File size: 5,657 Bytes
0a0878d |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Kidney_Chromophobe"
cohort = "GSE19982"
# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Chromophobe"
in_cohort_dir = "../DATA/GEO/Kidney_Chromophobe/GSE19982"
# Output paths
out_data_file = "./output/preprocess/3/Kidney_Chromophobe/GSE19982.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Chromophobe/gene_data/GSE19982.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Chromophobe/clinical_data/GSE19982.csv"
json_path = "./output/preprocess/3/Kidney_Chromophobe/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)
# Get unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# The background info shows this is mRNA profiling data (not miRNA/methylation)
is_gene_available = True
# 2.1 Data Availability and 2.2 Data Type Conversion
# Trait: Key 0 has disease state info, binary classification of chromophobe RCC vs oncocytoma
trait_row = 0
def convert_trait(value):
if not isinstance(value, str):
return None
value = value.lower()
if 'chromophobe' in value:
return 1 # Chromophobe RCC is positive class
elif 'oncocytoma' in value:
return 0 # Oncocytoma is negative class
return None
# Age and gender not available in sample characteristics
age_row = None
gender_row = None
def convert_age(value):
return None
def convert_gender(value):
return None
# 3. Save Metadata
# is_trait_available is True since trait_row is not None
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
# Since trait_row is not None, we extract clinical features
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 and save clinical data
print("Preview of clinical features:")
print(preview_df(clinical_df))
# Create directory if doesn't exist
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# These look like Affymetrix probe IDs rather than human gene symbols
# Format is typical of Affymetrix arrays with numeric_at pattern
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)
# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())
# Look at general data statistics
print("\nData shape:", gene_metadata.shape)
# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Extract gene mapping from annotation data
# 'ID' column in annotation matches probe IDs in expression data
# 'Gene Symbol' column contains the target gene symbols
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')
# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Preview the results
print("\nShape of probe data:", genetic_data.shape)
print("Shape of gene data:", gene_data.shape)
print("\nFirst 10 gene symbols:")
print(gene_data.index[:10].tolist())
# 1. Normalize gene symbols
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
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
is_biased = True
linked_data = None
else:
# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and save metadata
note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."
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,
note=note
)
# 6. Save the linked data only if it's usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |