HIGH CONTENT SCREENING ISSUE 2009 ■ ■ ■ ■ ■ transforms and frequency filtering are also part of the standard ImageJ package. A wide variety of mathematical functions may be easily performed on image pixels. For example, one may add, subtract, multiply, or divide by a constant, perform logarithms, square root, reciprocal, or find the absolute value of a selection of pixels in an image Image Calculator: The “Image Calculator” allows images to be combined using one of several mathematical functions, including add, subtract, multiply, and divide as well as AND, OR, XOR, min, max, and average. This is useful for performing calibrations such as dark subtraction and flat fielding on astronomical images. The image calculator accepts stacks as input, thus a stack of images can be batch reduced Measuring Images: A wide variety of measurements including Centre of Mass, Min/Max Gray Value, Mean Gray Value, Standard Deviation, and Area can be performed on an image or a stack of images. Measurements appear as a table in a popup window executed from node submenu. The measurements can be copied and pasted directly into a spreadsheet where they can be sorted or analysed by KNIME nodes in the same environment Image Display Nodes: Image scale, for example arcminutes per pixel, can easily be set using configuration popup window Results Visualisation: scatter plot, histogram, parallel coordinates, multidimensional scaling, rule plotters Misc: scripting nodes. Microscope image readers Microscope readers offers automated loading of image data from many HCS microscopes or cellular analysis system: Opera, MD Micro, BD Pathway, CellWorx, ArrayScan, ScanR. Those nodes can be also very easily customised. Library reader This node can be used to read RNAi data from an ASCII file or URL location. It can be configured to read in various formats. When you open the node's configuration dialog and provide a filename, it will try to guess the reader's settings by analysing the beginning of the file. Check the results of these settings in the preview table. If the data shown is not correct or an error is reported, you can adjust the settings manually (see below). When the node is executed it reads in the entire file and caches it in a temporary file for faster access by the connected successor nodes. It also stores all possible values it came across for each column. Image viewer Based on a previously developed microscope image converter called Screening Image Browser (SIB) 9 which provided us with a convenient way to view digital microscope slides produced via screen direct from image storage. We were able to read different image formats: TIFF 8 and 16 bit (BD Pathway, MD ImageXpress), FLEX Evotec Technologies, LSM Zeiss, LEI Leica, TIFF ArrayScan, CellWorx. Using this driver we can read images during a screening process in microscope format direct from microscope storage and provide them into pipeline of HC/DC. Image driver in same <strong>time</strong> extract metadata from microscope scan and display those information on image. Nodes for small molecule screening In provided pipeline, system based on Knime, exists a collection of plugins for chemical structure handling. Those nodes can read different file formats with molecules like Mol2, SDF file and creates a column with each molecule in a new row. The molecules' names are used as row IDs as long as they are unique (otherwise an artificial name is created based on the row index). View molecules can be displayed as 3D depictions of molecular structures. Other collection of nodes can wrap all data cells of a column into Smiles cells. This node does not validate the structures but simply marks the strings as being of Smiles type. Node “Generates 2D coordinates” is able to display chemical structure in a 2D viewer. Implementation HC/DC was implemented in Java, which is one of the most widely used languages for platform independent programming. The functional modules are realised as subroutines which are either fully coded in Java or include calls to R, Weka, ImageJ scripts or external programs. The method of coding depends on the statistical complexity and the need for speed. For example, the VSN normalisation method is available within the pipeline as an R package, while a permutation program that calculates P-values is written in Python to achieve a speed increase of several magnitudes compared with the R or Java equivalents. To allow the parallel execution of multiple analysis tasks, HC/DC can run on a cluster and automatically swaps large jobs to idle nodes. No special requirements are necessary to use the service and results can be viewed through a range of web browsers on any major operating system. HC/DC like KNIME is built upon Eclipse, employing its wealth of functionality in a variety of ways. A key concept behind Eclipse is its use of plugins which can be added onto an existing installation to provide additional functionality. The existing installation does not need to know about these extension plugins beforehand, it just has to offer a so-called extension point where other plugins can register themselves and offer additional functionality. At Eclipse's base/core, there is just a small run<strong>time</strong> engine that executes plugins and determines their dependencies. 22 www.europeanpharmaceuticalreview.com
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