Applicable Areas

  • Data Analysis for the Life Sciences (for Data-driven Life Sciences Research)
    • e.g. Discovery driven Research of massive and complex datasets generated by high-throughput from microarray and NGS

Challenges in High Dimensional Data Analysis

  • Interpreting information extracted from these massive and complex datasets requires sophisticated statistical skills as one can easily be fooled by patterns arising by chance
  • Practical issues with fitting linear models – collinearity, confounding
  • Batch effects

Services we provide

  • Identify Scope for Data-driven Research for the problem statement
  • For high throughput data:
    • Exploratory Data Analysis – to learn when to apply robust statistical techniques
    • Statistical Inference for High Dimensional Data
    • Fitting Complex Statistical Models (Parametric distributions, Hierarchical models, Empirical Bayes techniques)
    • Distance / Dimensionality Reduction
    • Machine Learning (Hierarchical and k-means clustering)