Part 6: Execution of overrepresentation analysis (ORA) in Python

import pandas as pd
from gseapy.plot import gseaplot
import gseapy as gp
import numpy as np
import matplotlib.pyplot as plt
from gseapy import dotplot
import os
from gseapy import barplot, dotplot

path_ORA = "C:\\Users\\shaki\\Downloads\\BackgroundORA-GSE109565"
dir_list_ORA = os.listdir(path_ORA)
print("Files and directories in '", path_ORA, "' :")
print(dir_list_ORA)

Files and directories in ' C:\Users\shaki\Downloads\BackgroundORA-GSE109565 ' :
['Glyphosate concentration 1-GSE109565.top.table.tsv', 'Glyphosate concentration 2-GSE109565.top.table.tsv', 'Glyphosate concentration 3-GSE109565.top.table.tsv', 'PCB concentration 1-GSE109565.top.table.tsv', 'PCB concentration 2-GSE109565.top.table.tsv', 'PCB concentration 3-GSE109565.top.table.tsv', 'Roundup concentration-GSE109565.top.table.tsv']

list_of_names_ORA = ['Glyphosate concentration 1-GSE109565.top.table','Glyphosate concentration 2-GSE109565.top.table','Glyphosate concentration 3-GSE109565.top.table','Roundup concentration-GSE109565.top.table','PCB concentration 1-GSE109565.top.table', 'PCB concentration 2-GSE109565.top.table','PCB concentration 3-GSE109565.top.table']
dataframes_list_ORA = []

for i in range(len(list_of_names_ORA)):
 
    temp_df_ORA = pd.read_csv("./BackgroundORA-GSE109565/" + list_of_names_ORA[i] + ".tsv", sep='\t')
    dataframes_list_ORA.append(temp_df_ORA)

combined_dataframes_list_ORA=pd.concat([dataframes_list_ORA[0],dataframes_list_ORA[1],dataframes_list_ORA[2],dataframes_list_ORA[3],dataframes_list_ORA[4],dataframes_list_ORA[5],dataframes_list_ORA[6]], ignore_index=True, axis=0)
genelist= combined_dataframes_list_ORA['Symbol'].copy()
gene_list = genelist.squeeze().str.strip().to_list()
Backgroundgenelist= [x for x in gene_list if x==x]

genelist_df1 = dataframes_list_ORA[4]
genelist_PCB1= genelist_df1[genelist_df1['padj'] < 0.05]
genelist_PCB1=genelist_PCB1['Symbol'].copy()
gene_listPCB1 = genelist_PCB1.squeeze().str.strip().to_list()
gene_list_PCB1= [x for x in gene_listPCB1 if x==x]

genelist_df2 = dataframes_list_ORA[5]
genelist_PCB2= genelist_df2[genelist_df2['padj'] < 0.05]
genelist_PCB2=genelist_PCB2['Symbol'].copy()
gene_listPCB2 = genelist_PCB2.squeeze().str.strip().to_list()
gene_list_PCB2= [x for x in gene_listPCB2 if x==x]

genelist_df3 = dataframes_list_ORA[6]
genelist_PCB3= genelist_df3[genelist_df3['padj'] < 0.05]
genelist_PCB3=genelist_PCB3['Symbol'].copy()
gene_listPCB3 = genelist_PCB3.squeeze().str.strip().to_list()
gene_list_PCB3= [x for x in gene_listPCB3 if x==x]

genelist_df4 = dataframes_list_ORA[3]
genelist_RU= genelist_df4[genelist_df4['padj'] < 0.05]
genelist_RU=genelist_RU['Symbol'].copy()
gene_list_RU= genelist_RU.to_list()

enr_bg_PCB1 = gp.enrichr(gene_list=gene_list_PCB1,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GSE109565_ORApathwaytable', 
                 verbose=True)

2025-04-14 14:42:38,015 [INFO] Run: WikiPathways_2024_Human 
2025-04-14 14:42:39,730 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-14 14:42:40,914 [INFO] Done.

enr_bg_PCB1.results.head(5)

enr_bg_PCB2 = gp.enrichr(gene_list=gene_list_PCB2,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GSE109565_ORApathwaytable', 
                 verbose=True)

2025-04-14 14:46:49,822 [INFO] Run: WikiPathways_2024_Human 
2025-04-14 14:46:51,457 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-14 14:46:51,963 [INFO] Done.

enr_bg_PCB2.results.head(5)

enr_bg_PCB3 = gp.enrichr(gene_list=gene_list_PCB3,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GSE109565_ORApathwaytable', 
                 verbose=True)

2025-04-14 14:47:15,835 [INFO] Run: WikiPathways_2024_Human 
2025-04-14 14:47:17,078 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-14 14:47:17,523 [INFO] Done.

enr_bg_PCB3.results.head(5)

ax_PCB1 = barplot(enr_bg_PCB1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
                title= 'PCB concentration 1 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='PCB concentration 1 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_PCB2 = barplot(enr_bg_PCB2.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'PCB concentration 2 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='PCB concentration 2 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_PCB3 = barplot(enr_bg_PCB3.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'PCB concentration 3 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='PCB concentration 3 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

path_ORA = "C:\\Users\\shaki\\Downloads\\BackgroundORA-EMEXP3583"
dir_list_ORA = os.listdir(path_ORA)
print("Files and directories in '", path_ORA, "' :")
print(dir_list_ORA)

