Summary¶
These scripts are designed to assist in the analysis of errors within BEL documents and provide some suggestions for fixes.
-
pybel_tools.summary.
count_relations
(graph)[source]¶ Return a histogram over all relationships in a graph.
Parameters: graph (pybel.BELGraph) – A BEL graph Returns: A Counter from {relation type: frequency} Return type: collections.Counter
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pybel_tools.summary.
get_edge_relations
(graph)[source]¶ Build a dictionary of {node pair: set of edge types}.
Return type: Mapping
[Tuple
[BaseEntity
,BaseEntity
],Set
[str
]]
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pybel_tools.summary.
count_unique_relations
(graph)[source]¶ Return a histogram of the different types of relations present in a graph.
Note: this operation only counts each type of edge once for each pair of nodes
Return type: Counter
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pybel_tools.summary.
count_annotations
(graph)[source]¶ Count how many times each annotation is used in the graph.
Parameters: graph (pybel.BELGraph) – A BEL graph Returns: A Counter from {annotation key: frequency} Return type: collections.Counter
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pybel_tools.summary.
get_annotations
(graph)[source]¶ Get the set of annotations used in the graph.
Parameters: graph (pybel.BELGraph) – A BEL graph Returns: A set of annotation keys Return type: set[str]
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pybel_tools.summary.
get_annotations_containing_keyword
(graph, keyword)[source]¶ Get annotation/value pairs for values for whom the search string is a substring
Parameters: - graph (
BELGraph
) – A BEL graph - keyword (
str
) – Search for annotations whose values have this as a substring
Return type: - graph (
-
pybel_tools.summary.
count_annotation_values
(graph, annotation)[source]¶ Count in how many edges each annotation appears in a graph
Parameters: - graph (
BELGraph
) – A BEL graph - annotation (
str
) – The annotation to count
Return type: Returns: A Counter from {annotation value: frequency}
- graph (
-
pybel_tools.summary.
count_annotation_values_filtered
(graph, annotation, source_predicate=None, target_predicate=None)[source]¶ Count in how many edges each annotation appears in a graph, but filter out source nodes and target nodes.
See
pybel_tools.utils.keep_node()
for a basic filter.Parameters: - graph (
BELGraph
) – A BEL graph - annotation (
str
) – The annotation to count - source_predicate (
Optional
[Callable
[[BELGraph
,BaseEntity
],bool
]]) – A predicate (graph, node) -> bool for keeping source nodes - target_predicate (
Optional
[Callable
[[BELGraph
,BaseEntity
],bool
]]) – A predicate (graph, node) -> bool for keeping target nodes
Return type: Returns: A Counter from {annotation value: frequency}
- graph (
-
pybel_tools.summary.
pair_is_consistent
(graph, u, v)[source]¶ Return if the edges between the given nodes are consistent, meaning they all have the same relation.
Return type: Optional
[str
]Returns: If the edges aren’t consistent, return false, otherwise return the relation type
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pybel_tools.summary.
get_consistent_edges
(graph)[source]¶ Yield pairs of (source node, target node) for which all of their edges have the same type of relation.
Return type: Iterable
[Tuple
[BaseEntity
,BaseEntity
]]Returns: An iterator over (source, target) node pairs corresponding to edges with many inconsistent relations
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pybel_tools.summary.
get_contradictory_pairs
(graph)[source]¶ Iterates over contradictory node pairs in the graph based on their causal relationships
Return type: Iterable
[Tuple
[BaseEntity
,BaseEntity
]]Returns: An iterator over (source, target) node pairs that have contradictory causal edges
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pybel_tools.summary.
count_pathologies
(graph)[source]¶ Count the number of edges in which each pathology is incident.
Parameters: graph (pybel.BELGraph) – A BEL graph Return type: Counter
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pybel_tools.summary.
get_unused_annotations
(graph)[source]¶ Get the set of all annotations that are defined in a graph, but are never used.
Parameters: graph (pybel.BELGraph) – A BEL graph Returns: A set of annotations Return type: set[str]
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pybel_tools.summary.
get_unused_list_annotation_values
(graph)[source]¶ Get all of the unused values for list annotations.
Parameters: graph (pybel.BELGraph) – A BEL graph Returns: A dictionary of {str annotation: set of str values that aren’t used} Return type: dict[str,set[str]]
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pybel_tools.summary.
count_error_types
(graph)[source]¶ Count the occurrence of each type of error in a graph.
Return type: Counter
Returns: A Counter of {error type: frequency}
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pybel_tools.summary.
count_naked_names
(graph)[source]¶ Count the frequency of each naked name (names without namespaces).
