Multiple Comparison Data Analysis

Do you have several datasets, which need to be analyzed and compared? Then choose the multiple comparison data analysis.

You will get a state of the art data analysis with bioinformatic annotations of the tendencies for each data set including a separate report. You will also get a report comparing the result across all data sets. This will include comparisons of the genes/proteins regulated, unique identifications and bioinformatic annotations.

In the individual data analysis
Each step chosen throughout the data analysis is well described. It also includes a report summarizing the results, tables of the data before and after each analytic step. You also get useful figures in vector-format, designed to be ready for publicizing. 

The analysis includes:

  • Data filtration
  • Detection of unique identification
  • Data transformation and normalization test
  • Outlier estimation
  • Statistical testing 
  • Data visualization
  • 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

Machine learning is utilized to find outliers, adjust p-value in case of low sample size, cluster analysis, and more. Clustering is used to personalized medicine-inspired expanded regulation analysis (if interesting subgroups or sub-studies are implied by clustering, we will do statistical analysis on these).

Bioinformatics is performed using the DAVID and Reactome database.

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