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callbacks.py
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callbacks.py
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import dash
import dash_core_components as dcc
import dash_bootstrap_components as dbc
import dash_html_components as html
import dash_cytoscape as cyto
from dash.dependencies import Output, Input, State
import plotly.express as px
from urllib.request import quote
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from matplotlib.colors import rgb2hex
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import KBinsDiscretizer
from app import app
import plotly.graph_objects as go
# cyto.load_extra_layouts()
stylesheet_base = [
{
"selector": "node",
"style": {
"background-color": "data(fill_color)",
"border-color": "data(line_color)",
"border-width": 3,
"font-size": "40px",
},
},
{
"selector": "node:selected",
"style": {
"background-color": "#FFE985",
"border-color": "#FFDD4A",
"border-width": 3,
},
},
]
def collapse_headbar_callback():
@app.callback(
Output("headbar_collapse", "is_open"),
[Input("headbar_toggler", "n_clicks")],
[State("headbar_collapse", "is_open")],
)
def collapse_headbar(n, is_open):
if n:
return not is_open
return is_open
def show_edges_callback(prefix, nodes, edges, edges_to_show):
@app.callback(
Output(prefix + "_show_edges_modal", "is_open"),
[Input(prefix + "_show_edges_close", "n_clicks")],
)
def close_show_all_edges_modal(n):
if n:
return False
else:
return True
@app.callback(
Output(prefix + "_show_all_edges_help", "on"),
[Input(prefix + "_show_all_edges_modal", "on")],
)
def modal_to_help(show_all):
if show_all:
return True
else:
return False
@app.callback(
Output(prefix + "_graph", "elements"),
[Input(prefix + "_show_all_edges_help", "on"),],
)
def show_edges(show_all):
if (
prefix != "target_projection"
): # useless because it will never be fired outside the target projection page
return nodes + edges_to_show
else:
if show_all:
return nodes + edges
else:
return nodes + edges_to_show
return show_edges, close_show_all_edges_modal
def displayHoverNodeData_callback(prefix, G):
@app.callback(
[
Output(prefix + "_title_card", "children"),
Output(prefix + "_img_card", "children"),
Output(prefix + "_attributes-list-card", "children"),
Output(prefix + "_selected_node_warning", "is_open"),
],
[
Input(prefix + "_graph", "mouseoverNodeData"),
Input(prefix + "_graph", "selectedNodeData"),
],
[State(prefix + "_selected_node_warning", "is_open")],
)
def displayHoverNodeData(data, selected_data, warning):
img = None
if not data:
data = {
"Name": "",
"Properties": "Hover over a node (or select it) to show its properties",
}
attributes = ["Name", "Properties"]
link_drugbank = ""
else:
if selected_data and len(selected_data) == 1:
data = selected_data[0]
if not warning:
warning = not warning
if data["kind"] == "Drug":
attributes = [
"ID",
"SMILES",
"ATC Code Level 1",
"ATC Identifier",
"Targets",
"Enzymes",
"Carriers",
"Transporters",
"Drug Interactions",
"Trials",
]
link_drugbank = "https://www.drugbank.ca/drugs/" + data["ID"]
if data["ID"] != "Not Available":
img = html.A(
html.Img(
src=data["structure"],
height="auto",
width="100%",
alt="Structure Image not Available",
),
href="https://www.drugbank.ca/structures/small_molecule_drugs/"
+ data["ID"],
target="_blank",
)
else:
attributes = [
"ID",
"Gene",
"PDBID",
"Organism",
"Protein Class",
"Protein Family",
"Cellular Location",
"STRING Interaction Partners",
"Drugs",
"Diseases",
]
link_drugbank = data["drugbank_url"]
if data["PDBID"] != "Not Available":
img = [
html.A(
html.Img(
src=data["structure"],
height="auto",
width="100%",
alt="Structure Image not Available",
),
href="https://www.rcsb.org/3d-view/" + data["PDBID"],
target="_blank",
),
html.A(
