# Source code for pybel.io.spia

```
# -*- coding: utf-8 -*-
"""An exporter for signaling pathway impact analysis (SPIA) described by [Tarca2009]_.
.. [Tarca2009] Tarca, A. L., *et al* (2009). `A novel signaling pathway impact analysis
<https://doi.org/10.1093/bioinformatics/btn577>`_. Bioinformatics, 25(1), 75–82.
.. seealso:: https://bioconductor.org/packages/release/bioc/html/SPIA.html
"""
import itertools as itt
import os
from collections import OrderedDict
from typing import Dict, Mapping, Set
import pandas as pd
from ..constants import ASSOCIATION, CAUSAL_DECREASE_RELATIONS, CAUSAL_INCREASE_RELATIONS, RELATION
from ..dsl import CentralDogma, Gene, ListAbundance, ProteinModification, Rna
from ..language import pmod_mappings
from ..struct import BELGraph
from ..typing import EdgeData
__all__ = [
'to_spia_dfs',
'to_spia_excel',
'to_spia_tsvs',
]
SPIADataFrames = Mapping[str, pd.DataFrame]
KEGG_RELATIONS = [
"activation",
"compound",
"binding/association",
"expression",
"inhibition",
"activation_phosphorylation",
"phosphorylation",
"inhibition_phosphorylation",
"inhibition_dephosphorylation",
"dissociation",
"dephosphorylation",
"activation_dephosphorylation",
"state change",
"activation_indirect effect",
"inhibition_ubiquination",
"ubiquination",
"expression_indirect effect",
"inhibition_indirect effect",
"repression",
"dissociation_phosphorylation",
"indirect effect_phosphorylation",
"activation_binding/association",
"indirect effect",
"activation_compound",
"activation_ubiquination"
]
[docs]def to_spia_excel(graph: BELGraph, path: str) -> None:
"""Write the BEL graph as an SPIA-formatted excel sheet at the given path."""
x = to_spia_dfs(graph)
spia_matrices_to_excel(x, path)
[docs]def to_spia_tsvs(graph: BELGraph, directory: str) -> None:
"""Write the BEL graph as a set of SPIA-formatted TSV files in a given directory."""
x = to_spia_dfs(graph)
spia_matrices_to_tsvs(x, directory)
[docs]def to_spia_dfs(graph: BELGraph) -> SPIADataFrames:
"""Create an excel sheet ready to be used in SPIA software.
:param graph: BELGraph
:return: dictionary with matrices
"""
index_nodes = get_matrix_index(graph)
spia_matrices = build_spia_matrices(index_nodes)
for u, v, edge_data in graph.edges(data=True):
# Both nodes are CentralDogma abundances
if isinstance(u, CentralDogma) and isinstance(v, CentralDogma):
# Update matrix dict
update_spia_matrices(spia_matrices, u, v, edge_data)
# Subject is CentralDogmaAbundance and node is ListAbundance
elif isinstance(u, CentralDogma) and isinstance(v, ListAbundance):
# Add a relationship from subject to each of the members in the object
for node in v.members:
# Skip if the member is not in CentralDogma
if not isinstance(node, CentralDogma):
continue
update_spia_matrices(spia_matrices, u, node, edge_data)
# Subject is ListAbundance and node is CentralDogmaAbundance
elif isinstance(u, ListAbundance) and isinstance(v, CentralDogma):
# Add a relationship from each of the members of the subject to the object
for node in u.members:
# Skip if the member is not in CentralDogma
if not isinstance(node, CentralDogma):
continue
update_spia_matrices(spia_matrices, node, v, edge_data)
# Both nodes are ListAbundance
elif isinstance(u, ListAbundance) and isinstance(v, ListAbundance):
for sub_member, obj_member in itt.product(u.members, v.members):
# Update matrix if both are CentralDogma
if isinstance(sub_member, CentralDogma) and isinstance(obj_member, CentralDogma):
update_spia_matrices(spia_matrices, sub_member, obj_member, edge_data)
# else Not valid edge
return spia_matrices
def get_matrix_index(graph: BELGraph) -> Set[str]:
"""Return set of HGNC names from Proteins/Rnas/Genes/miRNA, nodes that can be used by SPIA."""
