Question: identifying network communities in a protein-protein interaction graph
0
gravatar for Coghlan, Avril
9.9 years ago by
Coghlan, Avril190 wrote:
Dear all, I am wondering is there any function available in Bioconductor or other R libraries for detecting network communities in protein-protein interaction graphs (where a "network community" is defined as a part of a network that has more internal connections than to the rest of the network)? I will be very grateful for any advice. Regards, Avril Avril Coghlan University College Cork, Ireland [[alternative HTML version deleted]]
network • 976 views
ADD COMMENTlink modified 9.9 years ago by Stijn van Dongen80 • written 9.9 years ago by Coghlan, Avril190
Answer: identifying network communities in a protein-protein interaction graph
0
gravatar for Seth Falcon
9.9 years ago by
Seth Falcon7.4k
Seth Falcon7.4k wrote:
On 1/18/10 9:10 AM, Coghlan, Avril wrote: > I am wondering is there any function available in Bioconductor or other > R libraries for detecting network communities in protein-protein > interaction graphs (where a "network community" is defined as a part of > a network that has more internal connections than to the rest of the > network)? You might find relevant packages using these listings: http://bioconductor.org/packages/2.5/GraphsAndNetworks.html http://bioconductor.org/packages/2.5/Proteomics.html In particular, perhaps ppiStats has something of interest to you. + seth -- Seth Falcon Bioconductor Core Team | FHCRC
ADD COMMENTlink written 9.9 years ago by Seth Falcon7.4k
Answer: identifying network communities in a protein-protein interaction graph
0
gravatar for Gilbert Feng
9.9 years ago by
Gilbert Feng300
Gilbert Feng300 wrote:
If you can convert your network to igraph graph object, I think you can do that. Check function "community" in igraph manual page. http://igraph.sourceforge.net/doc/R/community.structure.html Gilbert On 1/18/10 11:10 AM, "Coghlan, Avril" <a.coghlan at="" ucc.ie=""> wrote: > Dear all, > > > > I am wondering is there any function available in Bioconductor or other > R libraries for detecting network communities in protein-protein > interaction graphs (where a "network community" is defined as a part of > a network that has more internal connections than to the rest of the > network)? > > > > I will be very grateful for any advice. > > > > Regards, > > Avril > > > > Avril Coghlan > > University College Cork, Ireland > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor >
ADD COMMENTlink written 9.9 years ago by Gilbert Feng300
Answer: identifying network communities in a protein-protein interaction graph
0
gravatar for Stijn van Dongen
9.9 years ago by
United Kingdom
Stijn van Dongen80 wrote:
Hi All, hi Avril, On Mon, Jan 18, 2010 at 05:10:53PM -0000, Coghlan, Avril wrote: > Dear all, > I am wondering is there any function available in Bioconductor or other > R libraries for detecting network communities in protein-protein > interaction graphs (where a "network community" is defined as a part of > a network that has more internal connections than to the rest of the > network)? Can I just point out that 'network community detection' is exactly the same thing as network clustering, aka graph clustering? I wrote and maintain MCL, a generic cluster algorithm for graphs, which has been used for PPI graphs by various people I believe (see e.g. http://www.biomedcentral.com/1471-2105/7/488/abstract). Someone else mentioned igraph. There is code available in igraph to call MCL externally, see http://igraph.wikidot.com/community-detection-in-python Quite likely they implement other (popular) algorithms as well. Rob Gevers wrote R code to call MCL externally from R: http://www.rob-gevers.com/pmwiki.php/MclR/MclR These two solutions require installing MCL separately from http://micans.org/mcl An important aspect in clustering PPI graphs is, I believe, the quality of the input. I recommend preprocessing it to make sure that there are no nodes of very high degree. One approach is to use a k-nearest neighbour approach, keeping edges only if they are in the list of top k edges (according to similarity) for both of the incident nodes. MCL has additional options to facilitate this kind of processing. Should you be interested, I would be glad to help. I have not used Rob's code myself, but it looks nice in that it is simple, uses the prefered exchange format, and should be easily modifiable to accommodate for example passing of additional MCL parameters. I'd be glad to hear any feedback or suggestions (which I'll try to pass on). best, Stijn -- Stijn van Dongen >8< -o) O< forename pronunciation: [Stan] EMBL-EBI /\\ Tel: +44-(0)1223-492675 Hinxton, Cambridge, CB10 1SD, UK _\_/ http://micans.org/stijn
ADD COMMENTlink written 9.9 years ago by Stijn van Dongen80
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 383 users visited in the last hour