def __init__(self):
load_extra_layouts()
super().__init__(id='graph',
layout=self.layout(),
style={'height': '99vh'},
elements=[],
stylesheet=self.stylesheet())
def get_solution_routing_cytoscape(solution: Solution) -> cyto.Cytoscape:
"""
Returns a cytoscape object showing the network topology and stream routing
"""
# elements
elements = []
nodes = []
edges = []
for n in solution.tc.N.values():
el = {}
el["data"] = {"id": n.id, "label": n.id}
if isinstance(n, end_system):
el["classes"] = "ES"
else:
el["classes"] = "SW"
nodes.append(el)
for l in solution.tc.L.values():
el = {}
el["data"] = {"source": l.src.id, "target": l.dest.id}
el["classes"] = "link"
edges.append(el)
for f_id, mtree in solution.tc.R.items():
for l in mtree.get_all_links(solution.tc):
el = {}
el["data"] = {"source": l.src.id, "target": l.dest.id}
el["classes"] = f_id
edges.append(el)
elements = flatten([nodes, edges])
# stylesheet
stylesheet = flatten(
[
_cytoscape_base_stylesheet(),
_cytoscape_base_stream_selectors(solution.tc.F_routed.keys()),
]
)
cyto.load_extra_layouts()
graph = cyto.Cytoscape(
id="cytoscape-routing-graph",
layout={"name": "cose"},
style={"width": "100%", "height": "800px"},
elements=elements,
stylesheet=stylesheet,
)
return graph
def main():
global parser
dcy.load_extra_layouts()
argparser = argparse.ArgumentParser(description='Stormspotter')
argparser.add_argument("--dbuser", "-dbu", required=True,
help='Username for neo4j', default="neo4j")
argparser.add_argument("--dbpass", "-dbp", required=True,
help='Password for neo4j')
argparser.add_argument("--db",
help='Url of database', default="bolt://localhost:7687")
args = argparser.parse_args()
# parser = DashParser("neo4j", "password")
parser = DashParser(args.dbuser, args.dbpass, args.db)
app.run_server(debug=True, threaded=True)
def __init__(self, graph_one, graph_two, app_display='default'):
"""
Initialises the dual graph interface and generates the interface layout.
:param graph_one: (Graph) The first graph for the comparison interface.
:param graph_two: (Graph) The second graph for the comparison interface.
:param app_display: (str) 'default' by default and 'jupyter notebook' for running Dash inside Jupyter Notebook.
"""
# Dash app styling with Bootstrap
if app_display == 'jupyter notebook':
self.app = JupyterDash(__name__,
external_stylesheets=[dbc.themes.BOOTSTRAP])
else:
self.app = Dash(__name__,
external_stylesheets=[dbc.themes.BOOTSTRAP])
# Setting input graphs as objest variables
cyto.load_extra_layouts()
self.graph_one = graph_one
self.graph_two = graph_two
# Getting a list of tuples with differnet edge connections
difference = graph_one.get_difference(graph_two)
# Updating the elements needed for the Dash Cytoscape Graph object
self.one_components = None
self.two_components = None
self._update_elements_dual(self.graph_one, difference, 1)
self._update_elements_dual(self.graph_two, difference, 2)
self.cyto_one = None
self.cyto_two = None
# Callback functions to allow simultaneous node selection when clicked
self.app.callback(Output('cytoscape_two',
'elements'), [Input('cytoscape', 'tapNode')],
[State('cytoscape_two', 'elements')])(
DualDashGraph._select_other_graph_node)
self.app.callback(Output('cytoscape', 'elements'),
[Input('cytoscape_two', 'tapNode')],
[State('cytoscape', 'elements')])(
DualDashGraph._select_other_graph_node)
def get_layout():
cyto.load_extra_layouts()
return html.Div([
cyto.Cytoscape(
id='cytoscape',
layout={
'name': 'dagre',
'nodeDimensionsIncludeLabels': True,
'animate': True,
},
elements=[],
style={'width': 'auto', 'height': '100vh'},
),
#class and feature names
html.Div(id='names', style={'display': 'none'}),
#decision tree
html.Div(id='tree', style={'display': 'none'}),
#visible nodes
html.Div(id='visible', style={'display': 'none'}),
#collapsed leaves
html.Div(id='collapsed_leaf', style={'display': 'none'}),
])
'opacity': 1,
'label': label,
'background-color': negative_color,
}
},
{
'selector': 'edge', # for the edges
'style': {
'line-color': '#C5D3E2', # very ligth blue
'curve-style': 'haystack'
}
}
]
# Necessary to get more cytoscape layouts
cyto.load_extra_layouts()
"""
#####################
## Middle window ##
#####################
"""
# This will appear above the cytoscape node graph
node_graph_layout = dbc.Row(
[
dbc.Col(
[
# Drop down selector for layout of cytoscape, at the top of the page
html.Main("Choose graph layout style:"),
dcc.Dropdown(
from propnet.web.layouts_home import home_layout
from propnet.web.layouts_interactive import interactive_layout
from propnet.web.layouts_correlate import correlate_layout
from propnet.web.layouts_explore import explore_layout
from propnet.web.layout_refs import refs_layout
from dash_cytoscape import load_extra_layouts
from propnet.web.utils import parse_path
from flask_caching import Cache
import logging
log = logging.getLogger(__name__)
load_extra_layouts()
# TODO: Fix math rendering
app = dash.Dash(__name__)
server = app.server
app.config.suppress_callback_exceptions = True # TODO: remove this?
