# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
#
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License
# as published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU Affero General Public License for more details.
#
# This program is available from Source Forge: at GeneNetwork Project
# (sourceforge.net/projects/genenetwork/).
#
# Contact Dr. Robert W. Williams at rwilliams@uthsc.edu
#
#
# This module is used by GeneNetwork project (www.genenetwork.org)
import scipy
import simplejson as json
from gn2.base.trait import create_trait
from gn2.base import data_set
from gn2.utility import helper_functions
from gn2.utility import corr_result_helpers
from gn2.utility.tools import GN2_BRANCH_URL
class NetworkGraph:
def __init__(self, start_vars):
trait_db_list = [trait.strip()
for trait in start_vars['trait_list'].split(',')]
helper_functions.get_trait_db_obs(self, trait_db_list)
self.all_sample_list = []
self.traits = []
for trait_db in self.trait_list:
this_trait = trait_db[0]
self.traits.append(this_trait)
this_sample_data = this_trait.data
for sample in this_sample_data:
if sample not in self.all_sample_list:
self.all_sample_list.append(sample)
self.sample_data = []
for trait_db in self.trait_list:
this_trait = trait_db[0]
this_sample_data = this_trait.data
this_trait_vals = []
for sample in self.all_sample_list:
if sample in this_sample_data:
this_trait_vals.append(this_sample_data[sample].value)
else:
this_trait_vals.append('')
self.sample_data.append(this_trait_vals)
# ZS: Variable set to the lowest overlapping samples in order to notify user, or 8, whichever is lower (since 8 is when we want to display warning)
self.lowest_overlap = 8
self.nodes_list = []
self.edges_list = []
for trait_db in self.trait_list:
this_trait = trait_db[0]
this_db = trait_db[1]
this_db_samples = this_db.group.all_samples_ordered()
this_sample_data = this_trait.data
corr_result_row = []
is_spearman = False # ZS: To determine if it's above or below the diagonal
max_corr = 0 # ZS: Used to determine whether node should be hidden when correlation coefficient slider is used
for target in self.trait_list:
target_trait = target[0]
target_db = target[1]
if str(this_trait) == str(target_trait) and str(this_db) == str(target_db):
continue
target_samples = target_db.group.all_samples_ordered()
target_sample_data = target_trait.data
this_trait_vals = []
target_vals = []
for index, sample in enumerate(target_samples):
if (sample in this_sample_data) and (sample in target_sample_data):
sample_value = this_sample_data[sample].value
target_sample_value = target_sample_data[sample].value
this_trait_vals.append(sample_value)
target_vals.append(target_sample_value)
this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(
this_trait_vals, target_vals)
if num_overlap < self.lowest_overlap:
self.lowest_overlap = num_overlap
if num_overlap < 2:
continue
else:
pearson_r, pearson_p = scipy.stats.pearsonr(
this_trait_vals, target_vals)
if is_spearman == False:
sample_r, sample_p = pearson_r, pearson_p
if sample_r == 1:
continue
else:
sample_r, sample_p = scipy.stats.spearmanr(
this_trait_vals, target_vals)
if -1 <= sample_r < -0.7:
color = "#0000ff"
width = 3
elif -0.7 <= sample_r < -0.5:
color = "#00ff00"
width = 2
elif -0.5 <= sample_r < 0:
color = "#000000"
width = 0.5
elif 0 <= sample_r < 0.5:
color = "#ffc0cb"
width = 0.5
elif 0.5 <= sample_r < 0.7:
color = "#ffa500"
width = 2
elif 0.7 <= sample_r <= 1:
color = "#ff0000"
width = 3
else:
color = "#000000"
width = 0
if abs(sample_r) > max_corr:
max_corr = abs(sample_r)
edge_data = {'id': f"{str(this_trait.name)}:{str(this_trait.dataset.name)}" + '_to_' + f"{str(target_trait.name)}:{str(target_trait.dataset.name)}",
'source': str(this_trait.name) + ":" + str(this_trait.dataset.name),
'target': str(target_trait.name) + ":" + str(target_trait.dataset.name),
'correlation': round(sample_r, 3),
'abs_corr': abs(round(sample_r, 3)),
'p_value': round(sample_p, 3),
'overlap': num_overlap,
'color': color,
'width': width}
edge_dict = {'data': edge_data}
self.edges_list.append(edge_dict)
if trait_db[1].type == "ProbeSet":
node_dict = {'data': {'id': str(this_trait.name) + ":" + str(this_trait.dataset.name),
'label': this_trait.symbol,
'symbol': this_trait.symbol,
'geneid': this_trait.geneid,
'omim': this_trait.omim,
'max_corr': max_corr}}
elif trait_db[1].type == "Publish":
node_dict = {'data': {'id': str(this_trait.name) + ":" + str(this_trait.dataset.name),
'label': this_trait.name,
'max_corr': max_corr}}
else:
node_dict = {'data': {'id': str(this_trait.name) + ":" + str(this_trait.dataset.name),
'label': this_trait.name,
'max_corr': max_corr}}
self.nodes_list.append(node_dict)
self.elements = json.dumps(self.nodes_list + self.edges_list)
self.gn2_url = GN2_BRANCH_URL
groups = []
for sample in self.all_sample_list:
groups.append(1)
self.js_data = dict(traits=[trait.name for trait in self.traits],
groups=groups,
cols=list(range(len(self.traits))),
rows=list(range(len(self.traits))),
samples=self.all_sample_list,
sample_data=self.sample_data,
elements=self.elements,)
