File size: 5,919 Bytes
a0b62f5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Arrhythmia"
cohort = "GSE182600"
# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE182600"
# Output paths
out_data_file = "./output/preprocess/3/Arrhythmia/GSE182600.csv"
out_gene_data_file = "./output/preprocess/3/Arrhythmia/gene_data/GSE182600.csv"
out_clinical_data_file = "./output/preprocess/3/Arrhythmia/clinical_data/GSE182600.csv"
json_path = "./output/preprocess/3/Arrhythmia/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
is_gene_available = True # Yes, this dataset contains gene expression data from PBMCs
# 2. Variable Availability and Data Type Conversion
trait_row = 3 # 'outcome' row contains binary outcome data
age_row = 1 # 'age' row contains continuous age values
gender_row = 2 # 'gender' row contains binary gender data
def convert_trait(value: str) -> int:
"""Convert outcome status to binary: Success=1, Failure=0"""
if not value:
return None
value = value.split(': ')[-1].lower()
if value == 'success':
return 1
elif value in ['failure', 'fail']:
return 0
return None
def convert_age(value: str) -> float:
"""Convert age string to float"""
if not value:
return None
try:
return float(value.split(': ')[-1])
except:
return None
def convert_gender(value: str) -> int:
"""Convert gender to binary: F=0, M=1"""
if not value:
return None
value = value.split(': ')[-1].upper()
if value == 'F':
return 0
elif value == 'M':
return 1
return None
# 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
if trait_row is not None:
selected_clinical_df = geo_select_clinical_features(
clinical_df=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 processed clinical data
preview = preview_df(selected_clinical_df)
print("Preview of processed clinical data:")
print(preview)
# Save to CSV
selected_clinical_df.to_csv(out_clinical_data_file)
# 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)
# ILMN_ prefix indicates these are Illumina microarray probe IDs, not gene symbols
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)
# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# Get gene mapping between probe IDs (ID) and gene symbols (Symbol)
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
# Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_data)
# Preview result to verify mapping worked correctly
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few mapped gene symbols:")
print(gene_data.index[:10])
print("\nFirst few rows of expression data:")
print(gene_data.head())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Evaluate bias
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate and save cohort info
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=is_biased,
df=linked_data
)
# 6. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file) |