Files and directories in ' C:\Users\shaki\Downloads\BackgroundORA-EMEXP3583 ' :
['topTable_Ag._.1.3_24 - H2O.control_.0.0_24.csv', 'topTable_Ag._.1.3_48 - H2O.control_.0.0_48.csv', 'topTable_AgNP._.12.1_24- H2O.control_.0.0_24.csv', 'topTable_AgNP._.12.1_48 - H2O.control_.0.0_48.csv', 'topTable_H2O_0.0_48 - H2O.control_.0.0_24.csv']

list_of_names_ORA = ['topTable_Ag._.1.3_24 - H2O.control_.0.0_24', 'topTable_Ag._.1.3_48 - H2O.control_.0.0_48','topTable_AgNP._.12.1_24- H2O.control_.0.0_24','topTable_AgNP._.12.1_48 - H2O.control_.0.0_48','topTable_H2O_0.0_48 - H2O.control_.0.0_24']
dataframes_list_ORA = []

for i in range(len(list_of_names_ORA)):
 
    temp_df_ORA = pd.read_csv("./BackgroundORA-EMEXP3583/" + list_of_names_ORA[i] + ".csv", comment=';')
    dataframes_list_ORA.append(temp_df_ORA)

combined_dataframes_list_ORA=pd.concat([dataframes_list_ORA[0],dataframes_list_ORA[1],dataframes_list_ORA[2],dataframes_list_ORA[3],dataframes_list_ORA[4]], ignore_index=True, axis=0)
genelist= combined_dataframes_list_ORA['gene_name'].copy()
gene_list = genelist.squeeze().str.strip().to_list()
Backgroundgenelist= [x for x in gene_list if x==x]

genelist_df1 = dataframes_list_ORA[0]
genelist_Ag1= genelist_df1[genelist_df1['padj'] < 0.05]
genelist_Ag1=genelist_Ag1['gene_name'].copy()
gene_listAg1 = genelist_Ag1.squeeze().str.strip().to_list()
gene_list_Ag1= [x for x in gene_listAg1 if x==x]

genelist_df2 = dataframes_list_ORA[1]
genelist_Ag2= genelist_df2[genelist_df2['padj'] < 0.05]
genelist_Ag2=genelist_Ag2['gene_name'].copy()
gene_listAg2 = genelist_Ag2.squeeze().str.strip().to_list()
gene_list_Ag2= [x for x in gene_listAg2 if x==x]

genelist_df3 = dataframes_list_ORA[2]
genelist_AgNP1= genelist_df3[genelist_df3['padj'] < 0.05]
genelist_AgNP1=genelist_AgNP1['gene_name'].copy()
gene_listAgNP1 = genelist_AgNP1.squeeze().str.strip().to_list()
gene_list_AgNP1= [x for x in gene_listAgNP1 if x==x]

genelist_df4 = dataframes_list_ORA[3]
genelist_AgNP2= genelist_df4[genelist_df4['padj'] < 0.05]
genelist_AgNP2=genelist_AgNP2['gene_name'].copy()
gene_listAgNP2 = genelist_AgNP2.squeeze().str.strip().to_list()
gene_list_AgNP2= [x for x in gene_listAgNP2 if x==x]

enr_bg_Ag1 = gp.enrichr(gene_list=gene_list_Ag1,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='ORA_tables_for_comparison', 
                 verbose=True)

2025-04-13 12:19:15,194 [INFO] Run: WikiPathways_2024_Human 
2025-04-13 12:19:16,786 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-13 12:19:18,724 [INFO] Done.

enr_bg_Ag1.results.head()

enr_bg_Ag2 = gp.enrichr(gene_list=gene_list_Ag2,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='ORA_tables_for_comparison', 
                 verbose=True)

2025-04-13 12:24:30,831 [INFO] Run: WikiPathways_2024_Human 
2025-04-13 12:24:32,402 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-13 12:24:32,816 [INFO] Done.

enr_bg_Ag2.results.head(5)

enr_bg_AgNP1 = gp.enrichr(gene_list=gene_list_AgNP1,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='ORA_tables_for_comparison', 
                 verbose=True)

2025-04-13 12:25:06,014 [INFO] Run: WikiPathways_2024_Human 
2025-04-13 12:25:07,904 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-13 12:25:08,414 [INFO] Done.

enr_bg_AgNP1.results.head(5)

enr_bg_AgNP2 = gp.enrichr(gene_list=gene_list_AgNP2,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='ORA_tables_for_comparison', 
                 verbose=True)

2025-04-13 12:25:48,930 [INFO] Run: WikiPathways_2024_Human 
2025-04-13 12:25:50,729 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-13 12:25:51,136 [INFO] Done.

enr_bg_AgNP2.results.head(5)

ax_Ag1 = barplot(enr_bg_Ag1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
                title= 'Ag+ timepoint 1 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='Ag+ timepoint 1 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_Ag2 = barplot(enr_bg_Ag2.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'Ag+ timepoint 2 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='Ag timepoint 2 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_AgNP1 = barplot(enr_bg_AgNP1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'AgNP timepoint 1 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='AgNP timepoint 1 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_AgNP2 = barplot(enr_bg_AgNP2.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'AgNP timepoint 2ORA',
              top_term=10,
              figsize=(3,5),
              ofname='AgNP timepoint 2 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

path_ORA = "C:\\Users\\shaki\\Downloads\\BackgroundORA-EMEXP2599"
dir_list_ORA = os.listdir(path_ORA)
print("Files and directories in '", path_ORA, "' :")
print(dir_list_ORA)

