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Posts tagged “network mapping

Network Visualization with Plotly and Shiny


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).

netmaping

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)2dnetwork


3-Dimensional Network  (interactive version)

3dnetwork

View all code used to generate the networks above.


Mapping to the MetabolOMIC Manifold


I recently had the pleasure of giving a presentation on one of my favorite topics, network mapping, and its application to metabolomic and genomic data integration. You can check out the full presentation below.


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.

deviuWeb

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

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!

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ASMS 2014


I’ve recently participated in the American Society of Mass Spectrommetry (ASMS) conference and had a great time. I met some great people and have a few new ideas for future projects. Specifically giving a go at using self-organizing maps (SOM) and  the R package mcclust  for clustering alternatives to hierarchical and k-means methods.


I had the pleasure of speaking at the conference in the Informatics-Metabolomics section, and was also a co-author on a project detailing a multi-metabolomics strategy (primary metabolites, lipids, and oxylipins) for the study of type 1 diabetes in an animal model. Keep an eye out for my full talk in an upcoming post.

ASMS 2014 j fahrman


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.

Tissue network cancer


Biochemical and structural similarity network of changes in tumor compared to control tissue from the same patient.

Cancer tissue network


Hierarchical clusters (color) mapped to a biochemical and structural similarity network displaying difference before and after drug administration.

cough syrup network


Partial correlation network displaying changes in metabolite relationships in response to drug treatment.

Treatment response network


Partial correlation network displaying changes in disease and response to drug treatment.

Treatment effects network


Check out the full presentation below.

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Introduction to Network Mapping

name networkNetwork mapping is a high-dimensional data visualization technique which can be applied to virtually any type of data. I recently gave a tutorial on the basics of network mapping where each participants generated a mapped network for their name.

Download the full tutorial at TeachingDemos, and then follow along with the tutorial at your own pace.

 

Happy  network mapping!


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.


C and E figure

The full article can be found be found here as well as the original image.