Characterisation and correction of signal fluctuations in successive acquisitions of microarray images

Background  

There are many sources of variation in dual labelled microarray experiments, including data acquisition and image processing. The final interpretation of experiments strongly relies on the accuracy of the measurement of the signal intensity. For low intensity spots in particular, accurately estimating gene expression variations remains a challenge as signal measurement is, in this case, highly subject to fluctuations.     

Background correction for cDNA microarray images using the TV+L1 model

MOTIVATION: Background correction is an important preprocess in cDNA microarray data analysis. A variety of methods have been used for this purpose. However, many kinds of backgrounds, especially inhomogeneous ones, cannot be estimated correctly using any of the existing methods. In this paper, we propose the use of the TV+L1 model, which minimizes the total variation (TV) of the image subject to an L1-fidelity term, to correct background bias. We demonstrate its advantages over the existing methods by both analytically discussing its properties and numerically comparing it with morphological opening. RESULTS: Experimental results on both synthetic data and real microarray images demonstrate that the TV+L1 model gives the restored intensity that is closer to the true data than morphological opening. As a result, this method can serve an important role in the preprocessing of cDNA microarray data.     

Grayscale representation of infrared microscopy images by extended multiplicative signal correction for registration with histological images

Fourier‐transform infrared (FTIR) microspectroscopy is rounding the corner to become a label‐free routine method for cancer diagnosis. In order to build infrared‐spectral based classifiers, infrared images need to be registered with Hematoxylin and Eosin (H&E) stained histological images. While FTIR images have a deep spectral domain with thousands of channels carrying chemical and scatter information, the H&E images have only three color channels for each pixel and carry mainly morphological information. Therefore, image representations of infrared images are needed that match the morphological information in H&E images. In this paper, we propose a novel approach for representation of FTIR images based on extended multiplicative signal correction highlighting morphological features that showed to correlate well with morphological information in H&E images. Based on the obtained representations, we developed a strategy for global‐to‐local image registration for FTIR images and H&E stained histological images of parallel tissue sections.     

Analysis of cDNA microarray images.

Microarrays are part of a new class of biotechnologies that allow the monitoring of expression levels for thousands of genes simultaneously. Image analysis is an important aspect of microarray experiments, one that can have a potentially large impact on subsequent analyses, such as clustering or the identification of differentially expressed genes. This paper reviews a number of existing image analysis methods used on cDNA microarray data. In particular, it describes and discusses the different segmentation and background adjustment methods. It was found that in some cases background adjustment can substantially reduce the precision--that is, increase the variability of low-intensity spot values. In contrast, the choice of segmentation procedure seems to have a smaller impact.     

Granulometric analysis of spots in DNA microarray images

As the topological properties of each spot in DNA microarray images may vary from one another, we employed granulometries to understand the shape-size content contributed due to a significant intensity value within a spot. Analysis was performed on the microarray image that consisted of 240 spots by using concepts from mathematical morphology. In order to find out indices for each spot and to further classify them, we adopted morphological multiscale openings, which provided microarrays at multiple scales. Successive opened microarrays were subtracted to identify the protrusions that were smaller than the size of structuring element. Spot-wise details, in terms of probability of these observed protrusions,were computed by placing a regularly spaced grid on microarray such that each spot was centered in each grid. Based on the probability of size distribution functions of these protrusions isolated at each level, we estimated the mean size and texture index for each spot. With these characteristics, we classified the spots in a microarray image into bright and dull categories through pattern spectrum and shape-size complexity measures. These segregated spots can be compared with those of hybridization levels.     

Stratification bias in low signal microarray studies

Background  

When analysing microarray and other small sample size biological datasets, care is needed to avoid various biases. We analyse a form of bias, stratification bias, that can substantially affect analyses using sample-reuse validation techniques and lead to inaccurate results. This bias is due to imperfect stratification of samples in the training and test sets and the dependency between these stratification errors, i.e. the variations in class proportions in the training and test sets are negatively correlated.     

Probabilistic segmentation and intensity estimation for microarray images

We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for complementary DNA microarray experiments. The approach overcomes several limitations of existing methods. In particular, it (a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods, including relatively common 'doughnut-shaped' spots; (b) models the image directly as background plus hybridization intensity, and estimates the two quantities simultaneously, avoiding the common logical error that estimates of foreground may be less than those of the corresponding background if the two are estimated separately; and (c) uses a probabilistic modeling approach to simultaneously perform segmentation and intensity estimation, and to compute spot quality measures. We describe two approaches to parameter estimation: a fast algorithm, based on the expectation-maximization and the iterated conditional modes algorithms, and a fully Bayesian framework. These approaches produce comparable results, and both appear to offer some advantages over other methods. We use an HIV experiment to compare our approach to two commercial software products: Spot and Arrayvision.     

