File size: 5,135 Bytes
9e2af38 |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Metabolic_Rate"
cohort = "GSE40873"
# Input paths
in_trait_dir = "../DATA/GEO/Metabolic_Rate"
in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE40873"
# Output paths
out_data_file = "./output/preprocess/3/Metabolic_Rate/GSE40873.csv"
out_gene_data_file = "./output/preprocess/3/Metabolic_Rate/gene_data/GSE40873.csv"
out_clinical_data_file = "./output/preprocess/3/Metabolic_Rate/clinical_data/GSE40873.csv"
json_path = "./output/preprocess/3/Metabolic_Rate/cohort_info.json"
# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
print(f"\n{row}:")
print(values)
# 1. Gene expression data availability
# Yes - The background indicates this is a genome-wide gene expression analysis
is_gene_available = True
# 2. Variable availability and data type conversion
# 2.1 Row identifiers
trait_row = 2 # Metabolic rate can be inferred from survival time
age_row = None # Age not available
gender_row = None # Gender not available
# 2.2 Conversion functions
def convert_trait(x):
"""Convert multicentric occurrence-free survival days to metabolic rate"""
try:
# Extract number after colon and convert to float
days = float(x.split(': ')[1])
# Higher survival time indicates better metabolic function
# Normalize to 0-1 range using 2500 days as max
return min(days/2500, 1.0)
except:
return None
def convert_age(x):
return None # Not used since age data unavailable
def convert_gender(x):
return None # Not used since gender data unavailable
# 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=trait_row is not None
)
# 4. Extract clinical features
clinical_df = geo_select_clinical_features(
clinical_df=clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait
)
# Preview and save clinical data
print(preview_df(clinical_df))
clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Examine data structure
print("Data structure and head:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])
# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Based on ID format (e.g., '1007_s_at'), these are Affymetrix probe IDs, not gene symbols
# They need to be mapped to standard gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)
# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Get gene mapping from annotation data
# 'ID' column stores probe IDs matching gene expression data
# 'Gene Symbol' column stores target gene symbols
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
# Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview mapped gene expression data
print("Shape after gene mapping:", gene_data.shape)
print("\nFirst few mapped genes and their expression values:")
print(gene_data.head())
# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
# 3. Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and information saving
note = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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=note
)
# 6. Save linked data only if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |