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- Method resolution order:
- GoClusteringSimilarityFunction
- ClusteringSimilarityFunction.ClusteringSimilarityFunction
- __builtin__.object
Methods defined here:
- __init__(self, piana_access=None, term_type=None, mode=None, path_length_threshold=None)
- "dbaccess" is a database accession object used to access information from a database, in case the similarity function needs information
that is not contained in the node itself. If not being used, just set to None
"term_type" specifies which kind of GO terms we will use for the clustering.
valid values are:
- molecular_function
- biological_process
- cellular_component
"mode" defines how to evaluate the distance between two clusters:
- random takes a random element from each cluster and evaluates similarity between them
- min takes the minimal distance between elements of each cluster
- max takes the maximal distance between elements of each cluster
- average takes the average distance between all elements of each cluster
"path_length_threshold": Maximum distance between two clusters to be joined
- calculate_formula(self, term1=None, term2=None)
- Method that calculates the formula using two specific elements inside
a cluster:
"term1": Go term id of the first element
"term2": Go term id of the second element
- calculate_similarity(self, list_node_attributes1, list_node_attributes2)
- Method that returns similarity score from two lists of attributes of nodes that are being clustered
"list_node_attributes1" is a list of node attributes that belong to the same cluster
"list_node_attributes2" is a list of node attributes that belong to the same cluster
This method calculates how similar this two clusters are
- get_highest_depth(self)
- returns highest detph found
used by the go clustering stop condition to stop the clustering
- get_proteinPianas_list(self, list_node_attribute=None)
- Method that returns a protein piana list, from an attributes list given
"list_node_attribute": list of attributes into a cluster. Each attribute is a protein pianaID
- get_terms_depth(self, list_of_terms_ids=None, search_mode=None)
- Method that searches the go term with maximum or minimum depth in a cluster
"list_of_terms_ids": the list of term ids in the cluster
"search_mode": determines which of the terms will be returned:
- min: returns the term id that has the lowest depth
- max: returns the term id that has the highest depth
returns the term id matching the search criteria
- print_go_graph_dot_file(self, filter_mode='all', output_target=None, use_alternative_id='no', representative_term='min')
- Generates .dot file output of the clustered graph of go
It can be fed directly into the GraphViz program neato. This produces very nice network images...
filter_mode can be:
- "all": prints all edges in the graph
- "hidden": prints hidden edges of the graph
- "unhidden": prints unhidden edges of the graph
"use_alternative_id" can be
- "yes" --> uses alternative id for printing graph
- "no" --> uses internal id for printing graph
"representative_term" sets which of the go terms in the cluster will be shown on the cluster box
- min takes the term with the minimal depth (more general term)
- max takes the term with the maximal depth (more specific term)
Methods inherited from ClusteringSimilarityFunction.ClusteringSimilarityFunction:
- get_index_to_node_id(self)
- Method that returns the correspondence between
matrix indexes and GraphNode ids
- get_max_value(self)
- method that returns max value from
the similarity matrix
- get_node_id_by_index(self, number)
- Method that returns the correnspondendence betrween a given index to a node_id
- get_similarity_matrix(self, cluster_graph, dbaccess=None)
- Method that returns similarity matrix from a given cluster graph "cluster_graph"
each position in the matrix indicates how similar two clusters are.
Data and other attributes inherited from ClusteringSimilarityFunction.ClusteringSimilarityFunction:
- __dict__ = <dictproxy object>
- dictionary for instance variables (if defined)
- __weakref__ = <attribute '__weakref__' of 'ClusteringSimilarityFunction' objects>
- list of weak references to the object (if defined)
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