Non-linear normalization and background correction in one-channel cDNA microarray studies

MOTIVATION: Data from one-channel cDNA microarray studies may exhibit poor reproducibility due to spatial heterogeneity, non-linear array-to-array variation and problems in correcting for background. Uncorrected, these phenomena can give rise to misleading conclusions. RESULTS: Spatial heterogeneity may be corrected using two-dimensional loess smoothing (Colantuoni et al., 2002). Non-linear between-array variation may be corrected using an iterative application of one-dimensional loess smoothing. A method for background correction using a smoothing function rather than simple subtraction is described. These techniques promote within-array spatial uniformity and between-array reproducibility. Their application is illustrated using data from a study of the effects of an insulin sensitizer, rosiglitazone, on gene expression in white adipose tissue in diabetic db/db mice. They may also be useful with data from two-channel cDNA microarrays and from oligonucleotide arrays. AVAILABILITY: R functions for the methods described are available on request from the author.     

Integration of pre-normalized microarray data using quantile correction

An enormous amount of microarray data has been collected and accumulated in public repositories. Although some of the depositions include raw and processed data, significant parts of them include processed data only. If we need to combine multiple datasets for specific purposes, the data should be adjusted prior to use to remove bias between the datasets. We focused on a GeneChip platform and a pre-processing method, RMA, and examined simple quantile correction as the post-processing method for integration. Integration of the data pre-processed by RMA was evaluated using artificial spike-in datasets and real microarray datasets of atopic dermatitis and lung cancer. Studies using the spike-in datasets show that the quantile correction for data integration reduces the data quality at some extent but it should be acceptable level. Studies using the real datasets show that the quantile correction significantly reduces the bias. These results show that the quantile correction is useful for integration of multiple datasets processed by RMA, and encourage effective use of public microarray data.     

Local mean normalization of microarray element signal intensities across an array surface: quality control and correction of spatially systematic artifacts

Here we present a methodology for the normalization of element signal intensities to a mean intensity calculated locally across the surface of a DNA microarray. These methods allow the detection and/or correction of spatially systematic artifacts in microarray data. These include artifacts that can be introduced during the robotic printing, hybridization, washing, or imaging of microarrays. Using array element signal intensities alone, this local mean normalization process can correct for such artifacts because they vary across the surface of the array. The local mean normalization can be usedfor quality control and data correction purposes in the analysis of microarray data. These algorithms assume that array elements are not spatially ordered with regard to sequence or biological function and require that this spatial mapping is identical between the two sets of intensities to be compared. The tool described in this report was developed in the R statistical language and is freely available on the Internet as part of a larger gene expression analysis package. This Web implementation is interactive and user-friendly and allows the easy use of the local mean normalization tool described here, without programming expertise or downloading of additional software.     

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.     

Use of signal quality measurements to gain efficiency in the analysis of cDNA microarray datacDNA microarray data

This research provides a new way to measure error in microarray data in order to improve gene expression analysis. Microarray data contains many sources of error. In order to glean information about mRNA expression levels, the true signal must first be segregated from noise. This research focuses on the variation that can be captured at the spot level in cDNA microarray images. Variation at other levels, due to differences at the array, dye, and block levels, can be corrected for by a variety of existing normalization procedures. Two signal quality estimates that capture the reliability of each spot printed on a microarray are described. A parametric estimate of within-spot variance, referred to here as σ²_spot, assumes that pixels follow a normal distribution and are spatially correlated. A non-parametric estimate of error, called the mean square prediction error (MSPE), assumes that spots of high quality possess pixels that are similar to their neighbors. This paper will provide a framework to use either spot quality measure in downstream analysis, specifically as weights in regression models. Using these spot quality estimates as weights can result in greater efficiency, in a statistical sense, when modeling microarray data.     

Application of a correlation correction factor in a microarray cross-platform reproducibility study

Background  

Recent research examining cross-platform correlation of gene expression intensities has yielded mixed results. In this study, we demonstrate use of a correction factor for estimating cross-platform correlations.     