Files and directories in ' C:\Users\shaki\Downloads\BackgroundORA-EMEXP2599 ' :
['X12_CdCI2_.5- X12_vehicle...control_.0.csv', 'X12_CsA_.5 - X12_vehicle...control_.0.csv', 'X12_Diquat_30 - X12_vehicle...control_.0.csv', 'X48_CdCI2_.5 - X48_vehicle...control_.0.csv', 'X48_CsA_.5 - X48_vehicle...control_.0.csv', 'X48_Diquat_30 - X48_vehicle...control_.0.csv']

list_of_names_ORA = ['X12_CdCI2_.5- X12_vehicle...control_.0', 'X12_CsA_.5 - X12_vehicle...control_.0','X12_Diquat_30 - X12_vehicle...control_.0','X48_CdCI2_.5 - X48_vehicle...control_.0','X48_CsA_.5 - X48_vehicle...control_.0','X48_Diquat_30 - X48_vehicle...control_.0']
dataframes_list_ORA = []

for i in range(len(list_of_names_ORA)):
 
    temp_df_ORA = pd.read_csv("./BackgroundORA-EMEXP2599/" + list_of_names_ORA[i] + ".csv", comment=';')
    dataframes_list_ORA.append(temp_df_ORA)

combined_dataframes_list_ORA=pd.concat([dataframes_list_ORA[0],dataframes_list_ORA[1],dataframes_list_ORA[2],dataframes_list_ORA[3],dataframes_list_ORA[4],dataframes_list_ORA[5]], ignore_index=True, axis=0)
genelist= combined_dataframes_list_ORA['gene_name'].copy()
gene_list = genelist.squeeze().str.strip().to_list()
Backgroundgenelist= [x for x in gene_list if x==x]

genelist_df1 = dataframes_list_ORA[0]
genelist_CdCI1= genelist_df1[genelist_df1['padj'] < 0.05]
genelist_CDCI1=genelist_CdCI1['gene_name'].copy()
gene_listCDCL1 = genelist_CDCI1.squeeze().str.strip().to_list()
gene_list_CDCI1= [x for x in gene_listCDCL1 if x==x]

genelist_df2 = dataframes_list_ORA[1]
genelist_CSA2= genelist_df2[genelist_df2['padj'] < 0.05]
genelist_CsA2=genelist_CSA2['gene_name'].copy()
gene_listCSA = genelist_CsA2.squeeze().str.strip().to_list()
gene_list_CSA1= [x for x in gene_listCSA if x==x]

genelist_df3 = dataframes_list_ORA[2]
genelist_diquat= genelist_df3[genelist_df3['padj'] < 0.05]
genelist_Diquat=genelist_diquat['gene_name'].copy()
gene_listDiquat = genelist_Diquat.squeeze().str.strip().to_list()
gene_list_Diquat1= [x for x in gene_listDiquat if x==x]

genelist_df4 = dataframes_list_ORA[3]
genelist_CDCL22= genelist_df4[genelist_df4['padj'] < 0.05]
genelist_CDCI2=genelist_CDCL22['gene_name'].copy()
gene_listCDCI22 = genelist_CDCI2.squeeze().str.strip().to_list()
gene_list_CDCI22= [x for x in gene_listCDCI22 if x==x]

genelist_df5 = dataframes_list_ORA[4]
genelist_CsA_2= genelist_df5[genelist_df5['padj'] < 0.05]
genelist_CSA2=genelist_CsA_2['gene_name'].copy()
gene_listCsA22 = genelist_CSA2.squeeze().str.strip().to_list()
gene_list_CsA2= [x for x in gene_listCsA22 if x==x]

genelist_df6 = dataframes_list_ORA[5]
genelist_Diquat_2= genelist_df6[genelist_df6['padj'] < 0.05]
genelist_Diquat2=genelist_Diquat_2['gene_name'].copy()
gene_listDiquat = genelist_Diquat2.squeeze().str.strip().to_list()
gene_list_Diquat2= [x for x in gene_listDiquat if x==x]

enr_bg_CdCI1 = gp.enrichr(gene_list=gene_list_CDCI1,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='EMEXP-2599_ORApathwaytable', 
                 verbose=True)

2025-04-14 09:58:56,222 [INFO] Run: WikiPathways_2024_Human 
2025-04-14 09:58:59,621 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-14 09:59:01,252 [INFO] Done.

enr_bg_CdCI1.results.head(5)

enr_bg_CSA1 = gp.enrichr(gene_list=gene_list_CSA1,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='EMEXP-2599_ORApathwaytable', 
                 verbose=True)

2025-04-14 10:00:23,265 [INFO] Run: WikiPathways_2024_Human 
2025-04-14 10:00:24,820 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-14 10:00:25,292 [INFO] Done.

enr_bg_CSA1.results.head(5)

enr_bg_Diquat1 = gp.enrichr(gene_list=gene_list_Diquat1,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='EMEXP-2599_ORApathwaytable', 
                 verbose=True)