html.Small(
"Structure Reference",
style={
"position": "absolute",
"bottom": "1em",
"right": "1em",
"color": "grey",
},
),
href="http://doi.org/10.2210/pdb%s/pdb" % data["PDBID"],
target="_blank",
),
]
attributes_list = []
for attribute in attributes:
if data[attribute] != "" and data[attribute] != []:
if attribute in [
"ATC Code Level 1",
"ATC Identifier",
"Targets",
"Enzymes",
"Carriers",
"Transporters",
"Drug Interactions",
"STRING Interaction Partners",
"Drugs",
"Diseases",
]:
attributes_list.append(
dbc.Container(
html.Li(
[
html.Strong(attribute + ": "),
", ".join(data[attribute]),
],
className="list-group-item",
),
style={
"max-height": "20vh",
"overflow-y": "auto",
"padding": "0",
},
fluid=True,
)
)
elif attribute == "ID":
attributes_list.append(
html.Li(
[
html.Strong(attribute + ": "),
html.A(
data[attribute], href=link_drugbank, target="_blank"
),
],
className="list-group-item",
)
)
elif attribute == "Trials":
if not (
len(data["Trials"]) == 1
and data["Trials"][0] == "Not Available"
):
trials_list = [html.Strong("Trials: ")]
for n, trial in enumerate(data["Trials"]):
trials_list.append(
html.A(
trial, href=data["Trials Hrefs"][n], target="_blank"
)
)
trials_list.append(f" (phase: {data['Trials Phases'][n]})")
trials_list.append(", ")
trials_list = trials_list[
:-1
] # in order to remove the last ", "
attributes_list.append(
dbc.Container(
html.Li(trials_list, className="list-group-item"),
style={
"max-height": "20vh",
"overflow-y": "auto",
"padding": "0",
},
fluid=True,
)
)
elif attribute == "PDBID" and data[attribute] != "Not Available":
attributes_list.append(
html.Li(
[
html.Strong(attribute + ": "),
html.A(
data[attribute],
href="https://www.rcsb.org/structure/"
+ data[attribute],
target="_blank",
),
],
className="list-group-item",
)
)
elif attribute == "Gene":
attributes_list.append(
html.Li(
[
html.Strong(attribute + ": "),
html.A(
data[attribute],
href="https://www.uniprot.org/uniprot/"
+ data["ID"],
target="_blank",
),
],
className="list-group-item",
)
)
else:
attributes_list.append(
html.Li(
[html.Strong(attribute + ": "), data[attribute]],
className="list-group-item",
)
)
return data["Name"], img, attributes_list, warning
return displayHoverNodeData
def inspected_table_callback(prefix):
@app.callback(
[
Output(prefix + "_inspected_table", "children"),
Output(prefix + "_side_inspected_table", "active"),
Output(prefix + "_side_inspected_table", "disabled"),
Output(prefix + "_inspected_data_side_tooltip", "style"),
Output(prefix + "_inspected_data", "is_open"),
Output(prefix + "_n_inspected_nodes", "children"),
Output(prefix + "_inspected_cache", "value"),
],
[
Input(prefix + "_graph", "selectedNodeData"),
Input(prefix + "_highlighted_data_cache", "value"),
Input(prefix + "_inspected_dropdown", "value"),
],
)
def inspected_table(selected_data, highlighted_data, to_inspect):
results = [
html.H5("No Data to Show"),
html.P("(Try changing the value of the dropdown menu)"),
]
open = False
visibility = {"visibility": "hidden"}
if not selected_data:
selected_data = []
selected = {data["ID"]: data for data in selected_data}
highlighted = {data["ID"]: data for data in highlighted_data}
merged = selected.copy()
merged.update(highlighted)
if len(merged) > 1:
open = True
visibility = None
if to_inspect == "selected":
data = selected_data
elif to_inspect == "highlighted":
data = highlighted_data
elif to_inspect == "selected_or_highlighted":
data = list(merged.values())
else:
data = [
merged[id]
for id in set(selected.keys()).intersection(set(highlighted.keys()))
]
if data:
if len(data) >= 1 and open:
results = []
drugs_data = [d for d in data if d["kind"] == "Drug"]
if len(drugs_data) > 0:
attributes = [
"Name",
"ID",
"ATC Code Level 1",
"ATC Identifier",
"SMILES",
"Targets",
"Enzymes",
"Carriers",
"Transporters",
"Drug Interactions",
]
table_header = [
html.Thead(
html.Tr(
[
html.Th(attribute, style={"white-space": "nowrap"})
for attribute in [
"Name",
"ID",
"ATC Level 1",
"ATC Identifier",
"SMILES",
"Targets",
"Enzymes",
"Carriers",
"Transporters",
"Drug Interactions",
]
]
)
)
]
table_body = []
for drug in drugs_data:
row = []
for attribute in attributes:
if attribute == "Name":
row.append(html.Td(drug["Name"]))
elif attribute == "ID":
row.append(
html.Td(
html.A(
drug["ID"],
href="https://www.drugbank.ca/drugs/"
+ drug["ID"],
target="_blank",
)
)
)
elif attribute == "SMILES":
row.append(
html.Td(
dbc.Container(
drug["SMILES"],
style={
"max-height": "20vh",
"overflow-y": "auto",
"padding": "0px",
},
fluid=True,
),
style={
"min-width": "12vw",
"max-width": "20vw",
"word-break": "break-word",
"padding-right": "0px",
},
)
)
elif "ATC" in attribute:
row.append(
html.Td(
dbc.Container(
", ".join(drug[attribute]),
style={
"max-height": "20vh",
"overflow-y": "auto",
"word-break": "break-word",
"padding": "0px",
},
fluid=True,
),
style={
"min-width": "7vw",
"max-width": "15vw",
"padding-right": "0px",
},
)
)
else:
row.append(
html.Td(
dbc.Container(
", ".join(drug[attribute]),
style={
"max-height": "20vh",
"overflow-y": "auto",
"word-break": "break-word",
"padding": "0px",
},
fluid=True,
),
style={
"min-width": "10vw",
"max-width": "20vw",
"padding-right": "0px",
},
)
)
table_body.append(html.Tr(row))
table_body = [html.Tbody(table_body)]
drugs_table = dbc.Table(
table_header + table_body,
className="table table-hover",
bordered=True,
)
drugs_href = "data:text/csv;charset=utf-8," + quote(
pd.DataFrame(
[
{
key: (
", ".join(value)
if isinstance(value, list)
else value
)
for key, value in d.items()
if key in attributes
}
for d in drugs_data
],
columns=attributes,
).to_csv(sep="\t", index=False, encoding="utf-8")
)
results += [
html.Br(),
dbc.Row(
[
dbc.Col(html.H4("Inspected Drugs"), xs=12, lg=10),
dbc.Col(
dbc.Row(
[
html.A(
dbc.Button(
"Download",
className="btn btn-outline-primary",
),
href=drugs_href,
download="inspected_drugs.tsv",
target="_blank",
),
dbc.Col(
html.A(
dbc.Row(
[
html.I(
className="fa fa-question-circle",
style={
"font-size": "1rem"
},
),
html.H5(
"Glossary",
style={
"margin-bottom": 0,
"margin-left": "0.5rem",
},
),
],
justify="center",
align="center",
),
id=prefix
+ "_inspected_table_drugs_glossary",
href="/covidrugnet/help#glossary",
target="_blank",
),
align="center",
),
],
justify="end",
align="center",
)
),
],
justify="around",
align="center",
),
html.Br(),
drugs_table,
]
targets_data = [d for d in data if d["kind"] == "Target"]
if len(targets_data) > 0:
if len(drugs_data) > 0:
results += [html.Br()]
attributes = [
"Name",
"ID",
"Gene",
"PDBID",
"Organism",
"Protein Class",
"Protein Family",
"Cellular Location",
"STRING Interaction Partners",
"Drugs",
"Diseases",
]
table_header = [
html.Thead(
html.Tr(
[
html.Th(attribute, style={"white-space": "nowrap"})
for attribute in attributes
]
)
)
]
table_body = []
for target in targets_data:
row = []
for attribute in attributes:
if attribute == "Name":
row.append(
html.Td(
target["Name"],
style={"min-width": "8vw", "max-width": "15vw"},
)
)
elif attribute == "ID":
row.append(
html.Td(
html.A(
target["ID"],
href=target["drugbank_url"],
target="_blank",
)
)
)
elif attribute == "Gene":
row.append(
html.Td(
html.A(
target["Gene"],
href="https://www.uniprot.org/uniprot/"
+ target["ID"],
target="_blank",
)
)
)
elif attribute == "PDBID":
if target["PDBID"] != "Not Available":
row.append(
html.Td(
html.A(
target["PDBID"],
href="https://www.rcsb.org/structure/"
+ target["PDBID"],
target="_blank",
)
)
)
else:
row.append(html.Td(target["PDBID"]))
elif attribute in [
"Organism",
"Protein Class",
"Protein Family",
"Cellular Location",