Return type: Counter
Returns: A Counter from {name: frequency}
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pybel_tools.summary.
get_naked_names
(graph)[source]¶ Get the set of naked names in the graph.
Return type: Set
[str
]
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pybel_tools.summary.
get_incorrect_names_by_namespace
(graph, namespace)[source]¶ Return the set of all incorrect names from the given namespace in the graph.
Return type: Set
[str
]Returns: The set of all incorrect names from the given namespace in the graph
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pybel_tools.summary.
get_incorrect_names
(graph)[source]¶ Return the dict of the sets of all incorrect names from the given namespace in the graph.
Return type: Mapping
[str
,Set
[str
]]Returns: The set of all incorrect names from the given namespace in the graph
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pybel_tools.summary.
get_undefined_namespaces
(graph)[source]¶ Get all namespaces that are used in the BEL graph aren’t actually defined.
Return type: Set
[str
]
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pybel_tools.summary.
get_undefined_namespace_names
(graph, namespace)[source]¶ Get the names from a namespace that wasn’t actually defined.
Return type: Set
[str
]Returns: The set of all names from the undefined namespace
-
pybel_tools.summary.
calculate_incorrect_name_dict
(graph)[source]¶ Group all of the incorrect identifiers in a dict of {namespace: list of erroneous names}.
Return type: Mapping
[str
,str
]Returns: A dictionary of {namespace: list of erroneous names}
-
pybel_tools.summary.
calculate_error_by_annotation
(graph, annotation)[source]¶ Group the graph by a given annotation and builds lists of errors for each.
Return type: Mapping
[str
,List
[str
]]Returns: A dictionary of {annotation value: list of errors}
-
pybel_tools.summary.
group_errors
(graph)[source]¶ Group the errors together for analysis of the most frequent error.
Return type: Mapping
[str
,List
[int
]]Returns: A dictionary of {error string: list of line numbers}
-
pybel_tools.summary.
get_names_including_errors
(graph)[source]¶ Takes the names from the graph in a given namespace and the erroneous names from the same namespace and returns them together as a unioned set
Return type: Mapping
[str
,Set
[str
]]Returns: The dict of the sets of all correct and incorrect names from the given namespace in the graph
-
pybel_tools.summary.
get_names_including_errors_by_namespace
(graph, namespace)[source]¶ Takes the names from the graph in a given namespace (
pybel.struct.summary.get_names_by_namespace()
) and the erroneous names from the same namespace (get_incorrect_names_by_namespace()
) and returns them together as a unioned setReturn type: Set
[str
]Returns: The set of all correct and incorrect names from the given namespace in the graph
-
pybel_tools.summary.
get_undefined_annotations
(graph)[source]¶ Get all annotations that aren’t actually defined.
Return type: Set
[str
]Returns: The set of all undefined annotations
-
pybel_tools.summary.
get_namespaces_with_incorrect_names
(graph)[source]¶ Return the set of all namespaces with incorrect names in the graph.
Return type: Set
[str
]
-
pybel_tools.summary.
get_most_common_errors
(graph, n=20)[source]¶ Get the (n) most common errors in a graph.
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pybel_tools.summary.
plot_summary_axes
(graph, lax, rax, logx=True)[source]¶ Plots your graph summary statistics on the given axes.
After, you should run
plt.tight_layout()
and you must runplt.show()
to view.Shows: 1. Count of nodes, grouped by function type 2. Count of edges, grouped by relation type
Parameters: - graph (pybel.BELGraph) – A BEL graph
- lax – An axis object from matplotlib
- rax – An axis object from matplotlib
Example usage:
>>> import matplotlib.pyplot as plt >>> from pybel import from_pickle >>> from pybel_tools.summary import plot_summary_axes >>> graph = from_pickle('~/dev/bms/aetionomy/parkinsons.gpickle') >>> fig, axes = plt.subplots(1, 2, figsize=(10, 4)) >>> plot_summary_axes(graph, axes[0], axes[1]) >>> plt.tight_layout() >>> plt.show()
-
pybel_tools.summary.
plot_summary
(graph, plt, logx=True, **kwargs)[source]¶ Plots your graph summary statistics. This function is a thin wrapper around
plot_summary_axis()
. It automatically takes care of building figures given matplotlib’s pyplot module as an argument. After, you need to runplt.show()
.plt
is given as an argument to avoid needing matplotlib as a dependency for this functionShows:
- Count of nodes, grouped by function type
- Count of edges, grouped by relation type
Parameters: - plt – Give
matplotlib.pyplot
to this parameter - kwargs – keyword arguments to give to
plt.subplots()
Example usage:
>>> import matplotlib.pyplot as plt >>> from pybel import from_pickle >>> from pybel_tools.summary import plot_summary >>> graph = from_pickle('~/dev/bms/aetionomy/parkinsons.gpickle') >>> plot_summary(graph, plt, figsize=(10, 4)) >>> plt.show()
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pybel_tools.summary.
is_causal_relation
(edge_data)[source]¶ Check if the given relation is causal.
