Tutorial- Building Biological Networks

April 4, 2013
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(This article was first published on imDEV » R, and kindly contributed to R-bloggers)

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.

View this document on Scribd

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!

 


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