app.scripts.config.serve_locally = True
app.title = "propnet"
route = dcc.Location(id='url', refresh=False)
cache = Cache(app.server,
config={
'CACHE_TYPE': 'filesystem',
'CACHE_DIR': '.tmp'
})
def prep_dash(elements):
cyto.load_extra_layouts()
app = dash.Dash(external_stylesheets=[dbc.themes.LUX])
app.layout = html.Div([
dbc.Row([
dbc.Col(
[
cyto.Cytoscape(
id='org-chart',
layout={
'name': 'klay'
}, # circle, dagre & klay work fast, cose & spread work slow
style={
'width': '100%',
'height': '95vh'
},
elements=elements,
# minZoom = 0.02, maxZoom = 2,
stylesheet=[{
'selector': 'node',
'style': {
'label': 'data(label)',
'height': 'data(soc_size)',
'width': 'data(soc_size)'
}
}, {
"selector": 'edge',
'style': {
"curve-style": "unbundled-bezier",
"opacity": 1,
'width': 1
}
}, {
'selector': '.city_1',
'style': {
'background-color': '#00DCA8'
}
}, {
'selector': '.city_2',
'style': {
'background-color': '#DABC3B'
}
}, {
'selector': '.city_3',
'style': {
'background-color': '#179BFF'
}
}, {
'selector': '.city_oth',
'style': {
'background-color': '#FF4D53'
}
}, {
'selector': '.city_unk',
'style': {
'background-color': '#AAAAAA'
}
}, {
'selector': '.square',
'style': {
'shape': 'square'
}
}, {
'selector': '.circle',
'style': {
'shape': 'circle'
}
}, {
'selector': '.diamond',
'style': {
'shape': 'diamond'
}
}, {
'selector': '.triangle',
'style': {
'shape': 'triangle'
}
}, {
'selector': '.border_1',
'style': {
'border-width': 2,
'border-color': '#111111'
}
}, {
'selector': '.border_0',
'style': {
'border-width': 1,
'border-color': '#FF0000'
}
}, {
'selector': '.link_1',
'style': {
'line-color': '#941100'
}
}, {
'selector': '.link_2',
'style': {
'line-color': '#929000'
}
}, {
'selector': '.link_3',
'style': {
'line-color': '#008F00'
}
}, {
'selector': '.link_4',
'style': {
'line-color': '#0096FF'
}
}, {
'selector': '.link_5',
'style': {
'line-color': '#FF9300'
}
}, {
'selector': '.link_6',
'style': {
'line-color': '#00FA92'
}
}, {
'selector': '.link_7',
'style': {
'line-color': '#FF85FF'
}
}, {
'selector': '.link_8',
'style': {
'line-color': '#9437FF'
}
}, {
'selector': '.link_0',
'style': {
'line-color': '#A9A9A9'
}
}, {
'selector': '.parent_1',
'style': {
'background-color': '#FFD0CB'
}
}, {
'selector': '.parent_2',
'style': {
'background-color': '#FFFD99'
}
}, {
'selector': '.parent_3',
'style': {
'background-color': '#CCFFCC'
}
}, {
'selector': '.parent_4',
'style': {
'background-color': '#99D6FF'
}
}, {
'selector': '.parent_5',
'style': {
'background-color': '#FFE2BD'
}
}, {
'selector': '.parent_6',
'style': {
'background-color': '#BCFFE3'
}
}, {
'selector': '.parent_7',
'style': {
'background-color': '#FFCCFF'
}
}, {
'selector': '.parent_8',
'style': {
'background-color': '#E3CCFF'
}
}, {
'selector': '.parent_0',
'style': {
'background-color': '#EEEEEE'
}
}])
],
width=12)
])
])
return app
def graph(G, mode="external", **kwargs):
"""
G: a multidirectional graph
kwargs are passed to the Jupyter_Dash.run_server() function. Some usefull arguments are:
mode: "inline" to run app inside the jupyter nodebook, default is external
debug: True or False, Usefull to catch errors during development.
