Matthew Baker. Visualisation of Electrophoresis Gels using TEX

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1 Visualisation of Electrophoresis Gels using TEX 1 Visualisation of Electrophoresis Gels using TEX Matthew Baker This paper describes a TEX system for creating interactive PDF files to visualize electrophoresis data. A Perl program processes greyscale electrophoresis images, segmentations derived from these images and computed numerical data to create a hyperlinked document in TEX. This paper describes the steps involved. Two-dimensional electrophoresis is a chemical process for separating proteins 1 in organic matter according to isoelectric charge and molecular mass. The process results in scanned greyscale images in which dark spots of a roughly Gaussian shape against a light background indicate the presence of proteins which reach equilibrium at the corresponding coordinates. The x coordinate represents isoelectric charge in terms of pi and the y coordinate represents molecular mass in kdaltons. Such images are used to map protein compositions in organic material. Applications exist in medical diagnosis and research, genetic engineering and pharmaceutical development. A typical gel electrophoresis analysis (using the system we develop) proceeds as follows. Several gels are first obtained (several gels are necessary to overcome noise and improve reproducibility). These are registered so that they align with each other (the position to which a given protein migrates may vary in different experiments). A single segmentation is then obtained from the registered gels. This segmentation assigns a unique label to each identified spot and labels the spots pixels accordingly. Finally, the segmented spots are quantified by fitting Gaussian kernels to them. Each spot is then characterised by x and y coordinates (relating to charge and mass), relative optical density (relating to relative quantity), standard deviation in the x and y axes, peak intensity and size. The process results in a number of greyscale images, a segmentation bitmap and numerical data. However, such automatic processing is never the final task as results must be analysed, compared with other experiments and annotated. The performance of the algorithm must also be assessed. In this paper, a simple visualisation tool implemented in PDF is described. A Perl program 1 Though 2D electrophoresis can be used to separate other macromolecules such as peptides or nucleic acids, we concentrate on proteins.

2 Visualisation of Electrophoresis Gels using TEX processes the various outputs from the image processing algorithms and creates TEX code using the ConTEXt macros.

2 2 Visualisation of Electrophoresis Gels using TEX processes the various outputs from the image processing algorithms and creates TEX code using the ConTEXt macros. Extensive use is made of the NetPBM tools. Gnuplot is used to produce three-dimensional plots. The Data The process starts with a set of n (unregistered) scanned electrophoresis gels (a typical example is depicted in figure 1). After processing, the following are obtained: n registered gels, a mean gel, a segmentation bitmap, a binary image showing the segmented regions borders, and a text file containing numerical quantification of each spot. Figure 1: A typical scanned electrophoresis gel The registered gels show the greyscale images warped so that corresponding spots are in their mean locations. The images are mutually aligned (at least,

3 Visualisation of Electrophoresis Gels using TEX 3 to the best of the registration algorithm s ability). The mean gel is a pixel-wise mean of the registered gels. Each spot is identified by an integer number and is marked in the segmentation bitmap by assigning this number to each of the spot s pixels (we assume there are no more than spots in an image and therefore the segmentation bitmap can be coded with 16 bits per pixel). This number also appears in the text file to relate the numerical data to the spot. Alongside this number are the various numerical values associated with the spot as described above (x and y coordinate, etc). Also present for each spot is its rectangular bounding box, a measure of the goodness of fit of the fitted Gaussian kernel, represented by a χ 2 value, and a flag indicated whether or not Gaussian fitting was successful. Overview of the visualisation tool The visualisation tool links the above data in the following way: The segmented regions boundaries are superimposed over the registered gels and the mean gel. Each spot in the mean gel is shown cut out of the rest of the image and zoomed. The greyscale values are shown in pink for each pixel that belongs to the spot. Each spot is shown as a 3D wireframe plot. The plot is red for pixels belonging to the segmentation and blue for pixels that do not. This plot appears alongside the zoomed and cropped image (see figure 2). Clicking on a spot in the registered gels or mean gel causes the viewer to jump to the zoomed image and plot for that spot. A menu bar is provided to allow the user to switch between the mean gel, the registered gels and the zoomed spots. An additional navigational tool is provided to allow random access to spots by number. HTML is the normal vehicle for such applications. However, most current HTML viewers do not allow zooming (we require zooming capability because gel images are typically bigger than pixels). They are also somewhat unwieldy because they require many separate files. If a CGI backend is used in an HTML system, it required a web server and the files must reside in a directory from which CGI scripts can be run. A PDF file, on the other hand,

4 4 Visualisation of Electrophoresis Gels using TEX Spot 297 row mean registered 93 spots µ = (290.6, 176.1), σ = (2.2, 1.5), I = 150.2, χ 2 = Spot µ = (320.3, 177.0), σ = (2.4, 1.4), I = 147.2, χ 2 = previous next exit Figure 2: Zoomed view of a spot

5 Visualisation of Electrophoresis Gels using TEX 5 is completely self-contained, allows zooming and supports vector graphics (handy for including plots). Processing the input data The PDF file is produced by a Perl program which writes ConTEXt code and runs TEX. In addition, the Perl program does the following, using Gnuplot and the NetPBM tools: Overlays the segmentation boundary image over each of the greyscale images. Processes the spot bounding boxes so that they do not overlap (this is needed for the image maps). Produces Gnuplot input data and calls Gnuplot for each spot in the text file. Creates colour, cropped versions of the mean gel by overlaying the thresholded segmentation bitmap. These form the zoomed image views. Each of these steps is described below. Overlaying the segmentation boundaries over the greyscale images In addition to the segmentation bitmap, the image processing algorithms produce a binary image showing the segmented regions borders. This is overlayed in red onto each of the greyscale images (the mean image and the registered images) using system calls to the NetPBM tools pnminvert and pnmarith. The pnmtopng tool is used to convert the result to PNG, suitable for inclusion into the ConTEXt file. Creating non-overlapping bounding boxes The greyscale images are made clickable by defining rectangular bounding boxes for each spot. Although segmented spots cannot overlap, their bounding boxes can. In order to create an image map in which rectangular areas act as links, we need non-overlapping regions. The Perl program performs very basic processing to reduce the size of the bounding boxes given by the text file so that they do not overlap. It simply scans the bounding boxes in the file from top to bottom, keeping track of which pixels have already been enclosed in a box. Whenever it encounters a box which encloses a pixel already

