MICROARRAY IMAGE ANALYSIS PROGRAM

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1 Revision submitted for publication to Loyola Schools Review, 13 November 2002 MICROARRAY IMAGE ANALYSIS PROGRAM Paul Ignatius D. Echevarria Jerome C. Punzalan John Paul C. Vergara Department of Information Systems and Computer Science School of Science and Engineering Ateneo De Manila University Manila 0917, Philippines Abstract Experiments using microarray technology generate large amounts of image data that are used in the analysis of genetic function. An important stage in the analysis is the determination of relative intensities of spots on the images generated. This paper describes a system that automates this stage, which involves locating the spots in the image, extracting relevant pixel intensities from each spot, and then computing the spot intensity ratios. Keywords: Image analysis, microarray technology, bioinformatics 1. Introduction and Biological Background Genetic research is an area of study that offers much promise. From treating degenerative conditions like Alzheimer s disease to extending the lifespan of humans, research in this area provides us with a wide range of benefits. Microarray technology is a powerful tool used for human genetic research. Its usefulness comes from its ability to collect large amounts of data from gene expression experiments that can later be analyzed and produce useful results. A cell contains within itself all the information necessary to produce the proteins needed to produce the entire organism from which it came from. This information is stored in its genes (when a protein associated with a particular gene is produced by the cell, we say that the gene is expressed). A cell, however, does not actually produce all the proteins of the organism, but only those specific to itself. For example, a muscle cell produces only those proteins which it needs to function as a muscle cell and not as a brain cell. Thus the proteins produced by a cell indicate what genes it expresses and determine what it is and what its functions are. In a microarray experiment, the gene expressions of two cell populations are compared. For example, in an experiment involving normal cells and cancerous cells, microarray technology determines which genes are expressed (or repressed) in each cell population in order to find which genes are responsible for the abnormal condition. Microarray technology does this in several steps [3].

2 First, mrna (messenger RNA, the intermediate molecules derived from DNA used in the production of proteins) is extracted separately from each of the cell populations. Second, cdna (complementary DNA, short single stranded strips of DNA which match the RNA) is synthetically produced from the extracted mrna and then tagged with a different colored fluorescent dye (i.e. green and red) for each cell population. Third, a glass slide is printed with matrices or arrays of spots of identified genes. The spots are made up of identified single stranded strips of DNA whose properties are known and to which matching cdna can bind. Fourth, the tagged cdna of both cell populations are mixed and poured over the microarray glass slide in order to let the cdna bind to the matching DNA spots. Then, the unbound cdna is washed off leaving the bound red and green-tagged cdna on the slide. Some spots have both red and green cdna bound to them, some only green cdna, and others only red cdna. Fifth, the glass slide is put in a dark box and scanned with lasers. A green laser is first used to scan the slide producing a digital image showing the spots that have green-tagged cdna bound to them. After this, a red laser is then used to scan the slide, this time, producing a digital image showing the spots that have red-tagged cdna bound to them. Figure 1 shows superimposed images of a single grid of spots (Figure 6 in the Appendix shows a complete image containing 4 grids). Figure 1. Green and red channels of a microarray grid

