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Python Network.dat['nodes']['col']方法代码示例

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?那么恭喜您, 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.........
开发者ID:ErwanDavid,项目名称:clustergrammer.js,代码行数:103,代码来源:enrichr_functions.py


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