2025-04-14 10:01:07,378 [INFO] Run: WikiPathways_2024_Human 
2025-04-14 10:01:08,961 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-14 10:01:09,394 [INFO] Done.

enr_bg_Diquat1.results.head(5)

enr_bg_CDCI22 = gp.enrichr(gene_list=gene_list_CDCI22,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='EMEXP-2599_ORApathwaytable', 
                 verbose=True)

2025-04-14 10:01:51,520 [INFO] Run: WikiPathways_2024_Human 
2025-04-14 10:01:53,315 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-14 10:01:54,607 [INFO] Done.

enr_bg_CDCI22.results.head(5)

enr_bg_CsA2 = gp.enrichr(gene_list=gene_list_CsA2,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='EMEXP-2599_ORApathwaytable', 
                 verbose=True)

2025-04-14 10:02:25,303 [INFO] Run: WikiPathways_2024_Human 
2025-04-14 10:02:26,877 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-14 10:02:27,470 [INFO] Done.

enr_bg_CsA2.results.head(5)

enr_bg_Diquat2 = gp.enrichr(gene_list=gene_list_Diquat2,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='EMEXP-2599_ORApathwaytable', 
                 verbose=True)

2025-04-14 10:03:28,790 [INFO] Run: WikiPathways_2024_Human 
2025-04-14 10:03:30,575 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-14 10:03:31,122 [INFO] Done.

enr_bg_Diquat2.results.head()

ax_CDCI1 = barplot(enr_bg_CdCI1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
                title= 'CDCI2 timepoint 1 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='CDCI2 timepoint 1 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_CsA1 = barplot(enr_bg_CSA1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'CsA timepoint 1 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='CsA timepoint 1 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_Diquat1 = barplot(enr_bg_Diquat1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'Diquat timepoint 1 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='Diquat timepoint 1 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_CDCI22 = barplot(enr_bg_CDCI22.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'CDCI2 timepoint 2 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='CDCI2 timepoint 2 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_CsA2 = barplot(enr_bg_CsA2.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'CsA timepoint 2 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='CsA timepoint 2 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_Diquat2 = barplot(enr_bg_Diquat2.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'Diquat timepoint 2 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='Diquat timepoint 2 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

path_ORA = "C:\\Users\\shaki\\Downloads\\BackgroundORA-EGEOD44729"
dir_list_ORA = os.listdir(path_ORA)
print("Files and directories in '", path_ORA, "' :")
print(dir_list_ORA)

Files and directories in ' C:\Users\shaki\Downloads\BackgroundORA-EGEOD44729 ' :
['adaptedACR10h.tsv', 'adaptedACR24h.tsv', 'adaptedCP10h.tsv', 'adaptedCP24h.tsv', 'adaptedMA10h.tsv', 'adaptedMA24h.tsv']

list_of_names_ORA = ['adaptedACR10h', 'adaptedACR24h','adaptedCP10h','adaptedCP24h','adaptedMA10h','adaptedMA24h']
dataframes_list_ORA = []

for i in range(len(list_of_names_ORA)):
 
    temp_df_ORA = pd.read_csv("./BackgroundORA-EGEOD44729/" + list_of_names_ORA[i] + ".tsv", sep='\t')
    dataframes_list_ORA.append(temp_df_ORA)

combined_dataframes_list_ORA=pd.concat([dataframes_list_ORA[0],dataframes_list_ORA[1],dataframes_list_ORA[2],dataframes_list_ORA[3],dataframes_list_ORA[4],dataframes_list_ORA[5]], ignore_index=True, axis=0)
genelist= combined_dataframes_list_ORA['GENE_SYMBOL'].copy().drop_duplicates()
gene_list = genelist.squeeze().str.strip().to_list()
Backgroundgenelist= [x for x in gene_list if x==x]

#genelist_df1 = dataframes_list_ORA[0]
#genelist_ACR1= genelist_df1[genelist_df1['padj'] < 0.05]
#genelist_ACR_1=genelist_ACR1['GENE_SYMBOL'].copy()
#gene_listACR1 = genelist_ACR_1.squeeze().str.strip().to_list()
#gene_list_ACR10_t1= [x for x in gene_listACR1 if x==x]

genelist_df2 = dataframes_list_ORA[1]
genelist_ACR2= genelist_df2[genelist_df2['padj'] < 0.05]
genelist_acr2=genelist_ACR2['GENE_SYMBOL'].copy()
gene_listACR2 = genelist_acr2.squeeze().str.strip().to_list()
gene_list_ACR24_t2= [x for x in gene_listACR2 if x==x]

genelist_df3 = dataframes_list_ORA[2]
genelist_CP10= genelist_df3[genelist_df3['padj'] < 0.05]
genelist_cp10=genelist_CP10['GENE_SYMBOL'].copy()
gene_listCP10 = genelist_cp10.squeeze().str.strip().to_list()
gene_list_CP10_t1= [x for x in gene_listCP10 if x==x]

genelist_df4 = dataframes_list_ORA[3]
genelist_CP24= genelist_df4[genelist_df4['padj'] < 0.05]
genelist_cp24=genelist_CP24['GENE_SYMBOL'].copy()
gene_listCP24 = genelist_cp24.squeeze().str.strip().to_list()
gene_list_CP24_t2= [x for x in gene_listCP24 if x==x]

