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callbacks.py
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callbacks.py
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# INTERACTIVITY CODE
# Import packages
import pandas as pd
import re
import dash
import requests
import numpy as np
import plotly.express as px
from dash import html, dcc, callback, ctx, dash_table
from dash.dependencies import Input, Output, State, ALL
import dash_bio as dashbio
from dash_bio.utils import PdbParser, create_mol3d_style
import dash_bootstrap_components as dbc
from dash.exceptions import PreventUpdate
import dash_cytoscape as cyto
# Import data
from data.data_prep import *
# Import functions
from functions.a1_protein_coords import *
from functions.a2_protein_strucuture import *
from functions.a3_create_logos import *
from functions.b1_table import *
from functions.b2_sunburst_fig import *
# Import aesthetetics
from assets.color_scheme import *
# ---------------------------- CALLBACKS -------------------------------
# 0. Updating dropdown list based on button
@callback(
Output('search-dropdown', 'options'),
[
Input('taxonomy', 'n_clicks'),
Input('accession-number', 'n_clicks'),
Input('node', 'n_clicks')
],
)
def update_dropdown_options(taxa_click=None, acc_click=None, node_click=None):
'''
A callback to update the dropdown menu based on the selected
category.
Args:
taxa_click: Clicking the Taxonomy button
acc_click: Clicking the Accession number button
node_click: Clicking the Node button
Returns:
Populates the dropdown menu with a list of all TAs based on one
of the 3 categories mentioned above.
'''
# The dataset used
df = df_netflax
#
ctx = dash.callback_context
button_id = ctx.triggered[0]['prop_id'].split('.')[0]
if button_id == 'taxonomy':
# Make a list with all values of all the taxonomy columns
options = list(set(pd.concat(
[df['Superkingdom'],
df['Phylum'],
df['Class'],
df['Order'],
df['Family'],
df['Genus'],
df['Taxa']]).tolist()))
options = sorted(options)
elif button_id == 'accession-number':
# Make a list of all ac
options = list(set(pd.concat([df['AT Accession'], df['T Accession']]).tolist()))
options = sorted(options)
elif button_id == 'node':
# Make a list of all nodes
options = list(set(pd.concat([df['AT Domain'], df['T Domain']]).tolist()))
options = sorted(options)
else:
# Otherwise the dropdown menu is empty
options = []
return [{'label': option, 'value': option} for option in options]
# 1. SLIDER FOR TAXONOMIC LEVEL
@callback(
Output('a1_taxonomy_sunburst', 'figure'),
Output('table-container', 'children'),
Input('taxonomy_level_slider', 'value'),
Input('search-dropdown', 'value'),
Input({"type": "ta-button", "index": ALL}, "n_clicks"),
State({"type": "ta-button", "index": ALL}, "id")
)
def update_sunburst_level(level=None, search_term=None, button_clicks=None, button_ids=None):
'''
A callback to update the taxonomy wheel figure through:
1. Search by taxonomy, accession, or node
2. The slider (taxonomy level)
Default level is set at 'Order' (3).
Args:
level: The taxonomic level
search_term: The protein accession number
button_index:
Returns:
Returns an updated wheel figure based on the search input and
the taxonomic level. It also updates the results output box on
the right-hand side. This is unique for each category.
'''
# -------------------------- VIEW STRUCTURE ------------------------
if button_clicks:
for clicks, button_id in zip(button_clicks, button_ids):
if clicks and button_id:
button_name = button_id.get('index')
search_term = button_name.split('(')[0]
break
# ------------------------------ SEARCH ----------------------------
if search_term != None:
# 1. Search by accession
if search_term.startswith('WP_'):
df = df_netflax.copy()
# Filter the dataframe for the selected search term
mask = (df['AT Accession'].str.contains(search_term, case=False)) | (df['T Accession'].str.contains(search_term, case=False))
filtered_df = df.loc[mask]
# Get the last ring based on the taxa of the selected row
last_ring = ""
if len(filtered_df[filtered_df['AT Accession'] == search_term]['Taxa'].values) > 0:
last_ring = filtered_df[filtered_df['AT Accession'] == search_term]['Taxa'].values[0]
elif len(filtered_df[filtered_df['T Accession'] == search_term]['Taxa'].values) > 0:
last_ring = filtered_df[filtered_df['T Accession'] == search_term]['Taxa'].values[0]
# Set the color based on matching or non-matching segments
df['color'] = wedge_non_highlight
df.loc[df['Taxa'] == last_ring, 'color'] = wedge_highlight
# Updated dataset
dataset = df_netflax[df_netflax.values == search_term]
level = 6
# Get protein logos
fig_antitoxin, fig_toxin = create_protein_logos(search_term)
# Get structure for protein
structure_data, styles, chain_sequence = visualising_protein(search_term)
# Get sunburst fig
sunburst_fig = create_sunburst_figure(df, level, 'color', default='no')
results_div = html.Div([