Automatic registration of microarray images. II. Hexagonal grid

MOTIVATION: In the first part of this paper the author presented an efficient, robust and completely automated algorithm for spot and block indexing in microarray images with rectangular grids. Although the rectangular grid is currently the most common type of grouping the probes on microarray slides, there is another microarray technology based on bundles of optical fibers where the probes are packed in hexagonal grids. The hexagonal grid provides both advantages and drawbacks over the standard rectangular packing and of course requires adaptation and/or modification of the algorithm of spot indexing presented in the first part of the paper. RESULTS: In the second part of the paper the author presents a version of the spot indexing algorithm adapted for microarray images with spots packed in hexagonal structures. The algorithm is completely automated, works with hexagonal grids of different types and with different parameters of grid spacing and rotation as well as spot sizes. It can successfully trace the local and global distortions of the grid, including non-orthogonal transformations. Similar to the algorithm from part I, it scales linearly with the grid size, the time complexity is O(M), where M is total number of grid points in hexagonal grid. The algorithm has been tested both on CCD and scanned images with spot expression rates as low as 2%. The processing time of an image with about 50 000 hex grid points was less than a second. For images with high expression rates ( approximately 90%) the registration time is even smaller, around a quarter of a second. Supplementary information: http://fleece.ucsd.edu/~vit/Registration_Supplement.pdf     

Automatic registration of microarray images. I. Rectangular grid

MOTIVATION: The analysis of high-throughput experiment data provided by microarrays becomes increasingly more and more important part of modern biological science. Microarrays allow to conduct genotyping or gene expression experiments on hundreds of thousands of test genes in parallel. Because of the large and constantly growing amount of experimental data the necessity of efficiency, robustness and complete automation of microarray image analysis algorithms is gaining significant attention in the field of microarray processing. RESULTS: The author presents here an efficient and completely automatic image registration algorithm (that is an algorithm for spots and blocks indexing) that allows to process a wide variety of microarray slides with different parameters of grid and block spacing as well as spot sizes. The algorithm scales linearly with the grid size, the time complexity is O(M), where M is number of rows x number of columns. It can successfully cope with local and global distortions of the grid, such as focal distortions and non-orthogonal transformations. The algorithm has been tested both on CCD and scanned images and showed very good performance-the processing time of a single slide with 44 blocks of 200 x 200 grid points (or 1 760 000 grid points total) was about 10 s. AVAILABILITY: The test implementation of the algorithm will be available upon request for academics. Supplementary information: http://fleece.ucsd.edu/~vit/Registration_Supplement.pdf     

A hill-climbing approach for automatic gridding of cDNA microarray images

Image and statistical analysis are two important stages of cDNA microarrays. Of these, gridding is necessary to accurately identify the location of each spot while extracting spot intensities from the microarray images and automating this procedure permits high-throughput analysis. Due to the deficiencies of the equipment used to print the arrays, rotations, misalignments, high contamination with noise and artifacts, and the enormous amount of data generated, solving the gridding problem by means of an automatic system is not trivial. Existing techniques to solve the automatic grid segmentation problem cover only limited aspects of this challenging problem and require the user to specify the size of the spots, the number of rows and columns in the grid, and boundary conditions. In this paper, a hill-climbing automatic gridding and spot quantification technique is proposed which takes a microarray image (or a subgrid) as input and makes no assumptions about the size of the spots, rows, and columns in the grid. The proposed method is based on a hill-climbing approach that utilizes different objective functions. The method has been found to effectively detect the grids on microarray images drawn from databases from GEO and the Stanford genomic laboratories.     

Donuts, scratches and blanks: robust model-based segmentation of microarray images

MOTIVATION: Inner holes, artifacts and blank spots are common in microarray images, but current image analysis methods do not pay them enough attention. We propose a new robust model-based method for processing microarray images so as to estimate foreground and background intensities. The method starts with a very simple but effective automatic gridding method, and then proceeds in two steps. The first step applies model-based clustering to the distribution of pixel intensities, using the Bayesian Information Criterion (BIC) to choose the number of groups up to a maximum of three. The second step is spatial, finding the large spatially connected components in each cluster of pixels. The method thus combines the strengths of the histogram-based and spatial approaches. It deals effectively with inner holes in spots and with artifacts. It also provides a formal inferential basis for deciding when the spot is blank, namely when the BIC favors one group over two or three. RESULTS: We apply our methods for gridding and segmentation to cDNA microarray images from an HIV infection experiment. In these experiments, our method had better stability across replicates than a fixed-circle segmentation method or the seeded region growing method in the SPOT software, without introducing noticeable bias when estimating the intensities of differentially expressed genes. AVAILABILITY: spotSegmentation, an R language package implementing both the gridding and segmentation methods is available through the Bioconductor project (http://www.bioconductor.org). The segmentation method requires the contributed R package MCLUST for model-based clustering (http://cran.us.r-project.org). CONTACT: fraley@stat.washington.edu.