]:
row.append(html.Td(target[attribute]))
else:
row.append(
html.Td(
dbc.Container(
", ".join(target[attribute]),
style={
"max-height": "20vh",
"overflow-y": "auto",
"word-break": "break-word",
"padding": "0px",
},
fluid=True,
),
style={
"min-width": "10vw",
"max-width": "20vw",
"padding-right": "0px",
},
)
)
table_body.append(html.Tr(row))
table_body = [html.Tbody(table_body)]
targets_table = dbc.Table(
table_header + table_body,
className="table table-hover",
bordered=True,
)
targets_href = "data:text/csv;charset=utf-8," + quote(
pd.DataFrame(
[
{
key: (
", ".join(value)
if isinstance(value, list)
else value
)
for key, value in d.items()
if key in attributes
}
for d in targets_data
],
columns=attributes,
).to_csv(sep="\t", index=False, encoding="utf-8")
)
results += [
html.Br(),
dbc.Row(
[
dbc.Col(html.H4("Inspected Targets"), xs=12, lg=10),
dbc.Col(
dbc.Row(
[
html.A(
dbc.Button(
"Download",
className="btn btn-outline-primary",
),
href=targets_href,
download="inspected_targets.tsv",
target="_blank",
),
dbc.Col(
html.A(
dbc.Row(
[
html.I(
className="fa fa-question-circle",
style={
"font-size": "1rem"
},
),
html.H5(
"Glossary",
style={
"margin-bottom": 0,
"margin-left": "0.5rem",
},
),
],
justify="center",
align="center",
),
id=prefix
+ "_inspected_table_targets_glossary",
href="/covidrugnet/help#glossary",
target="_blank",
),
align="center",
),
],
justify="end",
align="center",
)
),
],
justify="around",
align="center",
),
html.Br(),
targets_table,
]
return (
dbc.Container(results, fluid=True),
True,
False,
visibility,
open,
"%d Nodes" % len(data),
data,
)
elif len(data) >= 1:
return (
dbc.Container(results, fluid=True, style={"padding": "3%"}),
False,
True,
visibility,
open,
None,
data,
)
else:
return (
dbc.Container(results, fluid=True, style={"padding": "3%"}),
False,
True,
visibility,
open,
None,
[],
)
else:
return (
dbc.Container(results, fluid=True, style={"padding": "3%"}),
False,
True,
visibility,
open,
None,
[],
)
return inspected_table
def properties_table_callback(prefix, graph_properties_df, nodes):
@app.callback(
[
Output(prefix + "_graph_properties_table_container", "children"),
Output(prefix + "_download_graph_properties", "href"),
Output(prefix + "_search_properties", "options"),
],
[
Input(prefix + "_search_properties", "value"),
Input(prefix + "_properties_table_sorting", "value"),
Input(prefix + "_properties_table_rows", "value"),
Input(prefix + "_only_inspected_properties", "value"),
Input(prefix + "_inspected_cache", "value"),
],
)
def properties_table(search, sorting, rows, only_inspected, inspected_data):
df = graph_properties_df.copy()
if prefix == "drug_target":
df.drop(
"Clustering Coefficient", inplace=True, axis=1
) # removes the clustering coefficient column from the dataframe of the Drug-Target Networks, since it is bipartited and the clustering coefficient has no meaning in this case
if only_inspected and inspected_data:
df = df.loc[[node["nameID"] for node in inspected_data]]
options = [
{"label": name, "value": name}
for name in [node["nameID"] for node in inspected_data]
]
else:
options = [
{"label": data["nameID"], "value": data["nameID"]}
for data in [node["data"] for node in nodes]
]
if search:
df = df.loc[search]
sorting = sorting.split(",")
sorting = {"by": [sorting[0]], "ascending": bool(int(sorting[1]))}
df = df.sort_values(**sorting)
href = "data:text/csv;charset=utf-8," + quote(
df.to_csv(sep="\t", index=False, encoding="utf-8")
)
if rows != "all":
df = df.head(rows)
return (
dbc.Table.from_dataframe(
df,
bordered=True,
className="table table-hover",
id=prefix + "_graph_properties_table",
),
href,
options,
)
return properties_table
def group_highlighter_callback(prefix, nodes, maj):
def unite(conjunction, ll):
if not ll:
return []
