Cluster analysis

An insight into your data clusters


Cluster analysis


Do you expect there to be potentially relevant clusters in your data? In the cluster analysis, we utilize supervised and unsupervised clustering methods to find the clusters in your data to identify potential sub-groups or sub-studies. Then we run the individual analysis with and without cluster-defined grouping/separation of your data. This allows you to address your data on several levels.

Why is this relevant?

Most diseases are heterogeneous, which can be addressed to some level by clustering if sub-groups are affecting the measurement or quantifications at a high enough level. Response treatment has also been shown heterogeneous in several different drug-studies, and clustering can help to address this.



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Basic data analysis included


If you like, we can also implement data analysis with bioinformatic annotations of the tendencies found in your data. And you will, of course, get all features from our Basic Data Analysis in the Clustering Analysis.

The analysis includes:


  • Data filtration
  • Detection of unique identification
  • Data transformation and normalization test
  • Outlier estimation
  • Statistical testing
  • Bioinformatics enrichment analysis of
    • Cellular component
    • Molecular functions
    • Biological processes
    • Pathways
  • Bioinformatic investigation of potential crosslinking identifications in
    • Cellular component
    • Molecular functions
    • Biological processes
    • Pathways
  • Data visualization (Heatmap, PCA plot and Box-plots of regulations)
  • Bioinformatic visualization (Dot-plots)

  • Delivery time:

    3 - 4 weeks. Fast delivery available.


    Technology used

    Machine learning is utilized to find outliers, visualize data, and bioinformatics. The latter is performed using the DAVID and Reactome database.

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