Genedata Analyst

Technical Specifications - Genedata Analyst

Genedata Analyst™ is specifically designed to analyze and visualize massive amounts of life science data. It easily integrates data from different experimental platforms. Built on a scalable client-server architecture, Genedata Analyst supports billions of data points and hundreds of users. It puts rigorous statistical algorithms, interactive data analysis tools, and intuitive visualizations into the hands of scientists and data analysts.

Integration of Data from Different Experimental Platforms
Open and Flexible

Genedata Analyst enables researchers to analyze data from different experimental platforms. With applications ranging from biomarker discovery and patient stratification to trait development, Genedata Analyst is a powerful asset in any R&D software portfolio.

Biomarker discovery:

  • Identification of disease markers and prognostic biomarkers

  • Correlation between phenotype information and molecular profiling data

Modeling and prediction:

  • Prediction of compound toxicity (toxicogenomics)

  • Mode-of-action classification

  • Patient stratification and personalized medicine

  • Molecular diagnostics


  • Integration of diverse data through proprietary mapping technology

  • Incorporation of public domain data with proprietary experiments

  • Combination of omics profiling and phenotype data

Statistical Tools
Powerful Data Analysis

Genedata Analyst provides a range of powerful data analysis tools designed to support statistical analysis of even the most complex life science data.

Data normalization:

  • Linear normalization to standardize experiments and remove artifacts
  • Nonlinear normalization methods like LOWESS, Quantile Normalization and Median Polish

General statistics:

  • Data overview and QC using tools like Principal Components Analysis (PCA)
  • Unsupervised clustering and network analyses help facilitate generation of new hypotheses

Statistical tests:

  • Parametric and non-parametric tests
  • Mixed linear model, ANOVA, ANCOVA supporting complex experimental designs
  • Multiple testing corrections controlling false positives from high-throughput experiments
  • Trend identification and time series analyses

Machine learning:

  • Modeling and prediction using tools like Decision Trees, PLS, and Linear Discriminant Analysis
  • Feature selection methods like ANOVA and Recursive Feature Elimination
  • Model validation using leave-one-out and Monte Carlo cross validation

Data Visualization
Transparent Analysis in Real-Time

Genedata Analyst interactive data visualization allows for real-time quality control and result validation. In addition to common charts such as histograms, bar charts, box plots, heat maps, scatter plots, trees, maps, and parallel coordinate plots, Genedata Analyst includes specialized viewers for understanding the results of complex statistical analyses and biological interpretation. All data visualizations are fully interactive and feature shared selections between all graphs to facilitate and support interactive data exploration.

System Requirements
Cost-Efficient Client-Server Solution

Genedata Analyst works with standard server hardware without the need for clusters or grids. This allows seamless and cost-efficient integration into existing IT environments.

Server recommendation:

  • SUSE Linux Enterprise on x64
  • 4 GB of free memory
  • 100 GB of free disk space

Client recommendation:

  • Windows 7 on Intel Core2 or newer
  • 2 GB of RAM
  • 10 GB of free disk space
  • 1280 x 1024 color display

Network configuration:

  • LAN with 100 MB/sec
  • Fixed server IP address
  • Forward and reverse DNS lookups
  • No firewall between client and server