Return type: bool
-
pybel_tools.summary.
get_causal_out_edges
(graph, nbunch)[source]¶ Get the out-edges to the given node that are causal.
Return type: Set
[Tuple
[BaseEntity
,BaseEntity
]]Returns: A set of (source, target) pairs where the source is the given node
-
pybel_tools.summary.
get_causal_in_edges
(graph, nbunch)[source]¶ Get the in-edges to the given node that are causal.
Return type: Set
[Tuple
[BaseEntity
,BaseEntity
]]Returns: A set of (source, target) pairs where the target is the given node
-
pybel_tools.summary.
is_causal_source
(graph, node)[source]¶ Return true of the node is a causal source.
- Doesn’t have any causal in edge(s)
- Does have causal out edge(s)
Return type: bool
-
pybel_tools.summary.
is_causal_central
(graph, node)[source]¶ Return true if the node is neither a causal sink nor a causal source.
- Does have causal in edges(s)
- Does have causal out edge(s)
Return type: bool
-
pybel_tools.summary.
is_causal_sink
(graph, node)[source]¶ Return true if the node is a causal sink.
- Does have causal in edge(s)
- Doesn’t have any causal out edge(s)
Return type: bool
-
pybel_tools.summary.
get_causal_source_nodes
(graph, func)[source]¶ Return a set of all nodes that have an in-degree of 0.
This likely means that it is an external perturbagen and is not known to have any causal origin from within the biological system. These nodes are useful to identify because they generally don’t provide any mechanistic insight.
Return type: Set
[BaseEntity
]
-
pybel_tools.summary.
get_causal_central_nodes
(graph, func)[source]¶ Return a set of all nodes that have both an in-degree > 0 and out-degree > 0.
This means that they are an integral part of a pathway, since they are both produced and consumed.
Return type: Set
[BaseEntity
]
-
pybel_tools.summary.
get_causal_sink_nodes
(graph, func)[source]¶ Returns a set of all ABUNDANCE nodes that have an causal out-degree of 0.
This likely means that the knowledge assembly is incomplete, or there is a curation error.
Return type: Set
[BaseEntity
]
-
pybel_tools.summary.
get_degradations
(graph)[source]¶ Get all nodes that are degraded.
Return type: Set
[BaseEntity
]
-
pybel_tools.summary.
get_activities
(graph)[source]¶ Get all nodes that have molecular activities.
Return type: Set
[BaseEntity
]
-
pybel_tools.summary.
get_translocated
(graph)[source]¶ Get all nodes that are translocated.
Return type: Set
[BaseEntity
]
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pybel_tools.summary.
count_top_centrality
(graph, number=30)[source]¶ Get top centrality dictionary.
Return type: Mapping
[BaseEntity
,int
]
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pybel_tools.summary.
get_modifications_count
(graph)[source]¶ Get a modifications count dictionary.
Return type: Mapping
[str
,int
]
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pybel_tools.summary.
count_subgraph_sizes
(graph, annotation='Subgraph')[source]¶ Count the number of nodes in each subgraph induced by an annotation.
Parameters: annotation ( str
) – The annotation to group by and compare. Defaults to ‘Subgraph’Return type: Counter
[int
]Returns: A dictionary from {annotation value: number of nodes}
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pybel_tools.summary.
calculate_subgraph_edge_overlap
(graph, annotation='Subgraph')[source]¶ Build a DatafFame to show the overlap between different sub-graphs.
