with unambiguous interpretation. The characteristics of the spots in the synthetic images are determined from statistical models of the shape, intensity, size, spread and location of real spot data. The distribution of parameters is described using a Gaussian mixture model obtained from training images. The synthetic images allow us to investigate the effects of individual image properties, such as signal-to-noise ratios and degree of spot overlap, by measuring quantifiable outcomes, e.g. accuracy of spot position, false positive and false negative detection. We illustrate the approach by carrying out quantitative evaluations of spot detection on a number of widely used analysis packages.     

Proteomic signatures for histological types of lung cancer

We performed proteomic studies on lung cancer cells to elucidate the mechanisms that determine histological phenotype. Thirty lung cancer cell lines with three different histological backgrounds (squamous cell carcinoma, small cell lung carcinoma and adenocarcinoma) were subjected to two-dimensional difference gel electrophoresis (2-D DIGE) and grouped by multivariate analyses on the basis of their protein expression profiles. 2-D DIGE achieves more accurate quantification of protein expression by using highly sensitive fluorescence dyes to label the cysteine residues of proteins prior to two-dimensional polyacrylamide gel electrophoresis. We found that hierarchical clustering analysis and principal component analysis divided the cell lines according to their original histology. Spot ranking analysis using a support vector machine algorithm and unsupervised classification methods identified 32 protein spots essential for the classification. The proteins corresponding to the spots were identified by mass spectrometry. Next, lung cancer cells isolated from tumor tissue by laser microdissection were classified on the basis of the expression pattern of these 32 protein spots. Based on the expression profile of the 32 spots, the isolated cancer cells were categorized into three histological groups: the squamous cell carcinoma group, the adenocarcinoma group, and a group of carcinomas with other histological types. In conclusion, our results demonstrate the utility of quantitative proteomic analysis for molecular diagnosis and classification of lung cancer cells.     

Alignment of two-dimensional electrophoresis gels

Two-dimensional electrophoresis is a major separating technique for proteins in proteomics. Alignment of gel images is critical for intra-laboratory or even more difficult inter-laboratory gel comparisons. In the paper, we propose a novel iterative closest point (ICP) method for 2D-gel electrophoresis image alignment. The paper seeks to introduce an information theoretic measure as one part of distance metric to gel image alignment. We combine intensity information of spots with geometric information of landmarks by applying information potential idea. The proposed method has been applied to both synthetic and real gel images accessible in public 2D-electrophoresis gel protein databases. The high accuracy and robustness of the algorithm indicate that it is promising for gel image alignment.     

Altered proteome profiles in maternal plasma in pregnancies with fetal growth restriction

Fetal growth restriction (FGR) affects 3–5% of pregnancies and is associated with increased perinatal morbidity and mortality. Currently, there is no reliable biochemical test to differentiate a pathological FGR from a nonpathological one. The objective of this study was to screen whole maternal plasma to identify differentially expressed relatively abundant proteins associated with FGR. We analyzed maternal plasma from FGR (n=28) and healthy (n=22) pregnancies using two-dimensional gel electrophoresis (2D-GE) followed by software image analysis. Three spots with molecular weight (M_r) 18 kDa corresponding to haptoglobin (hp) α2, as identified by LC-MS/MS and immunoblotting, showed differential expression patterns in FGR. The distribution of hp α2 variants in maternal plasma samples showed the hp α2 variant 1 was low in 72% of FGR, medium in 16%, whereas high in 12%. In comparison, hp α2 variant 1 was high in (41%) of controls, medium in 41%, and low in 18% of cases. Based on the software image analysis, the mean spot volume for hp α2 variant 1 was 0.12 (SD=0.18) for FGR compared to 0.26 (SD=0.19) for control (p=0.006). Given that hp turnover is indicative of its maturation process and is traceable in plasma by its dominant/suppressed variants, we propose that hp α2 is an important potential target for evaluation of its clinical and pathophysiological role and as a diagnostic biomarker in FGR.     

Image reconstruction of optical computed tomography by using the algebraic reconstruction technique for dose readouts of polymer gel dosimeters

Optical computed tomography (optical CT) has been proven to be a useful tool for dose readouts of polymer gel dosimeters. In this study, the algebraic reconstruction technique (ART) for image reconstruction of gel dosimeters was used to improve the image quality of optical CT. Cylindrical phantoms filled with N-isopropyl-acrylamide polymer gels were irradiated using a medical linear accelerator. A circular dose distribution and a hexagonal dose distribution were produced by applying the VMAT technique and the six-field dose delivery, respectively. The phantoms were scanned using optical CT, and the images were reconstructed using the filtered back-projection (FBP) algorithm and the ART. For the circular dose distribution, the ART successfully reduced the ring artifacts and noise in the reconstructed image. For the hexagonal dose distribution, the ART reduced the hot spots at the entrances of the beams and increased the dose uniformity in the central region. Within 50% isodose line, the gamma pass rates for the 2 mm/3% criteria for the ART and FBP were 99.2% and 88.1%, respectively. The ART could be used for the reconstruction of optical CT images to improve image quality and provide accurate dose conversion for polymer gel dosimeters.