When you want to get to know and love your data

Archive for February, 2014

High Dimensional Biological Data Analysis and Visualization


High dimensional biological data shares many qualities with other forms of data. Typically it is wide (samples << variables), complicated by experiential design and made up of complex relationships driven by both biological and analytical sources of variance. Luckily the powerful combination of R, Cytoscape (< v3) and the R package RCytoscape can be used to generate high dimensional and highly informative representations of complex biological (and really any type of) data. Check out the following examples of network mapping in action or view a more indepth presentation of the techniques used below.


Partial correlation network highlighting changes in tumor compared to control tissue from the same patient.

Tissue network cancer


Biochemical and structural similarity network of changes in tumor compared to control tissue from the same patient.

Cancer tissue network


Hierarchical clusters (color) mapped to a biochemical and structural similarity network displaying difference before and after drug administration.

cough syrup network


Partial correlation network displaying changes in metabolite relationships in response to drug treatment.

Treatment response network


Partial correlation network displaying changes in disease and response to drug treatment.

Treatment effects network


Check out the full presentation below.

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Tutorials- Statistical and Multivariate Analysis for Metabolomics


2014 winter LC-MS stats courseI recently had the pleasure in participating in the 2014 WCMC Statistics for Metabolomics Short Course. The course was hosted by the NIH West Coast Metabolomics Center and focused on statistical and multivariate strategies for metabolomic data analysis. A variety of topics were covered using 8 hands on tutorials which focused on:

  • data quality overview
  • statistical and power analysis
  • clustering
  • principal components analysis (PCA)
  • partial least squares (O-/PLS/-DA)
  • metabolite enrichment analysis
  • biochemical and structural similarity network construction
  • network mapping


I am happy to have taught the course using all open source software, including: R, and Cytoscape. The data analysis and visualization were done using Shiny-based apps:  DeviumWeb and MetaMapR. Check out some of the slides below or download all the class material and try it out for yourself.

Creative Commons License
2014 WCMC LC-MS Data Processing and Statistics for Metabolomics by Dmitry Grapov is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Special thanks to the developers of Shiny and Radiant by Vincent Nijs.