ll = [set(l.split(",")) if isinstance(l, str) else l for l in ll if l]
if len(ll) > 1:
if conjunction == "OR":
return ll[0].union(*ll[1:])
else:
return ll[0].intersection(*ll[1:])
elif len(ll) == 1:
return ll[0]
else:
return ll
def centrality_filter(centrality, equality, value):
if value:
if equality == ">=":
return {
node["data"]["ID"]
for node in nodes
if node["data"][centrality] >= value
}
elif equality == ">":
return {
node["data"]["ID"]
for node in nodes
if node["data"][centrality] > value
}
elif equality == "=":
return {
node["data"]["ID"]
for node in nodes
if node["data"][centrality] == value
}
elif equality == "<=":
return {
node["data"]["ID"]
for node in nodes
if node["data"][centrality] <= value
}
elif equality == "<":
return {
node["data"]["ID"]
for node in nodes
if node["data"][centrality] < value
}
else:
return {}
if prefix == "drug_target":
properties = ["kind"]
elif prefix == "drug_projection":
properties = [
"ATC Code Level 1",
"ATC Code Level 2",
"ATC Code Level 3",
"ATC Code Level 4",
"Targets",
"Enzymes",
"Carriers",
"Transporters",
"Drug Interactions",
"Target Class",
"Trials Phases",
]
elif prefix == "target_projection":
properties = [
"STRING Interaction Partners",
"Drugs",
"Diseases",
"Organism",
"Protein Class",
"Protein Family",
"Cellular Location",
]
centralities = [
"Degree",
"Closeness Centrality",
"Betweenness Centrality",
"Eigenvector Centrality",
"Clustering Coefficient",
"VoteRank Score",
]
@app.callback(
[
Output(prefix + "_highlighted_cache", "value"),
Output(prefix + "_n_highlighted", "children"),
Output(prefix + "_download_group_highlighting_button_href", "href"),
Output(prefix + "_download_group_highlighting_button", "disabled"),
],
[Input(prefix + "_only_major_highlighted_groups", "value")]
+ [
Input(prefix + "_highlight_dropdown_" + property, "value")
for property in properties
]
+ [
Input(prefix + "_conjunction_" + property, "value")
for property in properties
]
+ [
Input(prefix + "_equality_" + centrality, "value")
for centrality in centralities
]
+ [
Input(prefix + "_highlight_value_" + centrality, "value")
for centrality in centralities
]
+ [Input(prefix + "_conjunction_general", "value")],
)
def group_highlighter(only_maj, *values):
if (
isinstance(only_maj, list) and len(only_maj) > 0 and only_maj[0] == True
): # could be avoided using daq BooleanSwitch
only_maj = True
else:
only_maj = False
highlighted_values = dict(
zip(
["highlighted_" + property for property in properties]
+ ["conjunction_" + property for property in properties]
+ ["equality_" + centrality for centrality in centralities]
+ ["value_" + centrality for centrality in centralities]
+ ["conjunction_general"],
values,
)
)
for centrality in centralities:
highlighted_values["highlighted_" + centrality] = centrality_filter(
centrality,
highlighted_values["equality_" + centrality],
highlighted_values["value_" + centrality],
)
highlighted = unite(
highlighted_values["conjunction_general"],
[
unite(
highlighted_values["conjunction_" + property],
highlighted_values["highlighted_" + property],
)
for property in properties
]
+ [
highlighted_values["highlighted_" + centrality]
for centrality in centralities
],
)
if highlighted:
if only_maj:
maj_id = list(nx.get_node_attributes(maj, "ID").values())
highlighted = [id for id in highlighted if id in maj_id]
attributes = ["Name", "ID"]
if prefix == "target_projection":
attributes += ["Gene"]
attributes += properties + centralities
href = "data:text/csv;charset=utf-8," + quote(
pd.DataFrame(
[
{
attribute: (
", ".join(node["data"][attribute])
if isinstance(node["data"][attribute], list)
else node["data"][attribute]
)
for attribute in attributes
}
for node in nodes
if node["data"]["ID"] in highlighted
],
columns=attributes,
).to_csv(sep="\t", index=False, encoding="utf-8")
)