"""
import dash
from jupyter_dash import JupyterDash
import dash_cytoscape as cyto
from dash.dependencies import Output, Input
import dash_html_components as html
import dash_core_components as dcc
import dash_table
import networkx as nx
import scConnect as cn
import plotly.graph_objs as go
import plotly.io as pio
import pandas as pd
import numpy as np
import json
import matplotlib
import matplotlib.pyplot as plt
pio.templates.default = "plotly_white"
cyto.load_extra_layouts()
JupyterDash.infer_jupyter_proxy_config()
app = JupyterDash(__name__)
server = app.server
# Add a modified index string to change the title to scConnect
app.index_string = '''
<!DOCTYPE html>
<html>
<head>
{%metas%}
<title>scConnect</title>
{%favicon%}
{%css%}
</head>
<body>
{%app_entry%}
<footer>
{%config%}
{%scripts%}
{%renderer%}
</body>
</html>
'''
# Add colors to each node
nodes = pd.Categorical(G.nodes())
# make a list of RGBA tuples, one for each node
colors = plt.cm.tab20c(nodes.codes / len(nodes.codes), bytes=True)
# zip node to color
color_map_nodes = dict(zip(nodes, colors))
# add these colors to original graph
for node, color in color_map_nodes.items():
G.nodes[node]["color"] = color[0:3] # Save only RGB
# Add colors to edges(source node color) for G
for u, v, k in G.edges(keys=True):
G.edges[u, v, k]["color"] = color_map_nodes[u][0:3]
# load graph into used formes
def G_to_flat(G, weight):
G_flat = cn.graph.flatten_graph(G, weight=weight, log=True)
# Add colors to edges(source node color) for G_flat
for u, v, in G_flat.edges():
G_flat.edges[u, v]["color"] = color_map_nodes[u][0:3]
return G_flat
# produce full graph variante to extract metadata
G_flat = G_to_flat(G, weight="score")
G_split = cn.graph.split_graph(G)
# find and sort all found interactions
interactions = list(G_split.keys())
interactions.sort()
G_cyto = nx.cytoscape_data(G_flat)
# get min and max weight for all edges for flat and normal graph
#weights = [d["weight"] for u, v, d in G_flat.edges(data=True)]
scores = [d["score"] for u, v, d in G.edges(data=True)]
cent = [d["centrality"] for n, d in G.nodes(data=True)]
# prepare data for network graph
nodes = G_cyto["elements"]["nodes"]
elements = []
# collect all available genes
genes = list(nodes[0]["data"]["genes"].keys())
# Styling parameters
font_size = 20
# Style for network graph
default_stylesheet = [{
'selector': 'node',
'style': {
'background-color': 'data(color)',
'label': 'data(id)',
'shape': 'ellipse',
'opacity': 1,
'font-size': f'{font_size}',
'font-weight': 'bold',
'text-wrap': 'wrap',
'text-max-width': "100px",
'text-opacity': 1,
'text-outline-color': "white",
'text-outline-opacity': 1,
'text-outline-width': 2
}
}, {
'selector': 'node:selected',
'style': {
'background-color': 'data(color)',
'label': 'data(id)',
'shape': 'ellipse',
'opacity': 1,
'border-color': "black",
'border-width': "5"
}
}, {
'selector': 'edge',
'style': {
'line-color': 'data(color)',
"opacity": 0.7,
"curve-style": "unbundled-bezier",
"width": "data(weight)",
"target-arrow-shape": "vee",
"target-arrow-color": "black",
'z-index': 1,
'font-size': f'{font_size}'
}
}, {
'selector': 'edge:selected',
'style': {
'line-color': 'red',
'line-style': "dashed",
'opacity': 1,
'z-index': 10,
}
}]
app.layout = html.Div(
className="wrapper",
children=[ # wrapper
html.Div(
className="header",
children=[ # header
html.Img(src="assets/logo.png", alt="scConnect logo"),
html.Div(
className="graph-info",
id="graph-stat",
children=[
html.
H3(f'Loaded graph with {len(G.nodes())} nodes and {len(G.edges())} edges'
)
])
]),
html.Div(
className="network-settings",
children=[ # network settings
html.H2("Network settings", style={"text-align":
"center"}),
html.Label("Interactions"),
dcc.Dropdown(id="network-interaction",
options=[{
'label': "all interactions",
'value': "all"
}] + [{
'label': interaction,
'value': interaction
} for interaction in interactions],
value="all"),
# select if only significant ligands and receptors should be shown
html.Label("Graph weight:"),
dcc.RadioItems(id="weight-select",
options=[{
"label": "Score",
"value": "score"
}, {
"label": "Log score",
"value": "log_score"
}, {
"label": "Specificity",
"value": "specificity"
}, {
"label": "Importance",
"value": "importance"
}],
value="importance",
labelStyle={
'display': 'block',
"margin-left": "50px"
},
style={
"padding": "10px",
"margin": "auto"
}),
html.Label("Graph Layout"),
dcc.Dropdown(
id="network-layout",
options=[{
'label':
name.capitalize(),
'value':
name
} for name in [
'grid', 'random', 'circle', 'cose', 'concentric',
'breadthfirst', 'cose-bilkent', 'cola', 'euler',
'spread', 'dagre', 'klay'
]],
value="circle",
clearable=False),
html.Label("Weight Filter",
style={
"paddingBottom": 500,
"paddingTop": 500
}),
dcc.
Slider( # min, max and value are set dynamically via a callback
id="network-filter",
step=0.001,
updatemode="drag",
tooltip={
"always_visible": True,
"placement": "right"
},
),
html.Label("Node size"),
dcc.RangeSlider(id="node-size",
value=[10, 50],
min=0,
max=100,
updatemode="drag"),
html.Label("Select gene"),
dcc.Dropdown(
id="gene_dropdown",
options=[{
"label": gene,
"value": gene
} for gene in genes],
clearable=True,
placeholder="Color by gene expression",
),
# Store node colors "hidden" for gene expresison
html.Div(id="node-colors",
style={"display": "none"},
children=[""]),
html.Div(id="min-max", children=[]),
# Click to download image of network graph
html.Button(children="Download current view",
id="download-network-graph",
style={"margin": "10px"})
]), # end network settings
html.Div(
id="network-graph",
className="network-graph",
children=[ # network graph
html.H2("Network graph", style={"text-align": "center"}),
cyto.Cytoscape(id="cyto-graph",
style={
'width': '100%',
'height': '80vh'
},
stylesheet=default_stylesheet,
elements=elements,
autoRefreshLayout=True,
zoomingEnabled=False)
]), # end network graph
html.Div(
className="sankey-settings",
children=[ # network settings
html.H2("Sankey Settings", style={"text-align": "center"}),
html.Label("Weight Filter"),
dcc.Slider(id="sankey-filter",
min=min(scores),
max=max(scores),
value=0.75,
step=0.001,
updatemode="drag",
tooltip={
"always_visible": True,
"placement": "right"
}),
html.Label("Toggle weighted"),
dcc.RadioItems(id="sankey-toggle",
options=[{
"label": "Score",
"value": "score"
}, {
"label": "Log score",
"value": "log_score"
}, {
"label": "Specificity",
"value": "specificity"
}, {
"label": "Importance",
"value": "importance"
}],
value="importance",
labelStyle={"display": "block"})
]), # end network settings
html.Div(
className="sankey",
id="sankey",
children=[ # sankey graph
html.H2("Sankey graph", style={"text-align": "center"}),
dcc.Graph(id="sankey-graph")
]), # end sankey graph
html.Div(
className="interaction-list",
children=[ # interaction list
html.Div(id="selection",
children=[
html.H2("Interactions",
style={"text-align": "center"}),
html.H3(id="edge-info",
style={"text-align": "center"}),
dcc.Graph(id="interaction-scatter"),
html.Div(id="interaction-selection",
style={"display": "none"},
children=[""])
]),
html.Div(children=[
dash_table.DataTable(
id="edge-selection",
page_size=20,
style_table={
"overflowX": "scroll",
"overflowY": "scroll",
"height": "50vh",
"width": "95%"
},
style_cell_conditional=[{
"if": {
"column_id": "interaction"
},
"textAlign": "left"
}, {
"if": {
"column_id": "receptorfamily"
},
"textAlign": "left"
}, {
"if": {
"column_id": "pubmedid"
},
"textAlign": "left"
}],
style_header={
"fontWeight": "bold",
"maxWidth": "200px",
"minWidth": "70px"
},
style_data={
"maxWidth": "200px",
"minWidth": "70px",
"textOverflow": "ellipsis"
},
sort_action="native",
fixed_rows={
'headers': True,
'data': 0
})
])
]), # end interaction list
html.Div(
className="L-R-scores",
children=[ # ligand and receptor lists
html.H2("Ligand and receptors",
style={"text-align": "center"}),
html.Div(children=[
html.H3(
id="selected-node",
style={"text-align": "center"},
children=["Select a node in the notwork graph"]),
html.Label("Search for ligands and receptors:",
style={"margin-right": "10px"}),
dcc.Input(id="filter_l_r",
type="search",
value="",
placeholder="Search")
]),
dcc.Tabs([
dcc.Tab(label="Ligands",
children=[
dcc.Graph(id="ligand-graph",
config=dict(autosizable=True,
responsive=True)),
dash_table.DataTable(
id="ligand-table",
page_size=20,
style_table={
"overflowX": "scroll",
"overflowY": "scroll",
"height": "50vh",
"width": "95%"
},
style_cell_conditional=[{
"if": {
"column_id": "Ligand"
},
"textAlign":
"left"
}],
style_header={
"fontWeight": "bold",
"maxWidth": "200px",
"minWidth": "70px"
},
style_data={
"maxWidth": "200px",
"minWidth": "70px",
"textOverflow": "ellipsis"
},
sort_action="native",
fixed_rows={
'headers': True,
'data': 0
})
]),
dcc.Tab(label="Receptors",
children=[
dcc.Graph(id="receptor-graph",
config=dict(autosizable=True,
responsive=True)),
dash_table.DataTable(
id="receptor-table",
page_size=20,
style_table={
"overflowX": "scroll",
"overflowY": "scroll",
"height": "50vh",
"width": "95%"
},
style_cell_conditional=[{
"if": {
"column_id": "Receptor"
},
"textAlign":
"left"
}],
style_header={
"fontWeight": "bold",
"maxWidth": "200px",
"minWidth": "70px"
},
style_data={
"maxWidth": "200px",
"minWidth": "70px",
"textOverflow": "ellipsis"
},