genelist_df5 = dataframes_list_ORA[4]
genelist_MA10= genelist_df5[genelist_df5['padj'] < 0.05]
genelist_ma10=genelist_MA10['GENE_SYMBOL'].copy()
gene_listMA10 = genelist_ma10.squeeze().str.strip().to_list()
gene_list_MA10_t1= [x for x in gene_listMA10 if x==x]

genelist_df6 = dataframes_list_ORA[5]
genelist_MA24= genelist_df6[genelist_df6['padj'] < 0.05]
genelist_ma24=genelist_MA24['GENE_SYMBOL'].copy()
gene_listMA24 = genelist_ma24.squeeze().str.strip().to_list()
gene_list_MA24_t2= [x for x in gene_listMA24 if x==x]

enr_bg_ACR24_t2 = gp.enrichr(gene_list=gene_list_ACR24_t2,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir=None, 
                 verbose=True)

2025-03-18 14:35:19,806 [INFO] Run: WikiPathways_2024_Human 
2025-03-18 14:35:20,846 [INFO] Done.

enr_bg_ACR24_t2.results.head(5)

enr_bg_CP10_t1 = gp.enrichr(gene_list=gene_list_CP10_t1,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir=None, 
                 verbose=True)

2025-03-18 14:53:58,480 [INFO] Run: WikiPathways_2024_Human 
2025-03-18 14:53:59,585 [INFO] Done.

enr_bg_CP10_t1.results.head(5)

enr_bg_CP24_t2 = gp.enrichr(gene_list=gene_list_CP24_t2,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir=None, 
                 verbose=True)

2025-03-18 14:29:05,143 [INFO] Run: WikiPathways_2024_Human 
2025-03-18 14:29:06,377 [INFO] Done.

enr_bg_CP24_t2.results.head(5)

enr_bg_MA10_t1 = gp.enrichr(gene_list=gene_list_MA10_t1,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir=None, 
                 verbose=True)

2025-03-18 14:29:07,128 [INFO] Run: WikiPathways_2024_Human 
2025-03-18 14:29:08,141 [INFO] Done.

enr_bg_MA10_t1.results.head(5)

enr_bg_MA24_t2 = gp.enrichr(gene_list=gene_list_MA24_t2,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir=None, 
                 verbose=True)

2025-03-18 14:29:09,485 [INFO] Run: WikiPathways_2024_Human 
2025-03-18 14:29:10,994 [INFO] Done.

enr_bg_MA24_t2.results.head(5)

ax_ACR24h_t2 = barplot(enr_bg_ACR24_t2.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'ACR timepoint 2 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='ACR timepoint 2 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_CP10_t1 = barplot(enr_bg_CP10_t1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'CP timepoint 1 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='CP timepoint 1 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_CP24_t2 = barplot(enr_bg_CP24_t2.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'CP timepoint 2 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='CP timepoint 2 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_MA_t1 = barplot(enr_bg_MA10_t1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'MA timepoint 1 ORA',
              top_term=10,
              figsize=(3,5),
              ofname='MA timepoint 1 ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

path_ORA = "C:\\Users\\shaki\\Downloads\\BackgroundORA_EGEOD69851"
dir_list_ORA = os.listdir(path_ORA)
print("Files and directories in '", path_ORA, "' :")
print(dir_list_ORA)

Files and directories in ' C:\Users\shaki\Downloads\BackgroundORA_EGEOD69851 ' :
['GSE69844.BisphenolA-100uM.tsv', 'GSE69844.BisphenolA-10uM.tsv', 'GSE69844.BisphenolA-1uM.tsv', 'GSE69844.Farnesol-100uM.tsv', 'GSE69844.Farnesol-10uM.tsv', 'GSE69844.Farnesol-1uM.tsv', 'GSE69844.Tetrachlorodibenzopdioxin-100nM.tsv', 'GSE69844.Tetrachlorodibenzopdioxin-10nM.tsv', 'GSE69844.Tetrachlorodibenzopdioxin-1nM.tsv', 'GSE69844.Troglitazone-100uM.tsv', 'GSE69844.Troglitazone-10uM.tsv', 'GSE69844.Troglitazone-1uM.tsv', 'GSE69844.ValproicAcid-100uM.tsv', 'GSE69844.ValproicAcid-10uM.tsv', 'GSE69844.ValproicAcid-1mM.tsv']

list_of_names_ORA = ['GSE69844.BisphenolA-100uM', 'GSE69844.BisphenolA-10uM', 'GSE69844.BisphenolA-1uM', 'GSE69844.Farnesol-100uM', 'GSE69844.Farnesol-10uM', 'GSE69844.Farnesol-1uM', 'GSE69844.Tetrachlorodibenzopdioxin-100nM', 'GSE69844.Tetrachlorodibenzopdioxin-10nM', 'GSE69844.Tetrachlorodibenzopdioxin-1nM', 'GSE69844.Troglitazone-100uM', 'GSE69844.Troglitazone-10uM', 'GSE69844.Troglitazone-1uM', 'GSE69844.ValproicAcid-100uM', 'GSE69844.ValproicAcid-10uM', 'GSE69844.ValproicAcid-1mM']
dataframes_list_ORA = []

for i in range(len(list_of_names_ORA)):
 
    temp_df_ORA = pd.read_csv("./BackgroundORA_EGEOD69851/" + list_of_names_ORA[i] + ".tsv", sep='\t')
    dataframes_list_ORA.append(temp_df_ORA)