3 Sixth, the digital images (each of which can contain several microarray grids) are analyzed. The green spot and red spot images are compared, and, based on the relative intensities of their spots, the expression of genes can be determined. For example, if a spot on the green image is bright and its corresponding spot on the red image is faint, then this means that the gene corresponding to the spot was strongly expressed by the first cell population while it was repressed by the second. Data is expressed in terms of ratios of green and red intensities. Faculty and researchers from Virginia Tech and other collaborators have undertaken a project called Expresso that aims to provide biologists with a problem solving environment tailored to help them analyze their microarray experiments on loblolly pine trees [1, 6]. The Microarray Image Analysis Program presented in this paper demonstrates a limited part of the aforementioned system, specifically the Image Analysis component (see Figure 2). Figure 2. Schematic Design of Expresso: a Problem Solving Environment for Microarray Analysis [1,6]. The system presented here will be compared with ScanAlyze, a preexisting microarray image analysis software system developed by Michael Eisner of Stanford University [5]. ScanAlyze requires the user to first specify the location of the spots in the images, by manually dragging and fitting a grid frame onto the spot grids. Then, based on the coordinates provided by the user, ScanAlyze automatically computes the intensity ratios of the microarray image pair. The system presented in this paper provides a means to eliminate the step of manually locating the spots and to automate most of the image analysis process. 2. The Program The program is called imganalyze and takes, as input, two TIFF (Tagged Image File Format) image files. These files correspond to the green and red channels in a microarray experiment. The program is invoked as follows: imganalyze img1.tif img2.tif -sx rows -sy cols -px prows -py pcols -iout out.tif -vout out.txt

4 where img1.tif and img2.tif are the names of the input image files, rows and cols specify the dimensions of a spot grid, prows and pcols indicate the number of grids (or patches) that exist in the images horizontally and vertically, and, out.tif and out.txt are the names of the output files that contain the gridded, superimposed images and resulting intensities and ratios. The program is written in C and has been tested on the Linux Operating System environment. It uses the Fourier Analysis modules from the CCMATH library developed by Daniel Atkinson [2]. 3. Image Analysis Components The main components of the program are summarized in this section. The program has a central data structure (called MICARRAY) that stores the input image files and intermediate data and results. After loading the images into this data structure, it proceeds with the following steps: a. partitioning into patches b. gridding c. segmentation d. spot ratio intensity computation. In partitioning the images into patches, the program divides the images into regions where each array of spots is found. The currently devised method is an equal grid partitioning of the microarray image according to the specified number of regions. In gridding, the program determines the locations (centers) of the spots in the images. To do this, it first obtains an initial upright grid by applying Fast Fourier Transforms to row and column pixel intensity histograms. This takes advantage of the regular grid pattern in the spot array to determine its location in the image and the distance between spots. After the initial grid is found, it is then rotated to fit the spots more accurately. In segmentation, the program identifies the pixels belonging to a spot and those that belong to its background. This is achieved by first creating a black and white mask for the entire image through thresholding. The mask is then cleaned using certain morphological techniques. Finally, the edge pixels for each spot in the mask are determined, together with its center and radius, thereby delineating spot pixels from background pixels. In spot ratio intensity computation, the median of the background-corrected spot pixel intensities is obtained for each spot and channel, from which a ratio per spot is obtained. This is patterned after the method employed by ScanAnalyze [5]. The authors note that other, more complex segmentation and ratio computation methods have been suggested such as the one described by Chen et al [4]. However, to allow for a more appropriate comparison with ScanAnalyze, particularly with respect to the gridding step, the simpler approach described above was chosen.

5 The general algorithm employed by the program is summarized in Figure 3. The succeeding sections provide further detail for the four steps described above. MICROARRAY IMAGE ANALYSIS ALGORITHM INPUT: OUTPUT: a pair G, R of microarray images spot intensity ratios 1 P = partition_into_patches ( G, R ) - Section 4 2 for each patch in P 3 grid = find_grid ( patch, G, R ) - Section 5 4 binmask = create_mask ( grid, G, R ) - 5 binmask = clean_mask ( binmask ) - Section 6 6 (grid, radii) = compute_centers_and_radii ( grid, binmask, G, R ) - 7 ratios = find_spot_ratios ( grid, radii, G, R ) - Section 7 8 return all ratios for each patch in P Figure 3. Microarray Image Analysis Algorithm 4. Separation into Patches Partitioning the images into patches is done by dividing the image into a grid of equalsized patches. Suppose r represents the number of pixel rows in the image and c represents the number of pixels in a row. Furthermore, suppose an image contains prows rows of patches, with pcols patches for each row. The partitioning step divides the image horizontally and vertically so that each patch will have r/prows rows of pixels each with c/pcols pixels per row. This simple partitioning algorithm was employed in order for the developers to focus on the gridding and segmentation tasks. Efforts to develop a better algorithm are currently underway, to handle cases where the patches are not uniformly placed in the image. 5. Gridding Gridding begins with the original images and patch coordinates for a specific patch. The result of this step is a set of centers for the spots in the patch. Each patch has rows X cols spots, where the values of rows and cols are known (16 and 24, for the test images). The main task in gridding is to determine where the spots begin and end in the patch, both horizontally and vertically. The horizontal and vertical distances between two adjacent spots are fixed, but need to be determined as well. Applying Fast Fourier Transforms (FFTs) to row and column pixelintensity histograms (sums of pixel intensities in a row or column) provide periods that are exactly the values of these distances. FFTs also provide offset values that, in turn, aid in obtaining the actual location of the grid. After an initial upright grid is obtained, a sequence of adjustments is made that involves repeatedly rotating the grid by a small angle until the most appropriate fit for the spots is reached.

