I recently gave a presentation of some of my work in network mapping to my research lab. The following covers my progress in the development of my metabolomic network mapping tool MetaMapR, and its application to a variety of data sets including a comparison of normal and malignant lung tissue from the same patient.
November 21, 2013 | Categories: Uncategorized | Tags: biochemical network, chemical similarity network, correlation network, Cytoscape, data analysis, data visualization, Gaussian graphical Markov metabolic network, metabolomics, MetaMapR, multivariate, network, network mapping | Leave a comment
I’ve posted two new tutorials focused on intermediate and advanced strategies for biological, and specifically metabolomic data analysis (click titles for pdfs).
May 29, 2013 | Categories: Uncategorized | Tags: ANCOVA, chemical similarity network, classification, climate, correlation network, covariate adjustment, data analysis, data visualization, Gaussian graphical Markov metabolic network, imDEV, metabolomics, network, PCA, PLS, PLS-DA, R, research, science, TeachingDemos, tutorial | Leave a comment
My new project is coming along nicely and should be released early 2013. It builds on the structures developed in imDEV to link Excel, Cytoscape and R using RExcel, RCytoscape, and CytoscapeRPC . This trio can be used to rapidly generate beautiful and informative network representations of data.
Here is an example of a undirected Gaussian graphical Markov metabolic network calculated from time course metabolomic measurements generated by gas chromatography time-of-flight mass spectrometry (GC/TOF).
Nodes represent metabolomic variables whose characteristics encode chemometric data and the results of statistical analyses and multivariate modeling. Ggplot2 is used to generate graphs of the time course data representing the means and standard error of metaboloite concentrations in two study populations. The connections between nodes or edges are calculated from q-order partial correlations using the R package qpgraph.