Options: 1. Total number of edges overlap (intersection) 2. Percentage overlap (tanimoto similarity)
Parameters: - graph (
BELGraph
) – A BEL graph - annotation (
str
) – The annotation to group by and compare. Defaults to ‘Subgraph’
Return type: Tuple
[Mapping
[str
,Set
[Tuple
[BaseEntity
,BaseEntity
]]],Mapping
[str
,Mapping
[str
,Set
[Tuple
[BaseEntity
,BaseEntity
]]]],Mapping
[str
,Mapping
[str
,Set
[Tuple
[BaseEntity
,BaseEntity
]]]],Mapping
[str
,Mapping
[str
,float
]]]Returns: {subgraph: set of edges}, {(subgraph 1, subgraph2): set of intersecting edges}, {(subgraph 1, subgraph2): set of unioned edges}, {(subgraph 1, subgraph2): tanimoto similarity},
- graph (
-
pybel_tools.summary.
summarize_subgraph_edge_overlap
(graph, annotation='Subgraph')[source]¶ Return a similarity matrix between all subgraphs (or other given annotation).
Parameters: annotation ( str
) – The annotation to group by and compare. Defaults to"Subgraph"
Returns: A similarity matrix in a dict of dicts Return type: dict
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pybel_tools.summary.
rank_subgraph_by_node_filter
(graph, node_predicates, annotation='Subgraph', reverse=True)[source]¶ Rank sub-graphs by which have the most nodes matching an given filter.
A use case for this function would be to identify which subgraphs contain the most differentially expressed genes.
>>> from pybel import from_pickle >>> from pybel.constants import GENE >>> from pybel_tools.integration import overlay_type_data >>> from pybel_tools.summary import rank_subgraph_by_node_filter >>> import pandas as pd >>> graph = from_pickle('~/dev/bms/aetionomy/alzheimers.gpickle') >>> df = pd.read_csv('~/dev/bananas/data/alzheimers_dgxp.csv', columns=['Gene', 'log2fc']) >>> data = {gene: log2fc for _, gene, log2fc in df.itertuples()} >>> overlay_type_data(graph, data, 'log2fc', GENE, 'HGNC', impute=0.0) >>> results = rank_subgraph_by_node_filter(graph, lambda g, n: 1.3 < abs(g[n]['log2fc']))
Return type: List
[Tuple
[str
,int
]]
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pybel_tools.summary.
summarize_subgraph_node_overlap
(graph, node_predicates=None, annotation='Subgraph')[source]¶ Calculate the subgraph similarity tanimoto similarity in nodes passing the given filter.
Provides an alternate view on subgraph similarity, from a more node-centric view
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pybel_tools.summary.
count_pmids
(graph)[source]¶ Count the frequency of PubMed documents in a graph.
Return type: Counter
Returns: A Counter from {(pmid, name): frequency}
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pybel_tools.summary.
get_pmid_by_keyword
(keyword, graph=None, pubmed_identifiers=None)[source]¶ Get the set of PubMed identifiers beginning with the given keyword string.
Parameters: Return type: Returns: A set of PubMed identifiers starting with the given string
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pybel_tools.summary.
count_citations
(graph, **annotations)[source]¶ Counts the citations in a graph based on a given filter
Parameters: - graph (
BELGraph
) – A BEL graph - annotations (dict) – The annotation filters to use
Return type: Returns: A counter from {(citation type, citation reference): frequency}
- graph (
-
pybel_tools.summary.
count_citations_by_annotation
(graph, annotation)[source]¶ Group the citation counters by subgraphs induced by the annotation.
Parameters: - graph (
BELGraph
) – A BEL graph - annotation (
str
) – The annotation to use to group the graph
Return type: Returns: A dictionary of Counters {subgraph name: Counter from {citation: frequency}}
- graph (
Get authors for whom the search term is a substring.
Parameters: - graph (pybel.BELGraph) – A BEL graph
- keyword (
str
) – The keyword to search the author strings for - authors (set[str]) – An optional set of pre-cached authors calculated from the graph
Return type: Returns: A set of authors with the keyword as a substring
Group the author counters by sub-graphs induced by the annotation.
Parameters: - graph (
BELGraph
) – A BEL graph - annotation (
str
) – The annotation to use to group the graph
Return type: Returns: A dictionary of Counters {subgraph name: Counter from {author: frequency}}
- graph (
-
pybel_tools.summary.
get_evidences_by_pmid
(graph, pmids)[source]¶ Get a dictionary from the given PubMed identifiers to the sets of all evidence strings associated with each in the graph.
Parameters: Returns: A dictionary of {pmid: set of all evidence strings}
Return type:
-
pybel_tools.summary.
count_citation_years
(graph)[source]¶ Count the number of citations from each year.
Return type: Counter
[int
]
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pybel_tools.summary.
create_timeline
(year_counter)[source]¶ Complete the Counter timeline.
Parameters: year_counter (Counter) – counter dict for each year Return type: List
[Tuple
[int
,int
]]Returns: complete timeline