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Partial fix issue 5 #9

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Partial fix issue 5 #9

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a38e4aa
Update utils.py
jhchung Feb 19, 2025
8a0857a
Update utils.py
jhchung Feb 19, 2025
cb5b5f9
Update dictionary extraction and dataframe concatenation
jhchung Feb 19, 2025
bd655bd
Update utils.py
jhchung Feb 27, 2025
f97d5a9
Update utils.py
jhchung Feb 27, 2025
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17 changes: 9 additions & 8 deletions kgwas/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
from multiprocessing import Pool
from tqdm import tqdm
from functools import partial
import pandas as pd

from .params import main_data_path, cohort_data_path, kinship_path, withdraw_path

Expand Down Expand Up @@ -458,7 +459,7 @@ def get_network_weight(run, data):
)
attention_layer = {i: j[1] for i,j in x_dict.items()}
attention_all_layers.append(attention_layer)
x_dict = {i: j[0] for i,j in x_dict.items()}
x_dict = {i: j[0][0] for i,j in x_dict.items()}

layer2rel2att = {
'l1': {},
Expand All @@ -480,15 +481,15 @@ def get_network_weight(run, data):
df_val['rel_type'] = rel[1]
df_val['t_type'] = rel[2]
df_val['layer'] = 'l1'
df_val_all = df_val_all.append(df_val)
df_val_all = pd.concat([df_val_all, df_val], ignore_index=True)

for rel, value in layer2rel2att['l2'].items():
df_val = pd.DataFrame(value).T.rename(columns = {0: 'h_idx', 1: 't_idx', 2: 'weight'})
df_val['h_type'] = rel[0]
df_val['rel_type'] = rel[1]
df_val['t_type'] = rel[2]
df_val['layer'] = 'l2'
df_val_all = df_val_all.append(df_val)
df_val_all = pd.concat([df_val_all, df_val], ignore_index=True)

df_val_all = df_val_all.drop_duplicates(['h_idx', 't_idx', 'rel_type', 'layer'])
return df_val_all
Expand Down Expand Up @@ -543,7 +544,7 @@ def generate_viz(run, df_network, data_path, variant_threshold = 5e-8,

if 'SNP' not in gwas.columns.values:
gwas.loc[:, 'SNP'] = gwas['ID']
hit_snps = gwas[gwas.P < 5e-8].SNP.values
hit_snps = gwas[gwas.P < variant_threshold].SNP.values
hit_snps_idx = [id2idx['SNP'][i] for i in hit_snps]

if magma_path is not None:
Expand Down Expand Up @@ -593,7 +594,7 @@ def generate_viz(run, df_network, data_path, variant_threshold = 5e-8,
v2g_hit_with_rel_type = pd.merge(v2g_hit, snp2genes_hit, left_on=['h_idx', 't_idx', 'importance'], right_on=['h_idx', 't_idx', 'z_rel'], how='left')
v2g_hit = v2g_hit_with_rel_type[['h_idx', 't_idx', 'importance', 'h_type', 't_type', 'rel_type']]
v2g_hit.loc[:,'rel_type'] = v2g_hit.rel_type.apply(lambda x: x[4:])
v2g_hit.loc[:,'Category'] = 'V2G'
v2g_hit = v2g_hit.assign(Category='V2G')

v2g_hit.loc[:,'h_id'] = v2g_hit['h_idx'].apply(lambda x: idx2id['Gene'][x])
v2g_hit.loc[:,'t_id'] = v2g_hit['t_idx'].apply(lambda x: idx2id['SNP'][x])
Expand All @@ -610,7 +611,7 @@ def generate_viz(run, df_network, data_path, variant_threshold = 5e-8,
g2g_hit_with_rel_type = pd.merge(g2g_hit, gene2gene_hit, left_on=['h_idx', 't_idx', 'importance'], right_on=['h_idx', 't_idx', 'z_rel'], how='left')
g2g_hit = g2g_hit_with_rel_type[['h_idx', 't_idx', 'importance', 'h_type', 't_type', 'rel_type']]
g2g_hit.loc[:,'rel_type'] = g2g_hit.rel_type.apply(lambda x: x.split('-')[1])
g2g_hit.loc[:,'Category'] = 'G2G'
g2g_hit = g2g_hit.assign(Category='G2G')

g2g_hit.loc[:,'h_id'] = g2g_hit['h_idx'].apply(lambda x: idx2id['Gene'][x])
g2g_hit.loc[:,'t_id'] = g2g_hit['t_idx'].apply(lambda x: idx2id['Gene'][x])
Expand All @@ -628,7 +629,7 @@ def generate_viz(run, df_network, data_path, variant_threshold = 5e-8,
g2p_hit_with_rel_type = pd.merge(g2p_hit, gene2program_hit, left_on=['h_idx', 't_idx', 'importance'], right_on=['h_idx', 't_idx', 'z_rel'], how='left')
g2p_hit = g2p_hit_with_rel_type[['h_idx', 't_idx', 'importance', 'h_type', 't_type', 'rel_type']]
g2p_hit.loc[:,'rel_type'] = g2p_hit.rel_type.apply(lambda x: x.split('-')[1])
g2p_hit.loc[:,'Category'] = 'G2P'
g2p_hit = g2p_hit.assign(Category='G2P')
g2p_hit.loc[:,'h_id'] = g2p_hit['h_idx'].apply(lambda x: idx2id['BiologicalProcess'][x])
g2p_hit.loc[:,'t_id'] = g2p_hit['t_idx'].apply(lambda x: idx2id['Gene'][x])
g2p_hit.loc[:,'h_id'] = g2p_hit.h_id.apply(lambda x: go2name[x].capitalize() if x in go2name else x)
Expand Down Expand Up @@ -722,4 +723,4 @@ def generate_viz(run, df_network, data_path, variant_threshold = 5e-8,
except:
df_variant_interpretation = pd.DataFrame()

return df_variant_interpretation, disease_critical_network
return df_variant_interpretation, disease_critical_network