File size: 5,366 Bytes
5716e25
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE53543"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE53543"

# Output paths
out_data_file = "./output/preprocess/3/Cystic_Fibrosis/GSE53543.csv"
out_gene_data_file = "./output/preprocess/3/Cystic_Fibrosis/gene_data/GSE53543.csv"
out_clinical_data_file = "./output/preprocess/3/Cystic_Fibrosis/clinical_data/GSE53543.csv"
json_path = "./output/preprocess/3/Cystic_Fibrosis/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Yes, dataset contains genome-wide gene expression data from Illumina array

# 2. Variable Availability and Data Type Conversion
# Trait (RV infection status)
trait_row = 2  # 'sample group' row contains infection status

def convert_trait(value):
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if value == 'RV_infected':
        return 1
    elif value == 'Uninfected':
        return 0
    return None

# Gender 
gender_row = 1  # 'gender' row contains gender info

def convert_gender(value):
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if value == 'Female':
        return 0
    elif value == 'Male':
        return 1
    return None

# Age
age_row = None  # Age information not available in sample characteristics

def convert_age(value):
    return None  # Not used but defined for completeness

# 3. Save Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, 
                            is_gene_available=is_gene_available, 
                            is_trait_available=is_trait_available)

# 4. Clinical Feature Extraction
selected_features = geo_select_clinical_features(clinical_df=clinical_data,
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait,
                                               gender_row=gender_row,
                                               convert_gender=convert_gender)

# Preview the extracted features
print("Preview of extracted clinical features:")
print(preview_df(selected_features))

# Save clinical features
selected_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# ILMN_ prefixes indicate these are Illumina probe IDs
# They need to be mapped to human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. From inspection: 'ID' column in annotation matches probe IDs, 'Symbol' column has gene symbols
probe_col = 'ID'
gene_col = 'Symbol'

# 2. Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, probe_col, gene_col)

# 3. Convert probe measurements to gene expression data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Print shape and preview to verify the mapping
print("Gene expression data shape:", gene_data.shape)
print("\nPreview of gene expression data:")  
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_features, gene_data)

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

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
is_usable = validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True, 
    is_biased=trait_biased,
    df=linked_data,
    note="Cell line study comparing deltaF508 CFTR mutant with wildtype CFTR in cystic fibrosis bronchial epithelial cells"
)

# 6. 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)