<jupyter_start><jupyter_text>Effort with All Gene Lists Trying with more panda dataframes to expand sample size. This proved inconclusive.<jupyter_code>wut = pd.read_csv('result-Supplemental-Gene-NIHMS1622076.csv')
pathname = 'NIHMS1622076-supplement-1622076_Supp_Tab1.xlsx'
guys_more = reactome_binomial_results(male_all, 7, total_all, 0.05,'greater')
gals_more = reactome_binomial_results(female_all, 11, total_all, 0.05, 'greater')
print(guys_more) 
print(gals_more) 
guys_less = reactome_binomial_results(male_all, 7, total_all, 0.05,'less')
gals_less = reactome_binomial_results(female_all, 11, total_all, 0.05, 'less')
print(guys_less) 
print(gals_less) 
guys_two = reactome_binomial_results(male_all, 7, total_all, 0.05, 'two-sided')
gals_two = reactome_binomial_results(female_all, 11, total_all,  0.05, 'two-sided')
print(guys_two)
print(gals_two) 
wut.iloc[:50, 1:4]
total_pathways = ["interleukin-10 signaling", "Signaling by Interleukins", "Cytokine Signaling in Immune system", "Immune System", "Chemokine receptors bind chemokines", "Interleukin-18 signaling", "Peptide ligand-binding receptors",  "GPCR downstream signalling", "Interleukin-4 and Interleukin-13 signaling", "Other interleuking signaling", "interleukin-1 family signaling", "Cellular Senescence", "Class A/1", 'G alpha (I) signalling events']
total_counts = [36, 64, 65, 70, 21, 5, 21, 21, 32, 2, 10, 6, 21, 16 ]
total = 58
total_all = np.array([z for z in zip(total_pathways, total_counts, [total for n in range(len(total_pathways))])])

male_pathways = ["interleukin-10 signaling", "Cytokine Signaling in Immune system", "Signaling by Interleukins", "Chemokine receptors bind chemokines", "Immune System", "GPCR ligand binding", "Peptide ligand-binding receptors", "GPCR downstream signalling", "Signalling by GPCR",  "G alpha (I) signalling events", "Class A/1"]
male_pathways_counts = [6, 7, 6, 3, 7, 4, 3 , 4, 3, 3, 3]
male_pathways_possible = [45, 804, 457, 57, 2249, 469, 198, 631, 706, 317, 333]
female_pathways = ["interleukin-10 signaling", "Signaling by Interleukins", "Cytokine Signaling in Immune system", "Immune System", "Chemokine receptors bind chemokines", "Interleukin-18 signaling", "Interleukin-4 and Interleukin-13 signaling", "Other interleuking signaling", "interleukin-1 family signaling", "Cellular Senescence" ]
female_pathways_counts = [8, 10, 11, 11, 3, 2, 3, 2, 3, 3] 
female_pathways_possible = [45, 457, 804, 2249, 57, 9, 111, 24, 142, 166]
female_all = np.array([y for y in zip(female_pathways, female_pathways_counts, female_pathways_possible)])
male_all = np.array([x for x in zip(male_pathways, male_pathways_counts, male_pathways_possible)])


guys_more = reactome_binomial_results(male_all, 7, total_all, 0.05,'greater')
gals_more = reactome_binomial_results(female_all, 11, total_all, 0.05, 'greater')
print(guys_more) 
print(gals_more) 
guys_less = reactome_binomial_results(male_all, 7, total_all, 0.05,'less')
gals_less = reactome_binomial_results(female_all, 11, total_all, 0.05, 'less')
print(guys_less) 
print(gals_less) 
guys_two = reactome_binomial_results(male_all, 7, total_all, 0.05, 'two-sided')
gals_two = reactome_binomial_results(female_all, 11, total_all,  0.05, 'two-sided')
print(guys_two)
print(gals_two) 

dataframes = pd.read_excel(pathname)
dataframes.iloc[22, 80:]
gene_list = []
for term in dataframes.iloc[22, 80:]:
    gene_list.append(term.strip(' ').split('or')[0].split('AND')[0].split('/')[0].split('And')[0].split('Or')[0])
for termy in gene_list:
    print(termy + '')
total = len(gene_list) - 3
print(total_all)<jupyter_output>IFNy
IL1a
IL1b
IL1RA
IL2
IL3
IL4
IL5
IL6
IL7
IL8
IL9
IL10
IL12p40
IL12p70
IL13
IL15
IL17A
IL17E
IL17F
IL18
IL22
IL27
CXCL10
CCL2
CCL7
MCSF
CCL22
CXCL9
CCL3
CCL4
PDGFAA
PDGFAB
CCL5
TGFa
TNFa
TNFb
VEGFA
Eotaxin2
CCL8
CXCL13
CCL13
CCL1
IL16
CCL17
CCL21
Eotaxin3
LIF
TPO
SCF
TSLP
IL33
IL20
IL21
IL23
TRAIL
CCL27
SDF1a
CXCL5
CCL15
IFNL2
[['interleukin-10 signaling' '36' '58']
 ['Signaling by Interleukins' '64' '58']
 ['Cytokine Signaling in Immune system' '65' '58']
 ['Immune System' '70' '58']
 ['Chemokine receptors bind chemokines' '21' '58']
 ['Interleukin-18 signaling' '5' '58']
 ['Peptide ligand-binding receptors' '21' '58']
 ['GPCR downstream signalling' '21' '58']
 ['Interleukin-4 and Interleukin-13 signaling' '32' '58']
 ['Other interleuking signaling' '2' '58']
 ['interleukin-1 family signaling' '10' '58']
 ['Cellular Senescence' '6' '58']
 ['Class A/1' '21' '58']
 ['G alpha (I) signalling events' '16' '58']]<jupyter_text>Gene List FinalGene names from paperIL1aIL1bIL1RAIL2IL3IL4IL5IL6IL7IL8IL9IL10IL12bIL13IL15IL17AIL17EIL17FIL18IL22IL27CXCL10CCL2CCL7CSF1CCL22CXCL9CCL3CCL4PDGFAAPDGFRCCL5TGFaTNFaTNFbVEGFACCL26CXCL13CCL13CCL1IL16CCL17CCL21CCL16LIFTPOSCFTSLPIL33IL20IL21IL23aTRAILCCL27SDF1aCXCL5IFNL2<jupyter_code>print(len(gene_list))<jupyter_output>61
{'fraction_non_alphanumeric': 0.1278614147246855, 'fraction_numerical': 0.08929676221901423, 'mean_word_length': 7.069883527454243, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '289209', 'license': 'CC-BY-4.0', 'n_tokens_mistral': 2346, 'n_tokens_neox': 1937, 'n_words': 461, 'path': 'ICBO-2021 data/ICBO-2021-Binomial-Compariosn-Copy1.ipynb', 'repo_name': 'CIDO-ontology/cido', 'size': '4849'}