6 6. Segmentation The segmentation step distinguishes spot pixels from pixels in the background for each channel, and is achieved by the program in three stages: a. Create a single binary image mask for the two channels. A binary image mask is a matrix of 0-1 (black or white) values. Each entry in the mask corresponds to a pixel in the original images. A black entry means the pixel is part of the spot. The binary image is generated by first getting the mean average pixel intensities of both channels for a particular pixel and then comparing this average against a predefined threshold (determined experimentally). If the average is above the threshold, it is set as black in the mask; otherwise it is set as white. b. Clean up the image using morphological techniques. The binary image is then cleaned by repeatedly applying dilation and erosion operations on the image. Dilation causes all white pixels adjacent to (above, below, to the right of, and to the left of) black pixels to be set to black. Erosion causes all black pixels with at least one white pixel adjacent to it to be set to white. Repeated applications of these operations tend to eliminate isolated black pixels (noise) and refine groups or clusters of black pixels. (See Figure 7 in the Appendix for an example of a cleaned, binary image). c. Determine the edge pixels of each spot in the cleaned binary image and compute a more accurate center and radius for each spot. To determine the edge pixels in a spot in the mask, the program first determines the topmost spot pixel (pixels marked black) above the center given by the gridding step (Section 4). It then determines the coordinates of the edge pixels of the spot by traversing adjacent edge pixels until it reaches the pixel from where it started the trace. A revised center for the spot is then obtained by computing the averages of the leftmost and rightmost edge pixels (for the x coordinate) and the topmost and bottommost pixels (for the y coordinate). A radius, on the other hand, is obtained by computing half of the greater of the differences of these pairs of edge pixels. After the centers and radii are computed, pixels in the images that fall within the circle described by the center and radius obtained as described above, are considered spot pixels for that particular spot. Pixels outside the circle but are within a square two radii long and wide surrounding the spot are considered background pixels of the spot. Figure 4 shows a gridded image (part of the output of the program) that illustrates the results of the gridding and segmentation steps. Note the angled grid and the varying spot centers and radii.

7 Figure 4. Gridded, superimposed images of the patch shown in Figure Spot Ratio Intensity Computation To compute the ratio intensity for each spot, the program distinguishes spot pixels from the background pixels using the circle defined by the centers and radii obtained in the segmentation step (Section 6). A background-corrected pixel ratio is then computed as follows (excerpt taken from ScanAlyze User Manual): While the amount of DNA often varies considerably across a spot, the background corrected pixel ratio should be fairly constant. ScanAlyze utilizes this property in alternate estimates of the ratio and spot quality. One alternate estimate of the ratio for a spot is the median of the set of background corrected single pixel ratios for all (red) pixels within the spot. Unlike pixel intensities, the background corrected pixel ratios are expected to be uniform, and thus the median is a useful value. The exported column MRAT contains the median of Ch2P1 CH2B Ch1P1 CH1B where Ch1PI and Ch2PI represent single spot pixel intensities [and CH1B and CH2B represent median background pixel intensities]. The primary utility of this value is that it is less susceptible to artifacts that can corrupt mean intensity based ratios, such as bright fluorescent specks [5]. The computed ratios provide the main output for the program. 8. Assessment The program was compared with ScanAlyze by having both programs analyze a single patch of a microarray image (the same patch shown in the Figures 1 and 4). Differences between