sort_action="native",
fixed_rows={
'headers': True,
'data': 0
})
])
])
]) # end ligand receptor list
]) # end wrapper
# Instantiate the graph and produce the bounderies for filters
@app.callback([
Output("cyto-graph", "elements"),
Output("network-filter", "min"),
Output("network-filter", "max"),
Output("network-filter", "value")
], [
Input("network-interaction", "value"),
Input("weight-select", "value")
])
def make_graph(interaction, score):
G_flat = G_to_flat(G, score)
if interaction == "all": # if no interaction is selected, use full graph
G_cyto = nx.cytoscape_data(G_flat)
weights = [d["weight"] for u, v, d in G_flat.edges(data=True)]
# prepare data for network graph
nodes = G_cyto["elements"]["nodes"]
edges = G_cyto["elements"]["edges"]
elements = nodes + edges
return elements, min(weights), max(weights), np.mean(weights)
else: # an interaction is selected, select only that interaction
G_split = cn.graph.split_graph(G)
G_split_flat = G_to_flat(G_split[interaction], score)
G_cyto = nx.cytoscape_data(G_split_flat)
weights = [
d["weight"] for u, v, d in G_split_flat.edges(data=True)
]
# prepare data for network graph
nodes = G_cyto["elements"]["nodes"]
edges = G_cyto["elements"]["edges"]
elements = nodes + edges
return elements, min(weights), max(weights), np.mean(weights)
# Change layout of network graph
@app.callback(Output("cyto-graph", "layout"),
[Input("network-layout", "value")])
def update_network_layout(layout):
return {"name": layout, "automate": True, "fit": True}
# Choose gene to color nodes by
@app.callback(
[Output("node-colors", "children"),
Output("min-max", "children")], [Input("gene_dropdown", "value")])
def calculate_colors(gene):
if gene is None:
return [None, ""]
# get all gene expression values for selected gene
gene_data = {
celltype["data"]["id"]: celltype["data"]["genes"][gene]
for celltype in nodes
}
min_value = min(gene_data.values())
max_value = max(gene_data.values())
# package min max expression information to a list that will be returned
expression = html.Ul(children=[
html.Li(f"minimum gene expression: {min_value}"),
html.Li(f"maximum gene expression: {max_value}")
])
cmap = matplotlib.cm.get_cmap("coolwarm")
color_dict = dict()
for k, v in gene_data.items():
color_dict[k] = {"rgb": cmap(v, bytes=True)[0:3], "expression": v}
color = pd.Series(color_dict)
return color.to_json(), expression
# Select visible edges of network graph depending on filter value
# node color depending on selected gene
# width of edges
@app.callback(Output("cyto-graph", "stylesheet"), [
Input("network-filter", "value"),
Input("network-filter", "min"),
Input("network-filter", "max"),
Input("node-size", "value"),
Input("node-colors", "children")
])
def style_network_graph(th, min_weight, max_weight, size, colors):
# create a filter for edges
filter_style = [{
"selector": f"edge[weight < {th}]",
"style": {
"display": "none"
}
}, {
"selector": "node",
"style": {
'height':
f'mapData(centrality, {min(cent)}, {max(cent)}, {size[0]}, {size[1]})',
'width':
f'mapData(centrality, {min(cent)}, {max(cent)}, {size[0]}, {size[1]})'
}
}]
# create a color style for nodes based on gene expression
if isinstance(colors, str):
colors = pd.read_json(colors, typ="series", convert_dates=False)
color_style = [{
'selector': f'node[id = "{str(index)}"]',
'style': {
'background-color': f'rgb{tuple(colors[index]["rgb"])}'
}
} for index in colors.index]
filter_style += color_style
else:
color_style = {
"selector": "node",
"style": {
'background-color': 'BFD7B5'
}
}
# Map edges width to a set min and max value (scale for visibility)
edge_style = [{
"selector": "edge",
"style": {
"width": f"mapData(weight, {min_weight}, {max_weight}, 1, 10)"
}
}]
return default_stylesheet + filter_style + edge_style
# download an image of current network graph view
@app.callback(Output("cyto-graph", "generateImage"),
Input("download-network-graph", "n_clicks"))
def download_networkgraph_image(get_request):
if get_request == None:
return dict()
return {"type": "svg", "action": "download"}
# Produce a table of all edge data from tapped edge
@app.callback([
Output("edge-info", "children"),
Output("edge-selection", "columns"),
Output("edge-selection", "data")
], [
Input("cyto-graph", "tapEdgeData"),
Input("interaction-selection", "children")
])
def update_data(edge, selection):
import pandas as pd
import json
# check if an edge has really been clicked, return default otherwise
if edge is None:
return ["", None, None]
info = f"Interactions from {edge['source']} to {edge['target']}."
