File size: 5,295 Bytes
5a96bf0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Prostate_Cancer"
cohort = "GSE235003"

# Input paths
in_trait_dir = "../DATA/GEO/Prostate_Cancer"
in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE235003"

# Output paths
out_data_file = "./output/preprocess/3/Prostate_Cancer/GSE235003.csv"
out_gene_data_file = "./output/preprocess/3/Prostate_Cancer/gene_data/GSE235003.csv"
out_clinical_data_file = "./output/preprocess/3/Prostate_Cancer/clinical_data/GSE235003.csv"
json_path = "./output/preprocess/3/Prostate_Cancer/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics 
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
is_gene_available = True  # Series contains gene expression data studying OC2's effect

# 2.1 Data Availability 
trait_row = None  # All samples are prostate cancer cell lines, no control group
age_row = None   # Not applicable for cell lines
gender_row = None  # Not applicable for cell lines

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    return 1 if 'Prostate cancer' in x else 0

def convert_age(x):
    return None  # Not used

def convert_gender(x): 
    return None  # Not used

# 3. Save Metadata
_ = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False  # trait_row is None
)

# 4. Skip clinical feature extraction since trait_row is None
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# The gene identifiers appear to be just numeric indices (4, 5, 6, etc)
# not gene symbols or other interpretable identifiers
# This data will require mapping to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Try searching for ID patterns in all columns
print("All column names:", gene_metadata.columns.tolist())
print("\nPreview first few rows of each column to locate numeric IDs:")
for col in gene_metadata.columns:
    sample_values = gene_metadata[col].dropna().head().tolist()
    print(f"\n{col}:")
    print(sample_values)

# Inspect raw file to see unfiltered annotation format
import gzip
print("\nRaw SOFT file preview:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    header = []
    for i, line in enumerate(f):
        header.append(line.strip())
        if i >= 10:  # Preview first 10 lines
            break
print('\n'.join(header))
# 1. Based on observation:
# - In expression data, identifiers are numeric IDs starting from 4,5,6...
# - In annotation data, 'ID' column contains numeric strings matching these identifiers
# - 'GENE_SYMBOL' column contains human gene symbols we want to map to

# 2. Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')

# 3. Convert probe-level data to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Print shape and preview to verify the mapping
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows after mapping to genes:")
print(gene_data.head())
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2-6. Record that this dataset is not usable due to missing clinical data
is_usable = validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,  
    is_biased=True,  # Set to True since no clinical data makes it unusable
    df=gene_data,    # Pass the gene expression data
    note="Contains normalized gene expression data but no clinical features for trait analysis."
)