8 the ratios obtained by both programs were calculated. The ratios obtained using the program are comparable to those obtained using ScanAlyze for spots that were clear and distinct (see Table 1). Table 1. Comparison of ratios for good spots Spot coordinate ScanAylze ratio Program ratio Discrepancy % Discrepancy (column, row) (12, 8) (12, 9) (13, 10) (16, 12) For spots which were very faint and had a lot of noise, notable discrepancies arose among the ratios of ScanAlyze and the program. This can be traced to the disappearance of the spot from the mask due to its faintness, the irregular size or shape of the spot which lead to faulty edge-tracing, and to initial centers which did not fall into the black spots of the mask. Around 60 percent of the spots produced discrepancies greater than 5%, 28 percent produced discrepancies between 1% and 5%, while 12 percent produced discrepancies less than 1% (see Table 2 in the Appendix). The gridding step was validated visually through the gridded, consolidated image produced by the program. Patches that have relatively low stray noise produced accurate grids, although stray bands of light in different places in the image resulted in dislocated grids (see lower left patch in Figure 8 of the Appendix). Although the program is a good start for automatically analyzing microarray images, it still leaves much room for improvement. First, partitioning the image can be improved by analyzing the image first instead of just cutting it up into equal portions (as the array patches might not lie in a neat, centered, and upright grid pattern). Second, determining the grid can be made better by having it handle cases where there are bright strips of light at the edges of the image as these cause the grid to be positioned incorrectly due to its use of histograms. Techniques that discriminate against these stray pixels could be devised. Third, the binary image mask generation algorithm can be enhanced by applying more advanced and more customized morphological techniques to clean the images first before generating the mask. 9. Conclusions The program serves as a good proof of concept that the spot intensities of microarray images can be extracted automatically with minimal human intervention. Future work can be directed towards improvements on the partitioning, gridding and segmentation steps of the process.

9 10. Acknowledgments We would like to thank Dr. Lenwood Heath of Virginia Tech for introducing us to this project and for providing valuable input and discussions, as well as test data. We also acknowledge the efforts of Jeffrey Jongko, who assisted in the documentation of the software. Bibliography [1] R. G. Alscher, B. I. Chevone, L. S. Heath, and N. Ramakrishnan, Expresso - A Problem Solving Environment for Bioinformatics: Finding Answers With Microarray Technology, Proceedings of the High Performance Computing Symposium, Advanced Simulation Technologies Conference (HPC 2001), 2001, [2] D. A. Atkinson, CCMATH. URL: [3] A. M. Campbell. DNA MicroArray Methodology Flash Animation. URL: [4] Y. Chen, E. R. Dougherty, and M. L. Bittner. Ratio-Based Decisions and the Quantitative Analysis of cdna Microarray Images. Journal of BiomedicalOptics. October [5] M. Eisen, ScanAlyze User Manual, Stanford University, [6] L. S. Heath, N. Ramakrishnan, R. R. Sederoff, R. W. Whetten, B. I. Chevone, C. A. Struble, V. Y. Jouenne, D. Chen, L. Merwe van Zyl, and R. Grene, Studying the Functional Genomics of Stress Responses in Loblolly Pine using the Expresso Microarray Management System, Comparative and Functional Genomics 3, 2002,

10 Appendix Figure 6. Superimposed microarray images (green and red channels).

11 Figure 7. Binary image mask produced by the program (coordinates added).

12 Table 2. Table of ratio discrepancies (in %) between the program and Scanalyze for a single grid

13 Figure 8. Results of gridding and segmentation.