File size: 5,861 Bytes
dd19378 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE49278"
# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE49278"
# Output paths
out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE49278.csv"
out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE49278.csv"
out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE49278.csv"
json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# 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
# Yes - Affymetrix Human Gene 2.0 ST Array data mentioned in background info
is_gene_available = True
# 2. Variable Availability and Row Identification
trait_row = 2 # Cell type row contains ACC info
age_row = 0 # Age data available
gender_row = 1 # Gender data available
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
"""Convert cell type to binary where ACC=1"""
if pd.isna(value):
return None
value = value.split(': ')[-1].strip().lower()
if 'adrenocortical carcinoma' in value:
return 1
return None
def convert_age(value: str) -> float:
"""Convert age to continuous numeric value"""
if pd.isna(value):
return None
value = value.split(': ')[-1].strip()
try:
return float(value)
except:
return None
def convert_gender(value: str) -> int:
"""Convert gender to binary where F=0, M=1"""
if pd.isna(value):
return None
value = value.split(': ')[-1].strip().upper()
if value == 'F':
return 0
elif value == 'M':
return 1
return None
# 3. Save initial 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. Extract clinical features
if trait_row is not None:
clinical_features = geo_select_clinical_features(
clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
age_row=age_row,
convert_age=convert_age,
gender_row=gender_row,
convert_gender=convert_gender
)
# Preview the extracted features
print("Preview of extracted clinical features:")
print(preview_df(clinical_features))
# Save to CSV
clinical_features.to_csv(out_clinical_data_file)
# 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 identifiers appear to be numeric probe IDs (16650001, etc)
# which are not human gene symbols and will need to be mapped
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# First inspect the SOFT file contents to understand the annotation format
import gzip
print("Inspecting SOFT file for gene mapping information:")
pattern = None
with gzip.open(soft_file, 'rt') as f:
for i, line in enumerate(f):
if i < 100: # Check first 100 lines
if "gene_assignment" in line or "gene_symbol" in line:
print(f"\nFound gene mapping pattern in line: {line.strip()}")
pattern = line
elif "transcript_id" in line or "mrna_assignment" in line:
print(f"\nFound alternative mapping pattern in line: {line.strip()}")
pattern = line
else:
break
# Based on file inspection, extract gene annotation with appropriate prefixes
gene_annotation = get_gene_annotation(soft_file, prefixes=['#', '!platform_table_begin', '!platform_table_end'])
# Preview gene annotation data structure
print("\nGene annotation shape:", gene_annotation.shape)
print("\nAvailable columns:")
print(gene_annotation.columns.tolist())
# Display a few rows of relevant mapping columns
mapping_cols = [col for col in gene_annotation.columns if 'gene' in col.lower()
or 'symbol' in col.lower()
or 'transcript' in col.lower()
or col == 'ID']
if mapping_cols:
print("\nPreview of mapping-related columns:")
print(gene_annotation[mapping_cols].head())
else:
print("\nNo obvious gene mapping columns found. Displaying first row:")
print(gene_annotation.iloc[0]) |