Python Network.dat_to_df方法代码示例

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import dat_to_df [as 别名]
def make_plex_matrix():
  '''
  Make a cell line matrix with plex rows and cell line columns.
  This will be used as a negative control that should show worsening correlation
  as data is normalized/filtered.
  '''
  import numpy as np
  import pandas as pd
  from clustergrammer import Network

  # load cl_info
  net = Network()
  cl_info = net.load_json_to_dict('../cell_line_info/cell_line_info_dict.json')

  # load cell line expression
  net.load_file('../CCLE_gene_expression/CCLE_NSCLC_all_genes.txt')
  tmp_df = net.dat_to_df()
  df = tmp_df['mat']

  cols = df.columns.tolist()

  rows = range(9)
  rows = [i+1 for i in rows]
  print(rows)

  mat = np.zeros((len(rows), len(cols)))

  for inst_col in cols:

    for inst_cl in cl_info:

      if inst_col in inst_cl:
        inst_plex = int(cl_info[inst_cl]['Plex'])

        if inst_plex != -1:
          # print(inst_col + ' in ' + inst_cl + ': ' + str(inst_plex))

          row_index = rows.index(inst_plex)
          col_index = cols.index(inst_col)

          mat[row_index, col_index] = 1


  df_plex = pd.DataFrame(data=mat, columns=cols, index=rows)

  filename = '../lung_cellline_3_1_16/lung_cl_all_ptm/precalc_processed/' + \
            'exp-plex.txt'
  df_plex.to_csv(filename, sep='\t')

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import dat_to_df [as 别名]
def main():
  import numpy as np
  import pandas as pd
  from clustergrammer import Network

  rtk_list = load_rtks()

  net = Network()
  net.load_file('txt/tmp_cst_drug_treat_cl.txt')
  df_dict = net.dat_to_df()

  inst_df = df_dict['mat']

  inst_df = inst_df.ix[rtk_list]

  inst_df.to_csv('txt/RTK_exp_in_drug_treat_cl.txt', sep='\t')

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import dat_to_df [as 别名]
def make_json_from_tsv(name):
  '''
  make a clustergrammer json from a tsv file
  '''
  from clustergrammer import Network

  print('\n' + name)

  net = Network()

  filename = 'txt/'+ name + '.txt'

  net.load_file(filename)

  df = net.dat_to_df()

  net.swap_nan_for_zero()

  # zscore first to get the columns distributions to be similar
  net.normalize(axis='col', norm_type='zscore', keep_orig=True)

  # filter the rows to keep the perts with the largest normalizes values
  net.filter_N_top('row', 1000)

  num_rows = net.dat['mat'].shape[0]
  num_cols = net.dat['mat'].shape[1]

  print('num_rows ' + str(num_rows))
  print('num_cols ' + str(num_cols))

  if num_cols < 50 or num_rows < 1000:

    views = ['N_row_sum']
    net.make_clust(dist_type='cos', views=views)
    export_filename = 'json/' + name + '.json'
    net.write_json_to_file('viz', export_filename)

  else:
    print('did not cluster, too many columns ')

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import dat_to_df [as 别名]
def reproduce_Mark_correlation_matrix():
  import pandas as pd
  from scipy.spatial.distance import squareform
  from clustergrammer import Network
  from copy import deepcopy

  dist_vect = calc_custom_dist(data_type='ptm_none', dist_metric='correlation',
                              pairwise='True')


  dist_mat = squareform(dist_vect)

  # make similarity matrix
  dist_mat = 1 - dist_mat

  net = Network()

  data_type = 'ptm_none'

  filename = '../lung_cellline_3_1_16/lung_cl_all_ptm/precalc_processed/' + \
             data_type + '.txt'

  # load file and export dataframe
  net = deepcopy(Network())
  net.load_file(filename)
  net.swap_nan_for_zero()
  tmp_df = net.dat_to_df()
  df = tmp_df['mat']

  cols = df.columns.tolist()
  rows = cols

  mark_df = pd.DataFrame(data=dist_mat, columns=cols, index=rows)

  save_filename = '../lung_cellline_3_1_16/lung_cl_all_ptm/precalc_processed/' \
             + 'Mark_corr_sim_mat' + '.txt'
  mark_df.to_csv(save_filename, sep='\t')

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import dat_to_df [as 别名]

#.........这里部分代码省略.........
  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']
    col_index = col_node_names.index(inst_term)

    # use combined score for full matrix - will not be seen in viz
    tmp_score = scores['combined_score'][inst_term]
    net.dat['node_info']['col']['value'].append(tmp_score)

    for inst_gene in inst_enr['int_genes']:
      row_index = row_node_names.index(inst_gene)

      # save association
      net.dat['mat'][row_index, col_index] = 1

  # cluster full matrix
  #############################
  # do not make multiple views
  views = ['']

  if len(net.dat['nodes']['row']) > 1:
    net.make_clust(dist_type='jaccard', views=views, dendro=False)
  else:
    net.make_clust(dist_type='jaccard', views=views, dendro=False, run_clustering=False)

  # get dataframe from full matrix
  df = net.dat_to_df()

  for score_type in score_types:

    for num_terms in num_dict:

      inst_df = deepcopy(df)
      inst_net = deepcopy(Network())

      inst_df['mat'] = inst_df['mat'][top_terms[score_type][num_terms]]

      # load back into net
      inst_net.df_to_dat(inst_df)

      # make views
      if len(net.dat['nodes']['row']) > 1:
        inst_net.make_clust(dist_type='jaccard', views=['N_row_sum'], dendro=False)
      else:
        inst_net.make_clust(dist_type='jaccard', views=['N_row_sum'], dendro=False, run_clustering = False)

      inst_views = inst_net.viz['views']

      # add score_type to views
      for inst_view in inst_views:

        inst_view['N_col_sum'] = num_dict[num_terms]

        inst_view['enr_score_type'] = score_type

        # add values to col_nodes and order according to rank
        for inst_col in inst_view['nodes']['col_nodes']:

          inst_col['rank'] = len(top_terms[score_type][num_terms]) - top_terms[score_type][num_terms].index(inst_col['name'])

          inst_name = inst_col['name']
          inst_col['value'] = scores[score_type][inst_name]

      # add views to main network
      net.viz['views'].extend(inst_views)

  return net