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NYU Microarray Database (NYUMAD)

NYUMAD provides efficient, extensible data analysis functionality for collaborative research. It offers clustering modules, custom calculation tools, and controlled data sharing features. The platform ensures easy data import/export and flexible design, with the use of MAGE-ML standards. Users benefit from tools for clustering analysis, expression profile comparison, and experimental data organization. The software engineering follows a three-tier architecture for optimal performance and scalability.

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NYU Microarray Database (NYUMAD)

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  1. NYU Microarray Database (NYUMAD) Marc Rejali

  2. Overview • Supports collaborative research • Large data sets • Extensible Analysis functionality • Efficient and intuitive interface • Flexible design • Use of standards (MAGE-ML)

  3. Custom Web Pages Java Applet Web pages Java Application Custom Application Clustering Modules Custom Calculation & clustering Modules Calculation Modules Java Servlets DB Relational DB Files Architecture/Design Front Tier XML ( MAGE-ML) Local C or C++ modules on user’s computer HTTP Middle Tier C++, C, Java, other languages Back Tier

  4. Data Sharing • Collaborative Data sharing • Controlled visibility/access • group-based read/write access • public/private data • Community access to public data • HTTP communication • XML and MAGE-ML standard • Easy data import/export • Can create customized data sets • Can filter and transform export data

  5. Array Definition Screen

  6. Experiment/Hybridization Data Screen

  7. Data Analysis 1 • Clustering analysis • Data filtering, normalization, transformation • New ‘shrinkage’ similarity measure • Gene and experiment cluster generation • Graphical display of clustering • Expression profile analysis • Find expression profiles correlated/anti-correlated to a given gene’s expression profile • Compare to a synthetic profile • Data filtering, normalization and transformation available

  8. Data Analysis 2 • Use of fast plug-in C/C++ analysis modules • Sophisticated new technique for normalization and differential expression

  9. Clustering Screen

  10. Data Content • Experiments • calculated and typed experimental factors • templates for factor input • Hybridization results • batch and regular import • Array Design • array feature annotations • User arrays • Protocols

  11. Software Engineering • Three-tier architecture ensures performance, scalability and flexibility • Client and Server caching ensure speed • Re-use demonstrated with NYUSIM

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