combined_dataframes_list_ORA=pd.concat([dataframes_list_ORA[0],dataframes_list_ORA[1],dataframes_list_ORA[2],dataframes_list_ORA[3],dataframes_list_ORA[4],dataframes_list_ORA[5],dataframes_list_ORA[6],dataframes_list_ORA[7],dataframes_list_ORA[8],dataframes_list_ORA[9],dataframes_list_ORA[10],dataframes_list_ORA[11],dataframes_list_ORA[12],dataframes_list_ORA[13],dataframes_list_ORA[14]], ignore_index=True, axis=0)
genelist= combined_dataframes_list_ORA['Gene.Symbol'].copy().drop_duplicates()
gene_list = genelist.squeeze().str.strip().to_list()
Backgroundgenelist= [x for x in gene_list if x==x]

genelist_df1 = dataframes_list_ORA[0]
genelist_BisphenolA100uM= genelist_df1[genelist_df1['padj'] < 0.05]
genelist_BisphenolA_100uM=genelist_BisphenolA100uM['Gene.Symbol'].copy()
gene_listBisphenolA_ = genelist_BisphenolA_100uM.squeeze().str.strip().to_list()
gene_list_BisphenolA_100uM= [x for x in gene_listBisphenolA_ if x==x]

genelist_df2 = dataframes_list_ORA[1]
genelist_BisphenolA10uM= genelist_df2[genelist_df2['padj'] < 0.05]
genelist_BisphenolA_10uM=genelist_BisphenolA10uM['Gene.Symbol'].copy()
gene_listBisphenolA_10 = genelist_BisphenolA_10uM.squeeze().str.strip().to_list()
gene_list_BisphenolA_10uM= [x for x in gene_listBisphenolA_10 if x==x]

genelist_df3 = dataframes_list_ORA[2]
genelist_BisphenolA1uM= genelist_df3[genelist_df3['padj'] < 0.05]
genelist_BisphenolA_1uM=genelist_BisphenolA1uM['Gene.Symbol'].copy()
gene_listBisphenolA_1 = genelist_BisphenolA_1uM.squeeze().str.strip().to_list()
gene_list_BisphenolA_1uM= [x for x in gene_listBisphenolA_1 if x==x]

genelist_df4 = dataframes_list_ORA[3]
genelist_Farnesol100_uM= genelist_df4[genelist_df4['padj'] < 0.05]
genelist_Farnesol100=genelist_Farnesol100_uM['Gene.Symbol'].copy()
gene_listFarnesol100uM = genelist_Farnesol100.squeeze().str.strip().to_list()
gene_list_Farnesol_100uM= [x for x in gene_listFarnesol100uM if x==x]

genelist_df5 = dataframes_list_ORA[4]
genelist_Farnesol10_uM= genelist_df5[genelist_df5['padj'] < 0.05]
genelist_Farnesol10=genelist_Farnesol10_uM['Gene.Symbol'].copy()
gene_listFarnesol10uM = genelist_Farnesol10.squeeze().str.strip().to_list()
gene_list_Farnesol_10uM= [x for x in gene_listFarnesol10uM if x==x]

genelist_df6 = dataframes_list_ORA[5]
genelist_Farnesol1_uM= genelist_df6[genelist_df6['padj'] < 0.05]
genelist_Farnesol1=genelist_Farnesol1_uM['Gene.Symbol'].copy()
gene_listFarnesol1uM = genelist_Farnesol1.squeeze().str.strip().to_list()
gene_list_Farnesol_1uM= [x for x in gene_listFarnesol1uM if x==x]

genelist_df7 = dataframes_list_ORA[6]
genelist_Tpdioxin100_uM= genelist_df7[genelist_df7['padj'] < 0.05]
genelist_Tpdioxin100=genelist_Tpdioxin100_uM['Gene.Symbol'].copy()
gene_listTpdioxin100uM = genelist_Tpdioxin100.squeeze().str.strip().to_list()
gene_list_Tpdioxin_100uM= [x for x in gene_listTpdioxin100uM if x==x]

genelist_df8 = dataframes_list_ORA[7]
genelist_Tpdioxin10_uM= genelist_df8[genelist_df8['padj'] < 0.05]
genelist_Tpdioxin10=genelist_Tpdioxin10_uM['Gene.Symbol'].copy()
gene_listTpdioxin10uM = genelist_Tpdioxin10.squeeze().str.strip().to_list()
gene_list_Tpdioxin_10uM= [x for x in gene_listTpdioxin10uM if x==x]

genelist_df9 = dataframes_list_ORA[8]
genelist_Tpdioxin1_uM= genelist_df9[genelist_df9['padj'] < 0.05]
genelist_Tpdioxin1=genelist_Tpdioxin1_uM['Gene.Symbol'].copy()
gene_listTpdioxin1uM = genelist_Tpdioxin1.squeeze().str.strip().to_list()
gene_list_Tpdioxin_1uM= [x for x in gene_listTpdioxin1uM if x==x]

genelist_df10 = dataframes_list_ORA[9]
genelist_Troglitazone100_uM= genelist_df10[genelist_df10['padj'] < 0.05]
genelist_Troglitazone100=genelist_Troglitazone100_uM['Gene.Symbol'].copy()
gene_listTroglitazone100 = genelist_Troglitazone100.squeeze().str.strip().to_list()
gene_list_Troglitazone_100uM= [x for x in gene_listTroglitazone100 if x==x]

