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.
August 9, 2014 | Categories: Uncategorized | Tags: correlation network, Cytoscape, data analysis, data visualization, enrichment, gene ontology, multivariate, network, network enrichment, PCA, PLS, proteomics, r-bloggers, tutorial | Leave a comment
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:
- structural similarity
- mass spectral similarity
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!
June 27, 2014 | Categories: Uncategorized | Tags: biochemical network, chemical similarity network, correlation network, Cytoscape, data analysis, data visualization, DeviumWeb, ggplot2, metabolomics, MetaMapR, multivariate, network mapping, O-PLS, R, r-bloggers, shiny, statistical analysis | 6 Comments
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.
February 22, 2014 | Categories: Uncategorized | Tags: biochemical network, chemical similarity network, clustering, correlation network, Cytoscape, data analysis, data visualization, Devium, metabolomics, multivariate, network, network mapping, O-PLS-DA, r-bloggers, tutorial | Leave a comment
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.
December 25, 2013 | Categories: Uncategorized | Tags: biochemical network, chemical similarity network, chemical translations, correlation network, data visualization, mass spectral similarity, metabolomics, MetaMapR, network mapping | Leave a comment
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
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.