cls.FunctionalSimilarity.CFunctionalSimilarityBase Class Reference

Functional similarity computation base class. More...

Inheritance diagram for cls.FunctionalSimilarity.CFunctionalSimilarityBase:

Public Member Functions
def	__init__ (s)
def	ComputePWSSMatrix (s, sp1, sp2)
	Compute pairwise semantic similarity matrix for a pair of proteins. More...
def	ComputePWGO (s, go1, go2)
	Compute pairwise semantic similarity for a pair of GOTerms. More...
def	Initialize (s, paths, ns, graphByFileName="")
	Initialize module. More...
def	SetSimMeasure (s, measure)
	Choose computational model for semantic similarity. More...
def	SetManual (s, only)
	Select manual filtering of GO annotations. More...
def	SetNDRemoval (s, flag)
	Remove annotations to ND. More...
def	GetIC (s, goID)
	Get information content of a go term ID. More...
def	SimAvg (s, sp1, sp2)
	Average similarity. More...
def	SimMax (s, sp1, sp2)
	Maximum similarity. More...
def	SimRCAvgMax (s, sp1, sp2)
	Maximum row/column averaged maxima. More...
def	SimBMA (s, sp1, sp2)
	Best match average. More...
def	SimBMA2 (s, sp1, sp2)
	Best match average averaged. More...

Detailed Description

Functional similarity computation base class.

Computes functional similarity as a combination of GO-annotated pairs of
protein identifiers.

Constructor & Destructor Documentation

__init__()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.__init__

(

s

)

Reimplemented in cls.FunctionalSimilarity.CFunctionalSimilarity.

Member Function Documentation

ComputePWGO()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.ComputePWGO	(		s,
			go1,
			go2
	)

Compute pairwise semantic similarity for a pair of GOTerms.

    The similarity measure has to be set before via @ref SetSimMeasure.
    @param go1  First GOTerm.
    @param go2  Second GOTerm.
    @return @c Float if the value could be computed, else @c None.

ComputePWSSMatrix()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.ComputePWSSMatrix	(		s,
			sp1,
			sp2
	)

Compute pairwise semantic similarity matrix for a pair of proteins.

    The similarity measure has to be set before via @ref SetSimMeasure.
    @param sp1  Swissprot accession number of first protein.
    @param sp2  Swissprot accession number of second protein.
    @return @c Tuple (@c goIDs1, @c goIDs2, @c matrix). @c matrix is a
    numpy array with rows corresponding to @c goIDs1 and columns
    corresponding to @c goIDs2. If a pair of GO IDs does not have a
    semantic similarity value, @c NaN will be in the corresponding matrix
    element. This is important when using numpy functions, as normally @c
    NaN propagates and special functions for ignoring @c Nans are needed.

GetIC()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.GetIC	(		s,
			goID
	)

Get information content of a go term ID.

    @param goID  GO term ID.
    @return @c Float or @c None, in case of unknown term ID.

Initialize()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.Initialize	(		s,
			paths,
			ns,
			graphByFileName = ""
	)

Initialize module.

    Set the GO namespace and load the corresponding populated graph.
    @param paths  cls.Paths.CPaths object.
    @param ns  Gene ontology namespace, eg. 'biological_process'.
    @param graphByFileName  Full path file name to graph. This replaces
    the default graph files normally read from the DI repository.
    @return @c True; namespace has been set and the graph file has been
     loaded, @c False, unknown namespace, or graph file not found (error
     message issued for the latter case).

SetManual()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.SetManual	(		s,
			only
	)

Select manual filtering of GO annotations.

    @param only @c True, only consider manual annotations, ie. ignore
    'IEA' type records. @c False, consider all annotations.

SetNDRemoval()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.SetNDRemoval	(		s,
			flag
	)

Remove annotations to ND.

    @param flag If @c True, annotations with evidence code ND will be
    omitted. If set to @c False, ND annotations are included.

SetSimMeasure()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.SetSimMeasure	(		s,
			measure
	)

Choose computational model for semantic similarity.

    Contrary to the namespace, this can be changed on the fly. So if
    multiple different semantic similarities should be used to compute the
    functional similarities, a call to this function before a call to @ref
    SimMax, @ref SimRCAvgMax, @ref SimBMA, or @ref SimBMA2 is enough!
    @param measure  One of the strings defined in @ref SS_MEASURES.
    @return @c True; measure is known and has been set, @c False; unknown
    measure.

SimAvg()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.SimAvg	(		s,
			sp1,
			sp2
	)

Average similarity.

    Each of the two input proteins is associated with GO IDs. For each of
    the possible pairs of GO IDs, the semantic similarity is computed
    according to the measure selected by a prior call to @ref
    SetSimMeasure. The so-resulted semantic similarity matrix is then
    combined into a single value. In this case, the average over all
    matrix entries is computed.
    @param sp1  First UniProt/Swiss-Prot accession number.
    @param sp2  Second UniProt/Swiss-Prot accession number.
    @return (@c float) Protein similarity. @c None, if a protein is not
    associated with any GO term.

SimBMA()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.SimBMA	(		s,
			sp1,
			sp2
	)

Best match average.

    Computes the sum of the row maxima and the sum of the column maxima
    and normalizes this by dividing by the number of columns and rows of
    the semantic similarity matrix.
    See @ref SimAvg.

SimBMA2()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.SimBMA2	(		s,
			sp1,
			sp2
	)

Best match average averaged.

    Computes the average of the row maxima and the average of the column
    maxima of the semantic similarity matrix and divides the sum of these
    two values by two.

    See @ref SimAvg.

SimMax()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.SimMax	(		s,
			sp1,
			sp2
	)

Maximum similarity.

    Computes the maximum value of the semantic similarity matrix.
    See @ref SimAvg.

SimRCAvgMax()

def cls.FunctionalSimilarity.CFunctionalSimilarityBase.SimRCAvgMax	(		s,
			sp1,
			sp2
	)

Maximum row/column averaged maxima.

    Computes the maximum of the averaged row and column maxima of the
    semantic similarity matrix.
    See @ref SimAvg.

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The documentation for this class was generated from the following file:

• cls/FunctionalSimilarity.py