genelist_df11 = dataframes_list_ORA[10]
genelist_Troglitazone10_uM= genelist_df11[genelist_df11['padj'] < 0.05]
genelist_Troglitazone10=genelist_Troglitazone10_uM['Gene.Symbol'].copy()
gene_listTroglitazone10 = genelist_Troglitazone10.squeeze().str.strip().to_list()
gene_list_Troglitazone_10uM= [x for x in gene_listTroglitazone10 if x==x]

genelist_df12 = dataframes_list_ORA[11]
genelist_Troglitazone1_uM= genelist_df12[genelist_df12['padj'] < 0.05]
genelist_Troglitazone1=genelist_Troglitazone1_uM['Gene.Symbol'].copy()
gene_listTroglitazone1 = genelist_Troglitazone1.squeeze().str.strip().to_list()
gene_list_Troglitazone_1uM= [x for x in gene_listTroglitazone1 if x==x]

genelist_df13 = dataframes_list_ORA[12]
genelist_Valproicacid100uM= genelist_df13[genelist_df13['padj'] < 0.05]
genelist_Valproicacid100=genelist_Valproicacid100uM['Gene.Symbol'].copy()
gene_listValproicacid100uM = genelist_Valproicacid100.squeeze().str.strip().to_list()
gene_list_Valproicacid_100uM= [x for x in gene_listValproicacid100uM if x==x]

genelist_df14 = dataframes_list_ORA[13]
genelist_Valproicacid10uM= genelist_df14[genelist_df14['padj'] < 0.05]
genelist_Valproicacid10=genelist_Valproicacid10uM['Gene.Symbol'].copy()
gene_listValproicacid10uM = genelist_Valproicacid10.squeeze().str.strip().to_list()
gene_list_Valproicacid_10uM= [x for x in gene_listValproicacid10uM if x==x]

genelist_df15 = dataframes_list_ORA[14]
genelist_Valproicacid1uM= genelist_df15[genelist_df15['padj'] < 0.05]
genelist_Valproicacid1=genelist_Valproicacid1uM['Gene.Symbol'].copy()
gene_listValproicacid1uM = genelist_Valproicacid1.squeeze().str.strip().to_list()
gene_list_Valproicacid_1uM= [x for x in gene_listValproicacid1uM if x==x]

enr_bg_BisphenolA100 = gp.enrichr(gene_list=gene_list_BisphenolA_100uM,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GEOD69851_ORApathwaytable', 
                 verbose=True)

2025-04-16 10:08:35,632 [INFO] Run: WikiPathways_2024_Human 
2025-04-16 10:08:37,759 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-16 10:08:38,878 [INFO] Done.

enr_bg_BisphenolA100.results.head(5)

enr_bg_BisphenolA10 = gp.enrichr(gene_list=gene_list_BisphenolA_10uM,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GEOD69851_ORApathwaytable', 
                 verbose=True)

2025-04-16 10:09:24,475 [INFO] Run: WikiPathways_2024_Human 
2025-04-16 10:09:26,039 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-16 10:09:26,347 [INFO] Done.

enr_bg_BisphenolA10.results.head(5)

enr_bg_BisphenolA1 = gp.enrichr(gene_list=gene_list_BisphenolA_1uM,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GEOD69851_ORApathwaytable', 
                 verbose=True)

2025-04-16 10:09:56,544 [INFO] Run: WikiPathways_2024_Human 
2025-04-16 10:09:58,628 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-16 10:09:59,107 [INFO] Done.

enr_bg_BisphenolA1.results.head(5)

enr_bg_Farnesol100 = gp.enrichr(gene_list=gene_list_Farnesol_100uM,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GEOD69851_ORApathwaytable', 
                 verbose=True)

2025-04-16 10:10:49,806 [INFO] Run: WikiPathways_2024_Human 
2025-04-16 10:10:51,345 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-16 10:10:51,762 [INFO] Done.

enr_bg_Farnesol100.results.head(5)

enr_bg_Tpdioxin100 = gp.enrichr(gene_list=gene_list_Tpdioxin_100uM,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GEOD69851_ORApathwaytable', 
                 verbose=True)

2025-04-16 10:11:14,809 [INFO] Run: WikiPathways_2024_Human 
2025-04-16 10:11:16,703 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-16 10:11:17,357 [INFO] Done.

enr_bg_Tpdioxin100.results.head(5)

enr_bg_Tpdioxin10 = gp.enrichr(gene_list=gene_list_Tpdioxin_10uM,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GEOD69851_ORApathwaytable', 
                 verbose=True)

2025-04-16 10:11:41,764 [INFO] Run: WikiPathways_2024_Human 
2025-04-16 10:11:43,291 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-16 10:11:43,886 [INFO] Done.

enr_bg_Tpdioxin10.results.head(5)

enr_bg_Tpdioxin1= gp.enrichr(gene_list=gene_list_Tpdioxin_1uM,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GEOD69851_ORApathwaytable', 
                 verbose=True)

2025-04-16 10:12:13,318 [INFO] Run: WikiPathways_2024_Human 
2025-04-16 10:12:14,897 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-16 10:12:15,287 [INFO] Done.

