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Category Genomics>Gene Expression Analysis/Profiling/Tools Abstract ArrayMiner® is a set of analysis tools using advanced (genetic) algorithms to reveal the true structure of your gene expression data. Its unique graphical interface gives you an intimate understanding of the analysis and an easy publishing of its results. ArrayMiner main modules consist of a Clustering module and a Classmarking module. Clustering module -- The ArrayMiner clustering module is an analysis tool that detects groups of co-expressed genes. Unlike most other clustering tools, ArrayMiner is a rigorous optimization tool, which means that it finds the best possible clusters, instead of using a simple algorithm and supplying a suboptimal classification. This implies that No important similarity between genes goes unnoticed, and No bogus clusters are produced. Note: ArrayMiner is available as a stand-alone application and can communicate seamlessly with GeneSpring (see G6G Product Number 20003). ArrayMiner algorithm -- Unlike the clustering methods available in most tools based on distances (like K-Means, SOM, etc.), ArrayMiner offers a rigorous statistical approach based on the Gaussian mixture model. A unique additional feature is its capacity to detect outliers (noise that does Not belong to any cluster). ArrayMiner puts a premium on solution quality, insuring that your research time is Not wasted on bogus clusters. The manufacturers' proprietary grouping ‘genetic algorithm’ technology insures that the huge computations are still done in short time. ArrayMiner unique interface -- A rich palette of views helps you gain an intimate knowledge of your data and their structure: Clustering interface -- The main window is fully designed to give you an intuitive feeling of your data. Multiple highly customizable views are accessible, such as, Heat Map; Profile view; and an extremely fast 2D and 3D visualizers. Comparing clusters -- Unlike other tools, ArrayMiner is designed to help you understand the relationship between all your clusters. For this purpose a special interface has been designed to let you in a glance, compare two or more clusters efficiently. Comparing classifications -- The classification compare window lets you analyze the differences between two (2) or more classifications. It lets you for example analyze how the classification behaves when you increase the number of clusters. Or easily compare your clustering result with a known biological classification. Building experiment and clustering trees -- In addition to ArrayMiner's unique data mining algorithm, an experiment tree window has been added. The Experiment tree module gives you an intuitive way to see how your experiments or your clusters match each other. Classmarking module -- offers a very intuitive way of performing class prediction. Thanks to its unique interface and sophisticated algorithm, it gives you the opportunity to easily extract target genes in your experiments. ArrayMiner’s ClassMarker helps scientists answer the following questions: “I have measured gene expression levels in patients with disease A, B, C, D. What, if any, are the genes that can differentiate among the diseases? Do any of the diseases share common molecular phenomena, and what genes are implicated? I also measured gene expression levels in patients I canNot diagnose, what is their diagnosis given that of the other patients?” ClassMarker operates on expression data measured on a number of genes in samples of different classes of cells. A class may be a particular tissue (e.g. brain, muscle, and tumor), a particular disease status (e.g. normal, diseased, a particular stage of a disease), a particular disease (e.g. various types of cancer), etc. The assignment of samples to classes is very easily specified thanks to the rich graphical interface. Provided enough samples of known class are supplied, unclassified samples (samples of unknown class) may also be supplied, in form of additional columns. ClassMarker will attempt to classify those samples into one of the known classes. When the data are read in, various filters can be specified (e.g. min/max expression level, minimal fold change, logarithmic transformation). The impact of the filters on each gene’s expression values is conveniently monitored by the graphical interface. Two (2) types of analysis are available: 1) Identification of marker genes and assessment of their quality by 'cross-validation'. 2) Identification of marker genes and assessment of their quality by 'train-and-test' evaluation, with class prediction for unclassified samples, if any. In both cases, marker genes are identified on the basis of a subset of the classified genes and used to classify the rest of the genes. Cross- validation repeatedly removes one sample, identifies the markers from the rest of the genes, and then classifies the removed sample. Train- and-test identifies markers from all the train samples and then classifies all the test samples. The quality of the markers is assessed by the success in classifying each sample into its proper class. Two (2) classification techniques are available: 1) A voting method. 2) A k-Nearest-Neighbors classification. When classifying with the voting method, it is possible to take into account couples of classes in identification of the markers and the subsequent classification. This allows ClassMarker to identify genes that discriminate two classes against the others, revealing 'families of classes', such as cancer types that share common molecular phenomena. ClassMarker offers a unique graphical interface that allows for a deep analysis of the data. Individual samples and whole classes can be excluded from the analysis; classes can be merged and split into parts, etc. This enables the scientist to test many hypotheses and identify promising target genes in record time. Publishing Tools -- Particular attention has been paid to ArrayMiner's publishing capacities. All data views are 'What You See Is What You Get' (WYSIWYG) exportable in various file formats and in various resolutions. And as of version 5.1, additional modules have been added for 'web publishing'. It is now possible in a single click to generate a 3D java applet in a HyperText Markup Language (HTML) page you can share with your colleagues even if they do Not have ArrayMiner. System Requirements Windows a) Minimal configuration OS Any Win32 flavor of Windows Processor Pentium III Display 1024x768 / 64k colors Memory 128 MB should be enough for small files b) Recommended configuration: OS Windows 2000 or better Processor PIII 800 MHz or better Display 1200x1024 or more / 64k colors Memory 256 MB or more Mouse a mouse with a wheel
a) Minimal configuration OS Mac OS 10.1 Processor any G3 Display 1024x768 / 32k colors Memory 128 MB should be enough for small files b) Recommended configuration: OS Mac OS 10.1 Processor G3 500 MHz Display 1200x1024 or more / 32k colors Memory 256 MB or more Mouse a mouse with a wheel
20 rue de l'Industrie B-1400 Nivelles Belgium Phone +32 (0)67 88 3761 Fax +32 (0)67 88 3688 Commercial: arrayminercom@optimaldesign.com Support: arrayminersupport@optimaldesign.com Information: arrayminerinfo@optimaldesign.com Manufacturer's Web Site www.optimaldesign. com/ArrayMiner/ArrayMiner.htm Price Contact manufacturer G6G Product Number 20147 G6G Manufacturer Number 102037 |
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