6 6 Visualisation of Electrophoresis Gels using TEX labelled as belonging to another box, the current box is reduced in size to prevent the overlap. If every pixel enclosed by the block is already labelled as belonging to another box, the current box is removed. This process can result in very small boxes and can also result in boxes for some spots disappearing altogether. However we have found in practice that only boxes in noisy regions, incorrectly segmented as spots, disappear in this way. An alternative approach would be to subdivide boxes using a quadtree approach. This would result in several boxes for each spot, each linking to the same destination. Producing Gnuplot data The plot surfaces are drawn in two colours: red for pixels belonging to the spot and blue for pixels that do not. The axes are defined such that a fixed area of 3 pixels around the segmented spot is drawn. This is necessary to provide some context for the spot. Often the segmentation algorithm fails to identify all pixels belonging to a spot. The NetPBM tool pnmcrop is used to crop the image and pnmnoraw is used to convert this to plain text. The Perl program then arranges this into rows of x, y and z coordinates. Two data sets are produced: one for the red portion of the plot, one for the blue portion. A limitation of Gnuplot is that data to be plotted as a wireframe must be defined on a rectangular grid with no points missing. This will not necessarily be the case for the portion of the data belonging to the segmented area. We therefore make judicious of Gnuplot s clipping and values outside the z-axis range to display a non-rectangular region of each surface. We instruct Gnuplot to output the plot as encapsulated Postscript and then convert it to PDF using texutil. However, outputting METAPOST code would also be practical. Creating zoomed spots A zoomed-in portion of the image containing each spot is shown alongside the surface plots. Again, the bounding box is extended by 3 pixels in each direction to show the context of the spot. Each pixel belonging to the segmented region is shaded in pink whereas other pixels are given grey values. Note that, because the pixels are coloured differently depending on whether they belong to the spot in question, we cannot simply reuse the same image and clip it differently for each view. Rather, the Perl program must create a

7 Visualisation of Electrophoresis Gels using TEX 7 separate image for each spot. These images are small however and therefore do not result in unreasonably large PDF files. In order to apply the pink tint only to pixels belonging to the spot, we threshold the cropped segmentation to create a mask and then apply this to the cropped greyscale image. Thus we obtain two cropped greyscale images: one showing all pixels within the bounding box and one showing only pixels not within the segmented region. By applying different tints to these images using pgmtoppm and adding them with pnmarith we achieve the desired result. The ConTEXt Code Creating the TEX input is comparatively simple. To simplify nagivational issues, the mean gel image is placed in its own chapter, the registered images in a second chapter and the zoomed spots and plots in a third chapter. Chapters are configured so that the titles are not displayed. Each zoomed spot/plot pair is given its own section. This means all links can be to chapter or section labels. Chapter links are placed in the right edge of the page, along with buttons to move forward and backward a page at a time. The image maps link to the spot sections. They are created easily with commands similar to the following: \startfigure[ref row-mean][row-mean][height=330pt,frame=on] \referring(2.8,1.1)(0.1,0.1)[spot 1] \referring(12.2,1.4)(0.2,0.2)[spot 2]... \stopfigure \placefigure [force] [] {none} {\externalfigure[ref row-mean]} A simple mechanism is provided to allow direct access to each spot by number: a page containing all the spot numbers as active links appears at the beginning of the spots chapter. This is simple and surprisingly usable but inelegant. Therefore we plan instead to introduce a text field in the footer of each page, allowing the user to type in a spot number directly. The future Though the system was designed to serve exclusively as an internal performance analysis tool, plans are already underway to incorporate it into an ex-

8 8 Visualisation of Electrophoresis Gels using TEX isting web-based electrophoresis database and analysis system. This would link it to an SQL database of images and numerical data as well as to live analysis programs. Since ConTEXt can create hyperlinks to different documents, it is possible to link several PDF like the one described above, thus enabling analysts to observe changes in protein composition in different samples. For example, observing the difference between leukæmia in early and advanced stages or observing the effect an antibiotic has on a cell structure. Alternatively it could be used to compare different organisms. Acknowledgements This research is funded by Aventis Research Technologies. Aventis Research Technologies also provided the data used in the preparation of this paper. The image processing programs are developed by Matthew Baker, Harald Busse and Martin Vogt at GMD. Address Matthew Baker GMD German National Research Centre for Information Technology Schloss Birlinghoven St. Augustin Germany matthew.baker@gmd.de

Contents. Basic Visualization Tools. Viewing Tools GNUPLOT 9/30/13. Viewing tools gnuplot Netpbm package ImageMagick Suite ezvizgeneric

Contents. Basic Visualization Tools. Viewing Tools GNUPLOT 9/30/13. Viewing tools gnuplot Netpbm package ImageMagick Suite ezvizgeneric Contents Basic Visualization Tools Tom Logan HPC Specialist Viewing tools gnuplot Netpbm package ImageMagick Suite ezvizgeneric Viewing Tools ghostview views postscript files % gv psfile.ps ImageMagick