# map visible names for columns with columns in edge[interaction]
columns = [{
"name": "Interaction",
"id": "interaction"
}, {
"name": "Receptor Family",
"id": "receptorfamily"
}, {
"name": "Score",
"id": "score"
}, {
"name": "Log10(score)",
"id": "log_score"
}, {
"name": "Specificity",
"id": "specificity"
}, {
"name": "Importance",
"id": "importance"
}, {
"name": "Ligand z-score",
"id": "ligand_zscore"
}, {
"name": "Ligand p-value",
"id": "ligand_pval"
}, {
"name": "Receptor z-score",
"id": "receptor_zscore"
}, {
"name": "Receptor p-value",
"id": "receptor_pval"
}, {
"name": "PubMed ID",
"id": "pubmedid"
}]
interactions = pd.DataFrame(edge["interactions"])[[
"interaction", "receptorfamily", "score", "log_score",
"specificity", "importance", "ligand_zscore", "ligand_pval",
"receptor_zscore", "receptor_pval", "pubmedid"
]]
# Sort values based on score
interactions.sort_values(by="score", ascending=False, inplace=True)
# round values for scores to two decimals
interactions[[
"score", "log_score", "specificity", "importance", "ligand_zscore",
"receptor_zscore"
]] = interactions[[
"score", "log_score", "specificity", "importance", "ligand_zscore",
"receptor_zscore"
]].round(decimals=2)
interactions[["ligand_pval", "receptor_pval"
]] = interactions[["ligand_pval",
"receptor_pval"]].round(decimals=4)
# if selection from interaction graph, filter dataframe
if selection != "":
selection = json.loads(selection)
interactions = interactions.loc[interactions["interaction"].isin(
selection)]
records = interactions.to_dict("records")
return [info, columns, records]
@app.callback([Output("interaction-scatter", "figure")],
[Input("cyto-graph", "tapEdgeData")])
def interaction_scatter_plot(edge):
import plotly.express as px
fig = go.Figure()
if not isinstance(edge, dict):
return [
fig,
]
interactions = pd.DataFrame(edge["interactions"])[[
"interaction", "receptorfamily", "score", "log_score",
"ligand_zscore", "ligand_pval", "receptor_zscore", "receptor_pval",
"specificity", "importance", "pubmedid"
]]
# add 10% to the min and max value to not clip the datapoint
range_x = (-max(interactions["log_score"]) * 0.1,
max(interactions["log_score"]) * 1.1)
range_y = (-max(interactions["specificity"]) * 0.1,
max(interactions["specificity"]) * 1.1)
#interactions["specificity"] = np.log10( interactions["specificity"])
fig = px.scatter(interactions,
x="log_score",
range_x=range_x,
y="specificity",
range_y=range_y,
color="importance",
hover_name="interaction",
hover_data=[
"ligand_pval", "receptor_pval", "score",
"specificity", "receptorfamily"
],
color_continuous_scale=px.colors.sequential.Viridis_r,
labels={
"ligand_zscore": "Ligand Z-score",
"receptor_zscore": "Receptor Z-score",
"log_score": "log(Interaction score)",
"score": "Interaction score",
"specificity": "Specificity",
"importance": "Importance",
"receptorfamily": "Receptor family",
"pubmedid": "PubMed ID",
"ligand_pval": "Ligand p-value",
"receptor_pval": "Receptor p-value"
})
return [
fig,
]
@app.callback(Output("interaction-selection", "children"),
[Input("interaction-scatter", "selectedData")])
def interaction_select(selected_data):
import json
if isinstance(selected_data, dict):
interactions = [
point["hovertext"] for point in selected_data["points"]
]
else:
return ""
return json.dumps(interactions)
# Produce ligand and receptor graphs based on tapped node
@app.callback([
Output("ligand-graph", "figure"),
Output("receptor-graph", "figure"),
Output("selected-node", "children")
], [Input("cyto-graph", "tapNodeData"),
Input("filter_l_r", "value")])
def plot_l_r_expression(node, filter_text):
# set output variables to empty figures
ligand_fig = go.Figure()
receptor_fig = go.Figure()
node_id = "Select a node in the network graph"
if isinstance(node, dict):
import plotly.express as px
node_id = node["id"]
ligands_score = pd.DataFrame.from_dict(node["ligands_score"],
orient="index",
columns=["Score"])
ligands_zscore = np.log2(
pd.DataFrame.from_dict(node["ligands_zscore"],
orient="index",
columns=["Z-score"]))
ligands_corr_pval = pd.DataFrame.from_dict(
node["ligands_corr_pval"], orient="index", columns=["p-value"])
ligands_merge = ligands_score.merge(ligands_zscore,
how="left",
left_index=True,
