I love networks! Nothing is better for visualizing complex multivariate relationships be it social, virtual or biological. I recently gave a hands-on network building tutorial using R and Cytoscape to build large biological networks. In these networks Nodes represent metabolites and edges can be many things, but I specifically focused on biochemical relationships and chemical 
Partial least squares projection to latent structures or PLS is one of my favorite modeling algorithms. PLS is an optimal algorithm for predictive modeling using wide data or data with rows << variables. While there is s a wealth of literature regarding the application of PLS to various tasks, I find it especially useful for biological 
Working with wide data is already hard enough, add to this row outliers and things can get murky fast. Here is an example of an anlysis of a wide data set, 24 rows x 84 columns. Using imDEV, written in R, to calculate and visualize a principal components analysis (PCA) on this data set. We find that 
I was interested in modeling the relationship between the power and sample size, while holding the significance level constant (p = 0.05) , for the common two-sample t-Test. Luckily R has great support for power analysis and I found the function I was looking for in the package pwr. To calculate the power for the two-sample T-test 
The chemical similarity network or CSN is a great tool for organizing biological data based on known biochemistry or chemical structural similarity. Here is an example CSN for visualizing metabolomic changes (measured via GC/TOF) due to anaerobic stress in germinating seeds. In this network edges are formed for chemical similarity scores > 75. Node color describes 
The concept is to make a GUI to provide a static and dynamic linking between data and its network representations. Static access will involve making networks based on data and metadata stored in some table or spreadsheet. Dynamic control will provide interactive access to network construction and annotation properties. Together, these will provide rapid generation of information rich networks, based 
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 
The Iris data set is a famous for its use to compare unsupervised classifiers. The goal is to use information about flower characteristics to accurately classify the 3 species of Iris. We can look at scatter plots of the 4 variables in the data set and see that no single variable nor bivariate combination can achieve this. One approach to improve the separation 
Here is an example of a data analysis work flow supported in imDEV. This network visualization was made using CmapTools.
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
