Dear Mark and Patrick,
thanks a lot for your comments and my apologies for the late reply.
I've contacted the author of the plugin directly and here's his reply.
Unfortunately, the plugin has issues with large datasets so I might
indeed go with Mark's suggestion and load the data into Cytoscape
directyl - without going through R.
All the best, Ignacio
-------------------------------------
Dear Ignacio,
Sorry for taking several days to get back to you.
I just ran some tests in which
1) an adjacency matrix is created in R
2) an R graphNEL is then created from the matrix
3) (optionally) node & edge attributes are added
4) a CytoscapeWindow is created, displayed, and layed out, and
redrawn
(all times are in seconds)
nodes edges attributes? matrix graph cw display
layout redraw total
70 35 no 0.001 0.001 0.5 5.7
2.5 0.016 9.4
70 0 no 0.033 0.001 0.2 4.2
0.5 0.49 5.6
700 350 no 0.198 0.036 6.0 8.3
1.6 0.037 16.7
1000 500 no 0.64 0.07 12.0 9.8
1.8 0.09 24.9
1000 500 yes 0.42 30.99 15.7 29.9
1.7 0.08 79.4
2000 1000 no 3.5 0.30 73.5 14.9
4.8 0.08 96.6
2500 1250 no 2.7 0.45 127.1 18.3
11.5 0.09 160.7
3000 1500 no 4.2 0.46 236.8 19.6
10.7 0.10 272.8
4000 2000 no 8.4 0.98 502.2 27.9
21.4 0.14 561.8
To my complete surprise, and chagrin, there is an exponential slowdown
in 'cw' (the new.CytoscapeWindow method) --which makes no sense at
all. It may be that your memory exhaustion is related to that, and is
quite fixable.
I will explore this, and probably have a fix in the next week.
By the way, did you know that you can create a graphNEL directly from
an adjacency matrix?
g = new ("graphAM", adjMat = matrix, edgemode="directed")
Thanks, Ignacio, for your most helpful report. I should have done
these timing tests long ago!
- Paul
-------------------------------------
On Apr 11, 12:52 pm, Mark McDowall <mark.mcdow...@gmail.com> wrote:
> Hi Patrick
> Seen something similar to this, I was hitting memory issues with very large
> tables.
> Check out:
> http://stat.ethz.ch/R-manual/R-devel/library/base/html/Memory-limits....
> It says that normally you are not able to access vectors that are larger
> than 2Gb. You might want to check out memory.size and memory.limit. I
> fear you may be pushing the limits with what you are trying to load.
> You could try doing a rather dirty dump of the adjacency list and then load
> that in to Cytoscape separately, but then you loose the R capabilities.
> Hope this helps,
> Mark
> On 11 April 2012 10:47, Patrick Burns <patr...@burns-stat.com> wrote:
> > My guess is that your question is much
> > too specialised for this list. I'm not
> > sure if there is a specific R mailing
> > list that is appropriate -- you might
> > have a look at the list of them.
> > If you don't find a specific list that
> > looks appropriate, then you could try
> > mailing to R-help. You should include
> > information on your machine: the platform,
> > how much RAM you have, etc.
> > Good luck,
> > Pat
> > On 11/04/2012 09:04, Ignacio Perez Hallerbach wrote:
> >> Hi everyone,
> >> I'm relatively new to R and am trying to solve the following problem:
> >> I work on a Windows 7 Enterprise platform with the 32bit version of R
> >> and have about 3GB of RAM on my machine. I have large-scale social
> >> network data (c. 7,000 vertices and c. 30,000 edges) which are
> >> currently stored in my SQL database. I have managed to pull this data
> >> (omitting vertex and edge attributes) into an R dataframe and then
> >> into an igraph object. For further analysis and visualization, I would
> >> now like to push this igraph into Cytoscape using RCytoscape.
> >> Currently, my approach is to convert the igraph object into an
> >> graphNEL object since RCytoscape seems to work well with this object
> >> type. (The igraph plotting functions are much too slow and lack
> >> further analysis functionality.)
> >> Unfortunately, I always run into memory issues when running this
> >> script. It has worked previously with smaller networks though.
> >> Does anyone have an idea on how to solve this issue? Or can you
> >> recommend any other visualization and analysis tools that work well
> >> with R and can handle such large-scale data?
> >> Any help would be much appreciated. Thanks so much in advance!
> >> Best, Ignacio
> >> PhD student
> >> University of Cambridge
> > --
> > Patrick Burns
> > patr...@burns-stat.com
> >http://www.burns-stat.com
> >http://www.portfolioprobe.com/**blog<http://www.portfolioprobe.com/blog>
> > twitter: @portfolioprobe
