File size: 5,466 Bytes
ff3b0fa |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE94524"
# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE94524"
# Output paths
out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE94524.csv"
out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE94524.csv"
out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE94524.csv"
json_path = "./output/preprocess/3/Endometrioid_Cancer/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))
# Step 1: Gene Expression Data Availability
# From title, this appears to be a gene expression dataset studying tamoxifen-associated endometrial tumors
is_gene_available = True
# Step 2: Variable Availability and Data Type Conversion
# From characteristics dict, all samples are endometrioid adenocarcinoma (trait=1)
trait_row = 0
age_row = None # Age data not available
gender_row = None # Gender data not available, but since endometrial cancer, we know all patients are female
def convert_trait(value: str) -> int:
"""Convert trait value to binary (0 for normal/control, 1 for endometrioid cancer)"""
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
if 'endometrioid' in value and 'adenocarcinoma' in value:
return 1
return None
# Age conversion function not needed since age data unavailable
convert_age = None
# Gender conversion function not needed since gender data unavailable
convert_gender = None
# Step 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
)
# Step 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 selected features
preview = preview_df(selected_clinical_df)
print("Preview of selected clinical features:")
print(preview)
# Save clinical data
selected_clinical_df.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])
# The gene identifiers appear to be just row numbers (1, 2, 3, etc.)
# This indicates they need to be mapped to actual human gene symbols
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))
# From the preview, we can see that 'ID' column matches the gene expression row IDs,
# and 'HUGO' column contains the gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='HUGO')
# Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_df, mapping_data)
# Preview the mapped gene expression data
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few gene symbols:")
print(gene_data.index[:10])
print("\nPreview of first few rows and columns:")
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
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_df, 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="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
)
# 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) |