diff options
Diffstat (limited to 'gn2/wqflask/network_graph')
| -rw-r--r-- | gn2/wqflask/network_graph/__init__.py | 0 | ||||
| -rw-r--r-- | gn2/wqflask/network_graph/network_graph.py | 188 |
2 files changed, 188 insertions, 0 deletions
diff --git a/gn2/wqflask/network_graph/__init__.py b/gn2/wqflask/network_graph/__init__.py new file mode 100644 index 00000000..e69de29b --- /dev/null +++ b/gn2/wqflask/network_graph/__init__.py diff --git a/gn2/wqflask/network_graph/network_graph.py b/gn2/wqflask/network_graph/network_graph.py new file mode 100644 index 00000000..d60d50ff --- /dev/null +++ b/gn2/wqflask/network_graph/network_graph.py @@ -0,0 +1,188 @@ +# 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,) |