We use high-performance computing!

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We use advanced machine learning

to identify patterns.

We find more information in your data!

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Data Analysis

Data Analysis in six steps

Our data analysis is a strictly controlled process consisting of six steps:

  1. Raw Data Processing
  2. Quality Control and Assurance
  3. Outlier Testing
  4. Peak Identification
  5. Metabolite Identification
  6. Metabolite Annotation
Basic Statistical Package

All our metabolomic experiments are combined with a basic statistical package:

  • Differential statistical analysis (ANOVA, t-test, Cruscal-Wallis etc.)
  • Principal Component Analysis (PCA)
  • Hierarchical Cluster Analysis (HCA)
  • Heatmaps
Multivariate Analysis

On top of that we can provide extensive and state-of-the-art multivariate analysis:

  • Partial Least Square Analysis (PLS)
  • Decision Trees
  • Random Forests
  • Artificial Neural Networks (ANN)
  • Support Vector Machine (SVM)
  • Relevance Networks
  • Correlation Analysis
Bioinformatic Support in Data Integration

We can offer bioinformatic support in data integration required for system biology approaches:

  • Integration of transcript/ gene expression and metabolite data (MxT)
  • Integration of protein and metabolite data (MxP)
  • Integration of gene information and metabolite data (MxG)
  • Integration of sensorial profiling and metabolite data (MxS)
  • Flux distribution
  • Genome-wide scale network modelling
Data Storage

In order to assure the management of large data generated by metabolomic experiments we have the best data storage systems in place. Our metabolomics data management system allows us to carry out data analysis, as well as pattern analysis and biomarker identification.

Tag Finder

Benefit from our expertise with TagFinder – The state-of-the-art GC-MS metabolomics software package for the analysis of GC/MS based metabolomics data. Available exclusively through us.

Contact us for more information or to sign up for our next TagFinder workshop.