本文整理汇总了Python中clustergrammer.Network.dat['nodes']['col']方法的典型用法代码示例。如果您正苦于以下问题：Python Network.dat['nodes']['col']方法的具体用法？Python Network.dat['nodes']['col']怎么用？Python Network.dat['nodes']['col']使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类clustergrammer.Network的用法示例。
在下文中一共展示了Network.dat['nodes']['col']方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: clust_from_response
# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import dat['nodes']['col'] [as 别名]
def clust_from_response(response_list):
from clustergrammer import Network
import scipy
import json
import pandas as pd
import math
from copy import deepcopy
print('----------------------')
print('enrichr_clust_from_response')
print('----------------------')
ini_enr = transfer_to_enr_dict( response_list )
enr = []
scores = {}
score_types = ['combined_score','pval','zscore']
for score_type in score_types:
scores[score_type] = pd.Series()
for inst_enr in ini_enr:
if inst_enr['combined_score'] > 0:
# make series of enriched terms with scores
for score_type in score_types:
# collect the scores of the enriched terms
if score_type == 'combined_score':
scores[score_type][inst_enr['name']] = inst_enr[score_type]
if score_type == 'pval':
scores[score_type][inst_enr['name']] = -math.log(inst_enr[score_type])
if score_type == 'zscore':
scores[score_type][inst_enr['name']] = -inst_enr[score_type]
# keep enrichement values
enr.append(inst_enr)
# sort and normalize the scores
for score_type in score_types:
scores[score_type] = scores[score_type]/scores[score_type].max()
scores[score_type].sort(ascending=False)
number_of_enriched_terms = len(scores['combined_score'])
enr_score_types = ['combined_score','pval','zscore']
if number_of_enriched_terms <10:
num_dict = {'ten':10}
elif number_of_enriched_terms <20:
num_dict = {'ten':10, 'twenty':20}
else:
num_dict = {'ten':10, 'twenty':20, 'thirty':30}
# gather lists of top scores
top_terms = {}
for enr_type in enr_score_types:
top_terms[enr_type] = {}
for num_terms in num_dict.keys():
inst_num = num_dict[num_terms]
top_terms[enr_type][num_terms] = scores[enr_type].index.tolist()[: inst_num]
# gather the terms that should be kept - they are at the top of the score list
keep_terms = []
for inst_enr_score in top_terms:
for tmp_num in num_dict.keys():
keep_terms.extend( top_terms[inst_enr_score][tmp_num] )
keep_terms = list(set(keep_terms))
# keep enriched terms that are at the top 10 based on at least one score
keep_enr = []
for inst_enr in enr:
if inst_enr['name'] in keep_terms:
keep_enr.append(inst_enr)
# fill in full matrix
#######################
# genes
row_node_names = []
# enriched terms
col_node_names = []
# gather information from the list of enriched terms
for inst_enr in keep_enr:
col_node_names.append(inst_enr['name'])
row_node_names.extend(inst_enr['int_genes'])
row_node_names = sorted(list(set(row_node_names)))
net = Network()
net.dat['nodes']['row'] = row_node_names
net.dat['nodes']['col'] = col_node_names
net.dat['mat'] = scipy.zeros([len(row_node_names),len(col_node_names)])
for inst_enr in keep_enr:
inst_term = inst_enr['name']
#.........这里部分代码省略.........