procope.examples
package of the src/
folder. Note that the execution of the program might take a some
seconds to finish as a lot of tasks are performed.data/
folder of this
package. For evaluation, the MIPS gold standard set for Yeast is
used. The PurificationDataReader
class
provides methods to load purification data from files or streams. It
will throw an exception of an IO error occurs or if the file format is
invalid. In this example we catch all kinds of exception in one catch
block:PurificationData dataKrogan=null,
dataGavin=null; |
ScoresCalculator
can be used for automatic score network generation using the NetworkGenerator
class. Note that Socio
Affinity scores do not require any further parameters. We
use a score threshold of 0.0 here which means that no negative scores
will be inserted into the scores network. ScoresCalculator calcSocios = new
SocioAffinityCalculator(dataMerged); |
CombinationRules
class
contains different settings which control the merging of two given
networks.ProteinNetwork scoresPEGavin =
NetworkGenerator |
scoresPEKrogan.scalarMultiplication(0.5f); |
Clusterer
interface.Clusterer clustererSocios = new
HierarchicalClusterer(HierarchicalLinkage.UPGMA, 2.7f); |
new HierarchicalClusterer...
could be replaced by any other clusterer, e.g. the Markov Clusterer
delivered with this library (MarkovClusterer
class).data/
folder of this
package. The ComplexSetReader
class contains
methods to load complex sets from files or streams.ComplexSet setMIPS = null; |
ComplexComparison
class contains different comparison methods, here we use the measure
proposed in Brohee et al., 2006. Check out the
JavaDocs of ComplexComparison
for more
information on complex set comparison.System.out.println("Socio clusters against
MIPS: " |
ComplexSetComparison.broheeComparison
produces a BroheeStats
object. This object
overwrites toString()
to produce a reasonable
string representation of the comparison result. This is why we can
directly append the result of the function in this print-call.data/
folder
of the package:LocalizationData locData = null; |
Colocalization coloc = new
Colocalization(locData); true )); |
true
means they
are ignored).data/yeastmappings.txt
.try { ",
true), true);
|
ProteinManager
handles the name mappings
and the mapping of protein identifiers to internal IDs. Please check
out the JavaDocs of that class for more information.readNetwork
indicates whether we are reading a directed network (in this case: yes). addNameMappings
methods tells the ProteinManager
that the file contains the targets in the first column and the synoyms in the second columns (targetFirst == true
). That is a mapping S000000061 => YAL066W
in the file looks like this:YAL066W
S000000061
The alternative would be (targetFirst == false
, which is not the case for data/yeastmappings.txt
):S000000061
YAL066W
GOAnnotations goAnno=null;
|
TermSimilarities
termSim = new TermSimilaritiesSchlicker(goNet, goAnno, |
FunctionalSimilarities
object is
a ScoresCalculator
and can be used to
calculate score networks or complex scores as described in the
following section.ComplexScoreCalculator
contains methods to calculate complex scores and average complex scores
over whole complex sets. The score of a complex is defined as the
average score of all inner-complex protein interactions where undefined
scores (e.g. missing edges in the network) are treated with a value of
0. That is each complex score is the average of n*(n-1)/2 different
inner-complex interaction scores. Again we calculate a
complex-size-weighted average:
System.out.println("Functional
similarity of socio clusters: " + |
Loading
purifications... |