right_index=True)
ligands_merge = ligands_merge.merge(ligands_corr_pval,
how="left",
left_index=True,
right_index=True)
ligands_merge["log(score + 1)"] = np.log10(ligands_merge["Score"] +
1)
ligands_merge["Significant"] = [
True if p_val < 0.05 else False
for p_val in ligands_merge["p-value"]
]
ligands_merge["-log(p-value)"] = -np.log10(
ligands_merge["p-value"])
if filter_text != "":
ligands_merge = ligands_merge.filter(like=filter_text, axis=0)
ligand_fig = px.scatter(ligands_merge,
x="log(score + 1)",
y="-log(p-value)",
color="Significant",
hover_name=ligands_merge.index,
hover_data=["Score", "Z-score", "p-value"])
receptors_score = pd.DataFrame.from_dict(node["receptors_score"],
orient="index",
columns=["Score"])
receptors_zscore = np.log2(
pd.DataFrame.from_dict(node["receptors_zscore"],
orient="index",
columns=["Z-score"]))
receptors_corr_pval = pd.DataFrame.from_dict(
node["receptors_corr_pval"],
orient="index",
columns=["p-value"])
receptors_merge = receptors_score.merge(receptors_zscore,
how="left",
left_index=True,
right_index=True)
receptors_merge = receptors_merge.merge(receptors_corr_pval,
how="left",
left_index=True,
right_index=True)
receptors_merge["log(score + 1)"] = np.log10(
receptors_merge["Score"] + 1)
receptors_merge["Significant"] = [
True if p_val < 0.05 else False
for p_val in receptors_merge["p-value"]
]
receptors_merge["-log(p-value)"] = -np.log10(
receptors_merge["p-value"])
if filter_text != "":
receptors_merge = receptors_merge.filter(like=filter_text,
axis=0)
receptor_fig = px.scatter(
receptors_merge,
x="log(score + 1)",
y="-log(p-value)",
color="Significant",
hover_name=receptors_merge.index,
hover_data=["Score", "Z-score", "p-value"])
return [ligand_fig, receptor_fig, node_id]
# Builds a sankey graph based on the tapped node (store in global G_s)
G_s = nx.MultiDiGraph() #variable holding sankey graph
@app.callback([
Output("sankey-filter", "min"),
Output("sankey-filter", "max"),
Output("sankey-filter", "value")
], [Input("cyto-graph", "tapNodeData"),
Input("sankey-toggle", "value")])
def build_sankey_graph(node, score):
import numpy as np
# If no node has been selected, dont try to build graph
if node is None:
return (0, 0, 0)
node = node["id"]
# Find all interactions where node is target or source node
nonlocal G_s
G_s = nx.MultiDiGraph() # reset content
weight = list(
) # list to store all weights (used to set min and max for the filter)
for n, nbrs in G.adj.items(
): # graph has been modified by network graph before
for nbr, edict in nbrs.items():
if n == node:
for e, d in edict.items():
G_s.add_edge(n, " Post " + nbr, **d)
weight.append(d[score])
if nbr == node:
for e, d in edict.items():
G_s.add_edge("Pre " + n, nbr, **d)
weight.append(d[score])
if len(weight) == 0:
weight = [0, 1]
if score == "specificity":
# set default start value to specificity value for ligand and receptor
# p-value of (0.05 and 0.05)/2 = 1.3
return (min(weight), max(weight), 1.3)
return (min(weight), max(weight), np.mean(weight))
@app.callback(Output("sankey-graph", "figure"), [
Input("sankey-filter", "value"),
Input("sankey-toggle", "value"),
Input("cyto-graph", "tapNodeData")
])
def filter_sankey_graph(th, score, node):
if node:
node = node["id"]
_G_s = nx.MultiDiGraph()
for u, v, n, d in G_s.edges(data=True, keys=True):
if d[score] > th:
_G_s.add_edge(u, v, n, **d)
_G_s.add_nodes_from(G_s.nodes(data=True))
edges = nx.to_pandas_edgelist(_G_s)
if len(edges) < 1:
fig = dict()
return fig
# add same color scheme as network graph
for node_s in _G_s.nodes():
if " Post" in node_s:
original_node = str(node_s).split(sep=" Post")[1]
elif "Pre " in node_s:
original_node = str(node_s).split(sep="Pre ")[1]
else:
original_node = str(node_s)
new_color = color_map_nodes[original_node.strip()]
G_s.nodes[node_s]["color"] = new_color
nodes = G_s.nodes()
node_map = {cluster: id for id, cluster in enumerate(list(nodes))}
sankey = go.Sankey(node=dict(pad=15,
thickness=20,
line=dict(color="black", width=0.5),
label=list(nodes),
color=[
f'rgb{tuple(d["color"][0:3])}'
for n, d in G_s.nodes(data=True)
]),
link=dict(
source=list(edges["source"].map(node_map)),
target=list(edges["target"].map(node_map)),
value=list(edges[score]),
