Thu Dec  8 18:19:55 PST 2005
 Authors: David J. Reiss, ISB    (c) 2005-2006
          Richard Bonneau, ISB
 http://halo.systemsbiology.org/cmonkey

This code is available for non-profit use only, free to be used and modified. If results derived using this method are published, please cite: Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks by David J Reiss, Nitin S Baliga, Richard Bonneau BMC Bioinformatics 2006, 7:280 (2 June 2006)

Currently supported organisms: halo, hpy, ecoli, yeast. All data and code are provided as R-Data binary "object" files. The source for the functions included in these files are visible using R (http://www.r-project.org) and may be easily updated/overridden. Installation instructions below have only been tested on UNIX-based OSes (Linux, OSX).

INSTALLATION / RUNNING:

1. Download the relevant organism's RData file and place in a directory called "data/"
2. Download the "cMonkey_v1.1_source.RData" and "cMonkey.R" files and place them in the current directory
3. Download (and compile if necessary) MEME and MAST (http://metameme.sdsc.edu/mhmm-download.html)
4. Create links to "meme" and "mast" from within the "progs/" directory.
5. Start R, and in the R environment:

 6. >   install.packages("brlr",repos="http://cran.r-project.org") ## install brlr pkg if not installed
 7. >   load("cMonkey_v1.1_source.RData")  load all cMonkey code
 8. >   run()   enter organism to run on

NOTES: A. Ignore all warning messages printed to the screen - only worry if it dies with an error. B. All output is placed in the output/ORGANISM/ directory, along with PDF files containing bicluster plots and statistics generated during the optimization. Also generated are RData dump files, for re-starting a stopped/killed session and for input to run the Inferelator (regulatory network inference algorithm). C. You may kill/restart a run at any time. To completely re-start a new job, delete the output/ORGANISM directory. D. At any time you may plot a bicluster (e.g. 2) by typing (in R, after running step 7 above):

    >   start.up(); load.latest(); plotCluster.motif( clusterStack[[2]] )

E. Or, to plot them all to a PDF, instead of the "plotCluster.motif()" function, type:

    >   plot.clusters( clusterStack )  PDF is placed in output/ORGANISM

ADVANCED USAGE: if you want to adjust the cMonkey parameters, between steps (7) and (8) above, insert:

 a. >   start.up(); detach( params )   ## enter the organism to run on
 b. *  change the parameter that you wish to modify in the "params" list; e.g.
       > params$kmax <- 100   ## sets the number of clusters to be learned
 c. >   attach( params )

OTHER ORGANISMS: if you wish to use cMonkey on your own data for another organism, you need to inspect the "global.data" data structure, and replace the sub-structures seen there. I will be happy to offer help/suggestions/advice.

PARALELLIZATION: Additional headaches arise if you want run cMonkey parallelized. I have not included the necessary ingredients in this distribution, but I will, if anyone expresses a desire to do so. The extra steps include:

1. installing pvm for your machine
2. setting up pvm-related environment variables
3. installing the rpvm and snow R libraries
4. loading the snow-utils code (not included here)
5. editing the test.opt.all.clusters() function to uncomment the snow-related functions

CONTACT for assistance: David J. Reiss, ISB -- [email protected]