enr_bg_Tpdioxin1.results.head(5)

enr_bg_Troglitazone100 = gp.enrichr(gene_list=gene_list_Troglitazone_100uM,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GEOD69851_ORApathwaytable', 
                 verbose=True)

2025-04-16 10:12:43,723 [INFO] Run: WikiPathways_2024_Human 
2025-04-16 10:12:45,964 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-16 10:12:46,585 [INFO] Done.

enr_bg_Troglitazone100.results.head(5)

enr_bg_Troglitazone10 = gp.enrichr(gene_list=gene_list_Troglitazone_10uM,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GEOD69851_ORApathwaytable', 
                 verbose=True)

2025-04-16 10:13:03,670 [INFO] Run: WikiPathways_2024_Human 
2025-04-16 10:13:05,315 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-16 10:13:05,773 [INFO] Done.

enr_bg_Troglitazone10.results.head(5)

enr_bg_Troglitazone1= gp.enrichr(gene_list=gene_list_Troglitazone_1uM,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GEOD69851_ORApathwaytable', 
                 verbose=True)

2025-04-16 10:13:29,699 [INFO] Run: WikiPathways_2024_Human 
2025-04-16 10:13:31,704 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-16 10:13:32,485 [INFO] Done.

enr_bg_Troglitazone1.results.head(5)

enr_bg_ValproicAcid1= gp.enrichr(gene_list=gene_list_Valproicacid_1uM,
                 gene_sets=['WikiPathways_2024_Human'],
                 organism='human',
                 background= Backgroundgenelist,
                 outdir='GEOD69851_ORApathwaytable', 
                 verbose=True)

2025-04-16 10:14:04,617 [INFO] Run: WikiPathways_2024_Human 
2025-04-16 10:14:06,619 [INFO] Save enrichment results for WikiPathways_2024_Human 
2025-04-16 10:14:07,063 [INFO] Done.

enr_bg_ValproicAcid1.results.head(5)

ax_BisphenolA_c100 = barplot(enr_bg_BisphenolA100.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'Bisphenol A 100uM ORA',
              top_term=10,
              figsize=(3,5),
              ofname='Bisphenol A 100uM ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
              )

ax_BisphenolA_c10 = barplot(enr_bg_BisphenolA10.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'Bisphenol A 10uM ORA',
              top_term=10,
              figsize=(3,5),
              ofname='Bisphenol A 10uM ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_BisphenolA_c1= barplot(enr_bg_BisphenolA1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'Bisphenol A 1uM ORA',
              top_term=10,
              figsize=(3,5),
              ofname='Bisphenol A 1uM ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_Farnesol_c100 = barplot(enr_bg_Farnesol100.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'Farnesol 100uM ORA',
              top_term=10,
              figsize=(3,5),
              ofname='Farnesol 100uM ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_Tpdioxin_c100 = barplot(enr_bg_Tpdioxin100.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'tetrachlorodibenzopdioxin 100uM ORA',
              top_term=10,
              figsize=(3,5),
              ofname='tetrachlorodibenzopdioxin 100uM ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_Tpdioxin_c10 = barplot(enr_bg_Tpdioxin10.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'tetrachlorodibenzopdioxin 10uM ORA',
              top_term=10,
              figsize=(3,5),
              ofname='tetrachlorodibenzopdioxin 10uM ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_Tpdioxin_c1 = barplot(enr_bg_Tpdioxin1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'tetrachlorodibenzopdioxin 1uM ORA',
              top_term=10,
              figsize=(3,5),
              ofname='tetrachlorodibenzopdioxin 1uM ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_Trogltiazone_c100 = barplot(enr_bg_Troglitazone100.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'Troglitazone 100uM ORA',
              top_term=10,
              figsize=(3,5),
              ofname='Troglitazone 100uM ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_Trogltiazone_c10 = barplot(enr_bg_Troglitazone10.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'Troglitazone 10uM ORA',
              top_term=10,
              figsize=(3,5),
              ofname='Troglitazone 10uM ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_Trogltiazone_c1 = barplot(enr_bg_Troglitazone1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'Troglitazone 1uM ORA',
              top_term=10,
              figsize=(3,5),
              ofname='roglitazone 1uM ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

ax_Valproicacid_c1 = barplot(enr_bg_ValproicAcid1.results,
              column="Adjusted P-value",
              group='Gene_set', 
              size=10,
              title= 'Valproic acid 1uM ORA',
              top_term=10,
              figsize=(3,5),
              ofname='Valproic acid 1uM ORA',
              color = {'WikiPathways_2024_Human': 'darkred'}
             )

%load_ext watermark
!pip install print-versions

Requirement already satisfied: print-versions in c:\users\shaki\anaconda3\lib\site-packages (0.1.0)

%watermark

    Last updated: 2025-06-02T13:18:59.342286+02:00
    
    Python implementation: CPython
    Python version       : 3.12.3
    IPython version      : 8.25.0
    
    Compiler    : MSC v.1938 64 bit (AMD64)
    OS          : Windows
    Release     : 11
    Machine     : AMD64
    Processor   : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
    CPU cores   : 8
    Architecture: 64bit

from print_versions import print_versions
print_versions(globals())

    pandas==2.2.3
    json==2.0.9
    ipykernel==6.28.0
    gseapy==1.1.4
    numpy==1.26.4
    matplotlib==3.8.4