label=edges["interaction"]))
data = [sankey]
layout = go.Layout(autosize=True,
title=f"Interactions: {node}",
font=dict(size=font_size))
fig = go.Figure(data=data, layout=layout)
return fig
@app.callback(
[Output("ligand-table", "columns"),
Output("ligand-table", "data")], [
Input("ligand-graph", "figure"),
Input("ligand-graph", "selectedData")
])
def select_ligands(figure, selected):
import json
ligands = []
score = []
zscore = []
pval = []
for group in figure["data"]:
for ligand in group["hovertext"]:
ligands.append(ligand)
for data in group["customdata"]:
score.append(data[0])
zscore.append(data[1])
pval.append(data[2])
df = pd.DataFrame({
"Ligand": ligands,
"Score": score,
"Z-score": zscore,
"P-value": pval
})
df.index = df["Ligand"]
df.sort_values(by="Score", ascending=False, inplace=True)
if isinstance(selected, dict):
filt = []
for point in selected["points"]:
filt.append(point["hovertext"])
df = df.loc[filt]
columns = [{
"name": "Ligand",
"id": "Ligand"
}, {
"name": "Score",
"id": "Score"
}, {
"name": "Z-score",
"id": "Z-score"
}, {
"name": "P-value",
"id": "P-value"
}]
data = df.to_dict("records")
return columns, data
@app.callback([
Output("receptor-table", "columns"),
Output("receptor-table", "data")
], [
Input("receptor-graph", "figure"),
Input("receptor-graph", "selectedData")
])
def select_ligands(figure, selected):
import json
receptors = []
score = []
zscore = []
pval = []
for group in figure["data"]:
for receptor in group["hovertext"]:
receptors.append(receptor)
for data in group["customdata"]:
score.append(data[0])
zscore.append(data[1])
pval.append(data[2])
df = pd.DataFrame({
"Receptor": receptors,
"Score": score,
"Z-score": zscore,
"P-value": pval
})
df.index = df["Receptor"]
df.sort_values(by="Score", ascending=False, inplace=True)
if isinstance(selected, dict):
filt = []
for point in selected["points"]:
filt.append(point["hovertext"])
df = df.loc[filt]
columns = [{
"name": "Receptor",
"id": "Receptor"
}, {
"name": "Score",
"id": "Score"
}, {
"name": "Z-score",
"id": "Z-score"
}, {
"name": "P-value",
"id": "P-value"
}]
data = df.to_dict("records")
return columns, data
# Run server
app.run_server(**kwargs)
def __init__(self, input_graph, app_display='default'):
"""
Initialises the DashGraph object from the Graph class object.
Dash creates a mini Flask server to visualise the graphs.
:param app_display: (str) 'default' by default and 'jupyter notebook' for running Dash inside Jupyter Notebook.
:param input_graph: (Graph) Graph class from graph.py.
"""
self.graph = None
# Dash app styling with Bootstrap
if app_display == 'jupyter notebook':
self.app = JupyterDash(__name__,
external_stylesheets=[dbc.themes.BOOTSTRAP])
else:
self.app = Dash(__name__,
external_stylesheets=[dbc.themes.BOOTSTRAP])
# Graph class object
self.graph = input_graph
# The dictionary of the nodes coordinates
self.pos = self.graph.get_pos()
# Colours of nodes
self.colour_groups = {}
# If colours have been assigned in Graph class, add styling
if self.graph.get_node_colours():
colour_map = self.graph.get_node_colours()
self._assign_colours_to_groups(list(colour_map.keys()))
self.weights = []
self.elements = []
self._update_elements()
# Load the different graph layouts
cyto.load_extra_layouts()
self.layout_options = ['cose-bilkent', 'cola', 'spread']
self.statistics = [
'graph_summary', 'average_degree_connectivity',
'average_neighbor_degree', 'betweenness_centrality'
]
# Load default stylesheet
self.stylesheet = None
self.stylesheet = self._get_default_stylesheet()
# Append stylesheet for colour and size
# If sizes have been set in the Graph class
self._style_colours()
if self.graph.get_node_sizes():
self._assign_sizes()
self.cyto_graph = None
# Callback functions to hook frontend elements to functions
self.app.callback(Output('cytoscape', 'layout'),
[Input('dropdown-layout', 'value')])(
DashGraph._update_cytoscape_layout)
self.app.callback(Output('json-output', 'children'),
[Input('dropdown-stat', 'value')])(
self._update_stat_json)
self.app.callback(Output('cytoscape', 'elements'),
[Input('rounding_decimals', 'value')])(
self._round_decimals)
def loadextralayouts():
import dash_cytoscape as cyto
cyto.load_extra_layouts() # Load extra layouts