html.Div([
html.Br(style={'padding':'10px'}),
dbc.Container([
dbc.Row(
html.H6(
f'Search results for "{search_term}"',
style={
'margin-top': '40px',
'padding': '20px',
'background-color': accent_medium,
'border-radius': '10px',
},
)
),
dbc.Row([
dbc.Col([
html.H6('Antitoxin'),
dcc.Graph(
id='antitoxin-logo',
figure=fig_antitoxin,
config={
'displayModeBar': False
}
),
], width={'size': 5}),
dbc.Col(width={'size': 1}),
dbc.Col([
html.H6('Toxin'),
dcc.Graph(
id='toxin-logo',
figure=fig_toxin,
config={
'displayModeBar': False
}
),
], width={'size': 5}),
]),
dbc.Row(
html.H6('Antitoxin-Toxin Structure')
),
dbc.Row([
dbc.Col([
dashbio.Molecule3dViewer(
id='dashbio-default-molecule3d',
modelData=structure_data,
styles=styles
),
], width={'size': 10},
style={'padding':'10px'})
]),
], style={
'background-color': page_background,
'border-radius': '10px',
'width': '100%',
'margin': '0 auto',
})
])
], style={'max-height': '90vh'})
return sunburst_fig, results_div
# 2. Search by node
elif search_term.startswith('D') or search_term.startswith('M') or search_term.startswith('Panacea'):
# Filter the dataframe for the selected search term
df = df_netflax.copy()
mask = (df['AT Domain'].str.contains(search_term, case=False)) | (df['T Domain'].str.contains(search_term, case=False))
filtered_df = df.loc[mask]
# Get the last ring based on the taxa of the selected row
last_ring = ""
if len(filtered_df[filtered_df['AT Domain'] == search_term]['Taxa'].values) > 0:
last_ring = filtered_df[filtered_df['AT Domain'] == search_term]['Taxa'].values[0]
elif len(filtered_df[filtered_df['T Domain'] == search_term]['Taxa'].values) > 0:
last_ring = filtered_df[filtered_df['T Domain'] == search_term]['Taxa'].values[0]
# Set the color based on matching or non-matching segments
df['color'] = np.where(df['Taxa'].isin(filtered_df['Taxa']), wedge_highlight, wedge_non_highlight)
# New dataset
level = 6
dataset = df[df.values == search_term]
# Creating results
sunburst_fig = create_sunburst_figure(df, level, 'color', default='no')
table_title = f'Dataset filtered based on node: {search_term}'
table = create_table(dataset)
results_div = create_results_div(table_title, table, dataset)
return sunburst_fig, results_div
# 3. Search by taxonomy at any level
else:
df = df_netflax.copy()
# Set the color of the matching segment to red
df['color'] = wedge_non_highlight
# Determine in which column the search term is present
if search_term in df['Superkingdom'].values:
level = 0
df.loc[df['Superkingdom'] == search_term, 'color'] = wedge_highlight
elif search_term in df['Phylum'].values:
level = 1
df.loc[df['Phylum'] == search_term, 'color'] = wedge_highlight
elif search_term in df['Class'].values:
level = 2
df.loc[df['Class'] == search_term, 'color'] = wedge_highlight
elif search_term in df['Order'].values:
level = 3
df.loc[df['Order'] == search_term, 'color'] = wedge_highlight
elif search_term in df['Family'].values:
level = 4
df.loc[df['Family'] == search_term, 'color'] = wedge_highlight
elif search_term in df['Genus'].values:
level = 5
df.loc[df['Genus'] == search_term, 'color'] = wedge_highlight
elif search_term in df['Taxa'].values:
level = 6
df.loc[df['Taxa'] == search_term, 'color'] = wedge_highlight
else:
print(f'{search_term} does not exist')
# New dataset
level = 6
dataset = df[df.values == search_term]
# Creating results
sunburst_fig = create_sunburst_figure(df, level, 'color', default='no')
table_title = f'Dataset filtered based on {search_term}'
table = create_table(dataset)
results_div = create_results_div(table_title, table, dataset)
return sunburst_fig, results_div
# ----------------------------- DEFAULT ----------------------------
# Create a sunburst chart with the selected number of levels
if level is None:
level = 3 # default value if level is not selected
# New dataset
dataset = df_netflax
# Creating results
sunburst_fig = create_sunburst_figure(dataset, level)
table_title = f'Complete NetFlax Dataset'
table = create_table(dataset)
results_div = create_results_div(table_title, table, dataset)
return sunburst_fig, results_div
# Update callback for Molecule3dViewer
@callback(
Output('molecule-info-container', 'children'),
Input('dashbio-default-molecule3d', 'selectedAtomIds')
)
def display_molecule_info(click_info):
if click_info is None:
return html.Div() # Return empty div if no click info available
else:
atom_id = click_info['atom']
chain_id = click_info['chain']
element = click_info['elem']
residue_name = click_info['residue_name']
return html.Div([
html.Div(f'Element: {element}'),
html.Div(f'Chain: {chain_id}'),
html.Div(f'Residue name: {residue_name}'),
html.Br()
])
@callback(
Output("download-dataframe-csv", "data"),
Input("btn_csv", "n_clicks"),
prevent_initial_call=True,
)
def download_csv(n_clicks):
df = create_results_div[2]
return dcc.send_data_frame(df.to_csv, "mydf.csv", index=False)