High Dimensional Biological Data Analysis and Visualization

February 22, 2014

(This article was first published on Creative Data Solutions » r-bloggers, and kindly contributed to R-bloggers)

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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