Medical Systems Biology

You are here:  Software > VennMaster > 3. Appendix

3. Appendix

1. Generating GoMiner summary export (.se) and gene-category export files (.gce)

After processing the data with GoMiner (http://discover.nci.nih.gov/gominer)the summary export (.se) and category-genes export files (.gce) have to be exported via the middle panel of the GoMiner graphical user interface (GUI). The desired root category node needs to be selected by a single left click, and the context menu activated by a single right click. The two menu items

  • Export summary data to text file- creates the summary export (.se) file

  • Export genes by category- creates the gene-category export (.gce) file

should be selected this way to save the .se and .gce files to disk.

2. The Parameter Dialog

Optimization and visualization parameters can be configured via the parameter dialog (menu Options/Options).

A. Visualization/Global Options

 

size factor
(default 1.0)
Can be adapted to grow/shrink the polygons to fit better in the bounding box which may occur if the intersections between sets is very small (so the diagram is too wide for the bounding box - reduce e.g. to 0.7 in those cases).
number of edges
(default 16)
How many edges should the polygons have (>=3)
Seed
(default 173)
The random seed value ensures reproducible results (for one software version). Other solutions are found if this value is changed. For negative values the current system time is used as random seed.
Update interval
(default 10)
The number of optimization steps after which the currently best solution is shown while the optimization is running.
Max categories
(default 10)
Exceeding the maximum number of categories shows a warning message. Raise this value to suppress warnings. Higher number of categories may need more computational power.
Log2 #elements Activate this checkbox to transform the number of elements to logarithmic scale. For consistency we set log(0)→log(1)→log(2)→1.
Color mode on Toggle color mode on/off.
B. Error Function
Error Function Options
error function type
(default 1)
0=old error function, 1=new error function
max intersections
(default 6)
The maximum number of sets which will observed when evaluating the error function
alpha
(default 10.0)
Error function weight for unwanted intersection
beta
(default 20.0)
Error function weight for missing intersections
gamma
(default 5.0)
 
delta
(default 400.0)
Pressure term for more compact solutions
min scale
(default 1.0)
Allow changes of the polygon radii (experimental).
max scale
(default 1.0)
Allow changes of the polygon radii (experimental).
C. Optimization Options

Different optimizers can be chosen in the Optimizer panel (The Evolutionary newis experimental).

i) Evolutionary Optimization
Evolutionary Optimization Options

The mutation parameters (like min mutation and max mutation) behave relative to the bounding box where the individuals "live". So a mutation rate of 0.2 can move an polygon 1/5-th of the total bounding box. 

tau
(default 1.0)
Mutation basis parameter (mutation of the mutation parameter)
min mutation
(default 0.01)
Lower bound for the mutation rate
max mutation
(default 0.2)
Upper bound for the mutation rate
generation size
(default 30)
How many individuals are in the generation (25-100 individuals are reasonable).
clone fraction
(default 0.2)
The best individual has N=clone fraction * generation size outcomes. The individual with rank r has at most min(1,N/r) outcomes.
max opt steps
(default 200)
Maximum number of optimization steps. Increase this value for better solutions.
max const steps
(default 25)
Stopping condition for the evolutionary algorithm. If there is no cost improvement in the best individual within max const steps epochs the optimization will be stopped. Increase this value for better solutions.
ii) Particle Swarm Optimization(PSO)
Particle Swarm Optimization Options

 

numParticles
(default 30)
How many particles are in the swarm (25-100 individuals are reasonable). Each particle represents a solution to the problem.
cGlobal
(default 0.5)
The velocity of each individual is influenced by the global optimum (that is: the best individual to its best iteration up to now).
cLocal
(default 0.5)
The velocity of each individual is influenced by the local optimum (that is: the optimum of the respective individual up to now).
maxIterations
(default 200)
Maximum number of optimization steps. Increase this value for better solutions.
maxConstIterations
(default 25)
Stopping condition for the evolutionary algorithm. If there is no cost improvement in the globally best particle within maxConstIterations epochs the optimization will be stopped. Increase this value for better solutions.
reflect
(default true)
If reflect is checked the particles are reflected at the bounding box (the domain of the optimization problem) resulting in an inverted velocity vector in the corresponding coordinates.

ii) Parallel Particle Swarm Optimization(PPSO)

This optimizer is the default optimizer. It's the parallel version of the PSO and uses the same parameters. It will dynamically chose the amount of parallel threads to use based on the number of processor cores available on the machine.

3. Command line options

The functionality of VennMaster can be automated from the command line to enable batch processing of multiple files. Type the following call in your VennMaster installation directory:

java -Xms256m -Xmx256m -jar venn.jar arguments ...

The two -Xoptions are necessary to adapt the memory assignment to VennMaster. For larger problems it may be necessary to increase those values (e.g. to 512).

-help,-?
displays help information
--version, -v
displays the version number of VennMaster
--cfg file.xml
configuration file
--ocfg file.xml
configuration file output
--list file.list
list file input
--gce input.gce
GoMiner gene-category export file
--se input.se
GoMiner summary export file
--htgce ht-input.gce
High-Throughput GoMiner gce file
--filter file.xml
filter file (for GoMiner)
--ofilter file.xml
filter file output
--svg file.svg
SVG file output
--sim file.txt
simulation errors output
--prof file.txt
error profile output for the last simulation

The original version of this page is available at

http://www.informatik.uni-ulm.de/ni/staff/HKestler/vennm/doc.html.


Contact/Imprint

André Müller, Hans A. Kestler
last modified: 2010-05-12

Latest News

Our paper "A model of the onset of the Senescence Associated Secretory Phenotype after DNA damage induced Senescence" has been accepted for publishing in PLOS Computational Biology.

Our paper "The influence of multi-class feature selection on the prediction of diagnostic phenotypeshas been accepted for publishing in Neural Processing Letters.