When you want to get to know and love your data

Posts tagged “networks

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


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!


Network Mapping Video

Here are a video and slides for a presentation of mine about my favorite topic :


Tutorial- Building Biological Networks

I love networks! Nothing is better for visualizing complex multivariate relationships be it social, virtual or biological.Bionetwork1

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 similarities. Your imagination is the limit.

genotype network

 

network DM

If you are interested check out the presentation below.

Here is all the R code and links to relevant data you will need to let you follow along with the tutorial.

</pre>
#load needed functions: R package in progress - "devium", which is stored on github
source("http://pastebin.com/raw.php?i=Y0YYEBia")
<pre>
# get sample chemical identifiers here:https://docs.google.com/spreadsheet/ccc?key=0Ap1AEMfo-fh9dFZSSm5WSHlqMC1QdkNMWFZCeWdVbEE#gid=1
#Pubchem CIDs = cids
cids # overview
nrow(cids) # how many
str(cids) # structure, wan't numeric 
cids<-as.numeric(as.character(unlist(cids))) # hack to break factor

#get KEGG RPAIRS
#making an edge list based on CIDs from KEGG reactant pairs
KEGG.edge.list<-CID.to.KEGG.pairs(cid=cids,database=get.KEGG.pairs(),lookup=get.CID.KEGG.pairs())
head(KEGG.edge.list)
dim(KEGG.edge.list) # a two column list with CID to CID connections based on KEGG RPAIS
# how did I get this?
#1) convert from CID to KEGG using get.CID.KEGG.pairs(), which is a table stored:https://gist.github.com/dgrapov/4964546
#2) get KEGG RPAIRS using get.KEGG.pairs() which is a table stored:https://gist.github.com/dgrapov/4964564
#3) return CID pairs

#get EDGES based on chemical similarity (Tanimoto distances >0.07)
tanimoto.edges<-CID.to.tanimoto(cids=cids, cut.off = .7, parallel=FALSE)
head(tanimoto.edges)
# how did I get this?
#1) Use R package ChemmineR to querry Pubchem PUG to get molecular fingerprints
#2) calculate simialrity coefficient
#3) return edges with similarity above cut.off

#after a little bit of formatting make combined KEGG + tanimoto edge list
# https://docs.google.com/spreadsheet/ccc?key=0Ap1AEMfo-fh9dFZSSm5WSHlqMC1QdkNMWFZCeWdVbEE#gid=2

#now upload this and a sample node attribute table (https://docs.google.com/spreadsheet/ccc?key=0Ap1AEMfo-fh9dFZSSm5WSHlqMC1QdkNMWFZCeWdVbEE#gid=1)
#to Cytoscape 


You can also download all the necessary materials HERE, which include:

  1. tutorial in powerpoint
  2. R script
  3. Network edge list and node attributes table
  4. Cytoscape file
Happy network making!