Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com
The following presentation also shows a sneak peak of a new data analysis visualization software, DAVe: Data Analysis and Visualization engine. Check out some early features. DAVe is built in R and seeks to support a seamless environment for advanced data analysis and machine learning tasks and biological functional and network analysis.
As an aside, building the main site (in progress) was a fun opportunity to experiment with Jekyll, Ruby and embedding slick interactive canvas elements into websites. You can checkout all the code here https://github.com/dgrapov/CDS_jekyll_site.
What is the highest dimensional visualization you can think of? Now imagine it being interactive. The following details a Frankenstein visualization packing a smorgasbord of multivariate goodness.
Enter first, self-organizing maps (SOM). I first fell into a love dream with SOMs after using the kohonen package. The wines data set example is a beautiful display of information.
Eloquently, making the visualization above is relatively easy. SOM is used to organize the data into related groups on a grid. Hierarchical cluster analysis (HCA) is used to classify the SOM codes into three groups.
HCA cluster information is mapped to the SOM grid using hexagon background colors. The radial bar plots show the variable (wine compounds’) patterns for samples (wines).
The goal for this project was to reproduce the kohonen.plot using ggplot2 and make it interactive using shiny.
The main idea was to use SOM to calculated the grid coordinates, geom_hexagon for the grid packing and any ggplot for the hexagon-inset sub plots. Some basic inset plots could be bar or line plots.
Part of the beauty is the organization of any ggplot you can think of (optionally grouping the input data or SOM codes) based on the SOM unit classification.
A Pavlovian response might be; does it network?
Yes we can (network). Above is an example of different correlation patterns between wine components in related groups of wines. For example the green grid points identify wines showing a correlation between phenols and flavanoids (probably reds?). Their distance from each other could be explained (?) by the small grid size (see below).
The next question might be, does it scale?
There is potential. The 4 x 4 grid shows radial bar plot patterns for 16 sub groups among the 3 larger sample groups. The next next 6 x 6 plot shows wine compound profiles for 36 ~related subsets of wines.
A useful side effect is that we can use SOM quality metrics to give us an extra-dimensional view into tuning the visualization. For example we can visualize the number of samples per grid point or distances between grid points (dissimilarity in patterns).
This is useful to identify parts of the somClustPlot showing the number of mapped samples and greatest differences.
One problem I experienced was getting the hexagon packing just right. I ended making controls to move the hexagons ~up/down and zoom in/out on the plot. It is not perfect but shows potential (?) for scaffolding highly multivariate visualizations? Some of my other concerns include the stochastic nature of SOM and the need for som random initialization for the embedding. Make sure to use it with set.seed() to make it reproducible, and might want to try a few seeds. Maybe someone out there knows how to make this aspect of SOM more robust?
I have an unnatural obsession with 4-dimensional networks. It might have started with a dream, but VR might make it a reality one day. For now I will settle for 3D networks in Plotly.
Presentation: R users group (more)
R users: networkly: network visualization in R using Plotly
In addition to their more common uses, networks can be used as powerful multivariate data visualizations and exploration tools. Networks not only provide mathematical representations of data but are also one of the few data visualization methods capable of easily displaying multivariate variable relationships. The process of network mapping involves using the network manifold to display a variety of other information e.g. statistical, machine learning or functional analysis results (see more mapped network examples).
The combination of Plotly and Shiny is awesome for creating your very own network mapping tools. Networkly is an R package which can be used to create 2-D and 3-D interactive networks which are rendered with plotly and can be easily integrated into shiny apps or markdown documents. All you need to get started is an edge list and node attributes which can then be used to generate interactive 2-D and 3-D networks with customizable edge (color, width, hover, etc) and node (color, size, hover, label, etc) properties.
2-Dimensional Network (interactive version)
3-Dimensional Network (interactive version)
Recently I had the pleasure of teaching data analysis at the 2014 UC Davis Proteomics Workshop. This included a hands on lab for making gene ontology enrichment networks. You can check out my lecture and tutorial below or download all the material.
2014 UC Davis Proteomics Workshop Dmitry Grapov is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.