2014 UC Davis Proteomics Workshop
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.
Introduction
Tutorial
2014 UC Davis Proteomics Workshop Dmitry Grapov is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Multivariate Data Analysis and Visualization Through Network Mapping
Recently I had the pleasure of speaking about one of my favorite topics, Network Mapping. This is a continuation of a general theme I’ve previously discussed and involves the merger of statistical and multivariate data analysis results with a network.
Over the past year I’ve been working on two major tools, DeviumWeb and MetaMapR, which aid the process of biological data (metabolomic) network mapping.
DeviumWeb– is a shiny based GUI written in R which is useful for:
- data manipulation, transformation and visualization
- statistical analysis (hypothesis testing, FDR, power analysis, correlations, etc)
- clustering (heiarchical, TODO: k-means, SOM, distribution)
- principal components analysis (PCA)
- orthogonal partial least squares multivariate modeling (O-/PLS/-DA)
MetaMapR– is also a shiny based GUI written in R which is useful for calculation and visualization of various networks including:
- biochemical
- structural similarity
- mass spectral similarity
- correlation
Both of theses projects are under development, and my ultimate goal is to design a one-stop-shop ecosystem for network mapping.
In addition to network mapping,the video above and presentation below also discuss normalization schemes for longitudinal data and genomic, proteomic and metabolomic functional analysis both on a pathway and global level.
As always happy network mapping!
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.
Biochemical and structural similarity network of changes in tumor compared to control tissue from the same patient.
Hierarchical clusters (color) mapped to a biochemical and structural similarity network displaying difference before and after drug administration.
Partial correlation network displaying changes in metabolite relationships in response to drug treatment.
Partial correlation network displaying changes in disease and response to drug treatment.
Check out the full presentation below.
Featured Network in Chemical and Engineering News (C&EN)
I am happy to announce the release of MetaMapR (v1.2.0).
New features include:
- An independent module for biological database identifier translations using the Chemical Translation System (CTS)
- a retention time filter for mass spectral connections
- increase in calculation speed
An application of MetaMapR was recently featured in an article in the Nov. 4th 2013 issue of Chemical & Engineering News (C&EN) , 91(44). This tool was used to generate a network of > 1200 metabolites based on enzymatic transformations and structural similarities.
The full article can be found be found here as well as the original image.
Tutorials Covering Biological Data Analysis Strategies
I’ve posted two new tutorials focused on intermediate and advanced strategies for biological, and specifically metabolomic data analysis (click titles for pdfs).
Biological Circuit
Network of relationships between protein and glycan components of human milk. Edge properties show the strength (line width) and direction (color) of correlations (spearmans rho, p<0.0001) between biological molecules which are represented by vertices which display the importance (size) and direction of change (color) in milk components between two experimental groups.
Connecting Lipids
As the title states this is too easy. The hard part is how to decide when enough is enough. Here 1194 lipids tested for changes due to genotype are visualized, connected based on their Spearmans’s correlations (p<1e-3).
Here is the legend based on test between two groups.
Creative Data Solutions 
