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Commit cf01fd7f authored by Kateryna Nikulina's avatar Kateryna Nikulina
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script for analysis

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import numpy as np
import pandas as pd
import glob
import scipy.stats as stats
def get_feature_pairs(df_with_grouped_intersections, name, bad):
'''
Search for combinations of parameters with low or high intersections
Args:
df_with_grouped_intersections: a pandas dataframe, a table obtained from get_intersections function,
grouped by hospital case and parameters' combination
name: a string, a hospital case (e.g. 'full df_1 in full df_2')
bad: boolean, determines what parameters are needed - with low or high intersections
Return:
A pandas series with combinations of parameters
'''
data = df_with_grouped_intersections[df_with_grouped_intersections['Name']==name]
#find the lowest limit for normal values - below are outliers
q1,q3 = df_with_grouped_intersections[df_with_grouped_intersections["Name"]==name].Intersection.quantile([0.25, 0.75])
low = q1 - 1.5*(q3-q1)
if bad: #bad==true -> return bad pairs
df = df_with_grouped_intersections[(df_with_grouped_intersections["Name"]==name) & (df_with_grouped_intersections['Intersection']<low)]
else: #bad==false -> return good pairs
df = df_with_grouped_intersections[(df_with_grouped_intersections["Name"]==name) & (df_with_grouped_intersections['Intersection']>=low)]
return df.Parameters
def get_features_count_df(df_with_grouped_intersections, name, bad):
'''
Counts occurences of a parameter
Args:
df_with_grouped_intersections: a pandas dataframe, a table obtained from get_intersections function,
grouped by hospital case and parameters' combination
name: a string, a hospital case (e.g. 'full df_1 in full df_2')
bad: boolean, determines what parameters are needed - with low or high intersections
Return:
df: a pandas dataframe, parameters and their count
'''
params = get_feature_pairs(df_with_grouped_intersections, name, bad)
#clean string values, get only parameter names
param_list=[]
for x in params:
a,b=x.split(", ",1)
param_list.append(a)
param_list.append(b)
#count parameter appearance
dct=dict(zip(param_list,[param_list.count(i) for i in param_list]))
sorted_dct = dict(sorted(dct.items(), key=lambda item: item[1], reverse=True))
if bad:
df = pd.DataFrame(sorted_dct.items(), columns=['Parameter', f'Count_in_bad_pairs'])
else:
df = pd.DataFrame(sorted_dct.items(), columns=['Parameter', f'Count_in_good_pairs'])
return df
def fishers_exact_test(df, name, param_count):
'''
Perform enrichment analysis
Args:
df: a pandas dataframe, a table obtained from get_intersections function,
grouped by hospital case and parameters' combination
name: a string, a hospital case (e.g. 'full df_1 in full df_2')
param_count: int, total count of parameters common for both hospitals
Return:
df: a dataframe, with information about each parameter: count, p value, etc.
'''
param_count = param_count - 1
#get bad pairs and their count
bad_pairs = get_feature_pairs(df_with_grouped_intersections=df, name=name, bad=True)
df_bad=get_features_count_df(df_with_grouped_intersections=df, name=name, bad=True)
#get good pairs and their count
good_pairs = get_feature_pairs(df_with_grouped_intersections=df, name=name, bad=False)
df_good=get_features_count_df(df_with_grouped_intersections=df, name=name, bad=False)
if len(bad_pairs)!=0:
enrich_df= pd.merge(df_bad, df_good, how='outer', on='Parameter').fillna(0)
#perform Fisher's exact test
odds_list=[]
pval_list=[]
for i in enrich_df.index:
v00=enrich_df.iloc[i,[1,2]][0] # count of a parameter presence in a bad list
v10=enrich_df.iloc[i,[1,2]][1] # count of a parameter presence in a good list
odds,pval=stats.fisher_exact([[v00,(len(bad_pairs)-v00)],[v10,(len(good_pairs)-v10)]])
odds_list.append(odds)
pval_list.append(pval)
enrich_df['Name']=name
enrich_df["Parameter_count"] = param_count #parameter count
enrich_df["Ratio_in_bad_pairs"] = np.round(enrich_df["Count_in_bad_pairs"]/enrich_df["Parameter_count"], 2)
enrich_df["odds_ratio"] = odds_list
enrich_df['p_value'] = pval_list
return enrich_df
else: #there are no low intersections
enrich_df = pd.DataFrame()
return enrich_df
def produce_summary_tables_ch(path_to_ch_outputs, dataset_labels):
'''
Computes parameters with the lowest intersections, their count and the mean intersections for each datasets
Args:
path_to_ch_outputs: a string, a path to a folder containing the intersections of convex hull analysis
dataset_labels: a list of strings, names of the datasets used in convex hull analysis computations
Returns:
mean_coverage_df: dataframe, mean intersection for a dataset
bad_params_df: dataframe, names of parameters that cause the lowest intersections for a case
bad_params_df_count: dataframe, count of parameters with the lowest intersections
'''
all_df = pd.DataFrame()
files = glob.glob(path_to_ch_outputs)
for file in files:
ch_int = pd.read_csv(file, index_col=0)
all_df = pd.concat([all_df,ch_int])
all_df.Parameters = all_df.Parameters.str.replace(' ', '')
all_df = all_df.reset_index()
all_df = pd.merge(all_df, all_df.Parameters.str.split(',', 1, expand = True), left_index=True, right_index=True)
### get labels from datasets
mean_coverage_df = pd.DataFrame(columns = dataset_labels, index = dataset_labels)
bad_params_df_count = pd.DataFrame(columns = dataset_labels, index = dataset_labels)
bad_params_df = pd.DataFrame(columns = dataset_labels, index = dataset_labels)
### for each case of dataset intersection (dataset 1 in 2 or vice versa, etc..)
for case in all_df.Name.unique():
ch_int = all_df[(all_df.Name == case)]
params = set(ch_int[1].unique()).union(set(ch_int[0].unique()))
cover_list = []
for param in params:
### take median intersection for each of parameters for a particular case of dataset intersection
cover_list.append(ch_int[ch_int.Parameters.str.contains(param)].Intersection.median())
int_df = pd.DataFrame(cover_list, index = params, columns=["Intersection"])
### here choose bad parameters based on criterium - outliers on the boxplot
chosen = int_df[int_df.Intersection<(int_df.quantile(0.25) - 1.5*(int_df.quantile(0.75) - int_df.quantile(0.25)))[0]]
### extract dataset names
indexes = case.replace('full ','',2)
indexes = indexes.replace('sampled ','').replace(' in ', ',')
indexes = indexes.split(',')
mean_coverage_df.loc[indexes[1], indexes[0]] = int_df.Intersection.mean()
bad_params_df_count.loc[indexes[1], indexes[0]] = chosen.shape[0]
bad_params_df.loc[indexes[1], indexes[0]] = ', '.join(list(chosen.index.to_series()))
bad_params_df_count = bad_params_df_count.astype(float)
mean_coverage_df = mean_coverage_df.astype(float)
return mean_coverage_df, bad_params_df, bad_params_df_count
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