# TODO: Using HGNC Symbols for now
return {
node.name
for node in graph
if isinstance(node, CentralDogma) and node.namespace.upper() == 'HGNC'
}
def build_spia_matrices(nodes: Set[str]) -> Dict[str, pd.DataFrame]:
"""Build an adjacency matrix for each KEGG relationship and return in a dictionary.
:param nodes: A set of HGNC gene symbols
:return: Dictionary of adjacency matrix for each relationship
"""
nodes = list(sorted(nodes))
# Create sheets of the excel in the given order
matrices = OrderedDict()
for relation in KEGG_RELATIONS:
matrices[relation] = pd.DataFrame(0, index=nodes, columns=nodes)
return matrices
UB_NAMES = {"Ub"} | {e.name for e in pmod_mappings['Ub']['xrefs']}
PH_NAMES = {"Ph"} | {e.name for e in pmod_mappings['Ph']['xrefs']}
def update_spia_matrices(
spia_matrices: Dict[str, pd.DataFrame],
u: CentralDogma,
v: CentralDogma,
edge_data: EdgeData,
) -> None:
"""Populate the adjacency matrix."""
if u.namespace.lower() != 'hgnc' or v.namespace.lower() != 'hgnc':
return
u_name = u.name
v_name = v.name
relation = edge_data[RELATION]
if relation in CAUSAL_INCREASE_RELATIONS:
# If it has pmod check which one and add it to the corresponding matrix
if v.variants and any(isinstance(variant, ProteinModification) for variant in v.variants):
for variant in v.variants:
if not isinstance(variant, ProteinModification):
continue
elif variant.entity.name in UB_NAMES:
spia_matrices["activation_ubiquination"][u_name][v_name] = 1
elif variant.entity.name in PH_NAMES:
spia_matrices["activation_phosphorylation"][u_name][v_name] = 1
elif isinstance(v, (Gene, Rna)): # Normal increase, add activation
spia_matrices['expression'][u_name][v_name] = 1
else:
spia_matrices['activation'][u_name][v_name] = 1
elif relation in CAUSAL_DECREASE_RELATIONS:
# If it has pmod check which one and add it to the corresponding matrix
if v.variants and any(isinstance(variant, ProteinModification) for variant in v.variants):
for variant in v.variants:
if not isinstance(variant, ProteinModification):
continue
elif variant.entity.name in UB_NAMES:
spia_matrices['inhibition_ubiquination'][u_name][v_name] = 1
elif variant.entity.name in PH_NAMES:
spia_matrices["inhibition_phosphorylation"][u_name][v_name] = 1
elif isinstance(v, (Gene, Rna)): # Normal decrease, check which matrix
spia_matrices["repression"][u_name][v_name] = 1
else:
spia_matrices["inhibition"][u_name][v_name] = 1
elif relation == ASSOCIATION:
spia_matrices["binding_association"][u_name][v_name] = 1
def spia_matrices_to_excel(spia_matrices: SPIADataFrames, path: str) -> None:
"""Export a SPIA data dictionary into an Excel sheet at the given path.
.. note::
# The R import should add the values:
# ["nodes"] from the columns
# ["title"] from the name of the file
# ["NumberOfReactions"] set to "0"
"""
writer = pd.ExcelWriter(path, engine='xlsxwriter')
for relation, df in spia_matrices.items():
df.to_excel(writer, sheet_name=relation, index=False)
# Save excel
writer.save()
def spia_matrices_to_tsvs(spia_matrices: SPIADataFrames, directory: str) -> None:
"""Export a SPIA data dictionary into a directory as several TSV documents."""
os.makedirs(directory, exist_ok=True)
for relation, df in spia_matrices.items():
df.to_csv(os.path.join(directory, '{relation}.tsv'.format(relation=relation)), index=True)
```