Hugging Face's logo

Datasets:
Liu-Hy
/
GenoTEX

Tasks:
Text Generation
Table Question Answering
Tabular Regression
Languages:
English
Size:
100B<n<1T
ArXiv:
Tags:
ai4science
agent
llm
genomics
biology
License:
Dataset card Files Community
GenoTEX
File size: 4,648 Bytes 
06befd3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
 
# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lower_Grade_Glioma"

# Input paths
tcga_root_dir = "../DATA/TCGA"

# Output paths
out_data_file = "./output/preprocess/3/Lower_Grade_Glioma/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Lower_Grade_Glioma/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Lower_Grade_Glioma/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Lower_Grade_Glioma/cohort_info.json"

# Select the relevant directory
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Lower_Grade_Glioma_(LGG)')

# Get file paths using helper function
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# Load data
clinical_df = pd.read_csv(clinical_file, sep='\t', index_col=0)
genetic_df = pd.read_csv(genetic_file, sep='\t', index_col=0)

# Print column names of clinical data
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Step 1: Identify candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth', 'first_diagnosis_age_asth_ecz_hay_fev_mold_dust', 'first_diagnosis_age_of_animal_insect_allergy', 'first_diagnosis_age_of_food_allergy']
candidate_gender_cols = ['gender']

# Step 2: Load and preview columns from clinical data 
cohort_dir = os.path.join(tcga_root_dir, trait)
clinical_file, _ = tcga_get_relevant_filepaths(cohort_dir)
clinical_df = pd.read_csv(clinical_file, index_col=0)

# Preview age columns
age_preview = {}
for col in candidate_age_cols:
    if col in clinical_df.columns:
        age_preview[col] = clinical_df[col].head(5).tolist()
print("Age columns preview:", age_preview)

# Preview gender columns 
gender_preview = {}
for col in candidate_gender_cols:
    if col in clinical_df.columns:
        gender_preview[col] = clinical_df[col].head(5).tolist()
print("\nGender columns preview:", gender_preview)
# Define candidate columns
candidate_age_cols = ["age", "composite_element_ref", "birth_days_to", "days_to_birth", "year_of_birth", "age_at_initial_pathologic_diagnosis"]
candidate_gender_cols = ["gender"]

# Extract and preview demographic columns from clinical data
clinical_data_preview = {}

if len(candidate_age_cols) > 0:
    for col in candidate_age_cols:
        if col in clinical_df.columns:
            age_preview = preview_df(clinical_df[[col]], n=5)
            clinical_data_preview.update(age_preview)
    
if len(candidate_gender_cols) > 0:
    for col in candidate_gender_cols:
        if col in clinical_df.columns:
            gender_preview = preview_df(clinical_df[[col]], n=5) 
            clinical_data_preview.update(gender_preview)

print("Clinical Data Preview:")
print(clinical_data_preview)
# Examine candidate columns and select appropriate ones for age and gender
age_col = 'age_at_initial_pathologic_diagnosis'  # This column has clear age values
gender_col = 'gender'  # This column has clear gender values

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Redefine demographic columns
age_col = 'age_at_initial_pathologic_diagnosis'
gender_col = 'gender'

# Extract clinical features (trait and demographics)
clinical_data = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)

# Save processed clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_data.to_csv(out_clinical_data_file)

# Normalize gene symbols
normalized_gene_data = normalize_gene_symbols_in_index(genetic_df)

# Save processed gene data 
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Link clinical and genetic data
linked_data = pd.concat([clinical_data, normalized_gene_data.T], axis=1, join='inner')

# Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# Check for biased features and remove biased demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate and save cohort info
note = "Data obtained from TCGA Lower Grade Glioma cohort (LGG). Trait is determined by sample type (tumor vs normal)."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note=note
)

# Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
    print(f"Linked data saved to: {out_data_file}")
else:
    print("Dataset was not usable and was not saved.")