import numpy as np

import pandas as pd

DEFAULT_CLUSTERPROFILE_KEYS = {

'item_key': 'Description', 'group_key': 'group', 'sizes_key': 'Count', 'color_key': 'pvalue',

'circle_key': 'qvalue'

}

def merge_clusterprofile_results(dataframes, groups, group_key='group', term_list=None):

assert len(dataframes) == len(groups)

merged_df = None

for _dataframe, _group in zip(dataframes, groups):

if term_list is None:

_sub_df = _dataframe

else:

_sub_df = _dataframe[_dataframe.index.isin(term_list)]

if not _sub_df.empty:

# _sub_df[group_key] = _group # SettingWithCopyWarning

_sub_df.insert(_sub_df.shape[1], group_key, _group)

if merged_df is not None:

merged_df = pd.concat((merged_df, _sub_df))

else:

merged_df = _sub_df

merged_df['qvalue'] = merged_df['qvalue'].map(lambda x: -np.log10(x))

merged_df['pvalue'] = merged_df['pvalue'].map(lambda x: -np.log10(x))

merged_df['p.adjust'] = merged_df['p.adjust'].map(lambda x: -np.log10(x))

return merged_df