File size: 5,509 Bytes
1a37a63 |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Ovarian_Cancer"
cohort = "GSE126132"
# Input paths
in_trait_dir = "../DATA/GEO/Ovarian_Cancer"
in_cohort_dir = "../DATA/GEO/Ovarian_Cancer/GSE126132"
# Output paths
out_data_file = "./output/preprocess/3/Ovarian_Cancer/GSE126132.csv"
out_gene_data_file = "./output/preprocess/3/Ovarian_Cancer/gene_data/GSE126132.csv"
out_clinical_data_file = "./output/preprocess/3/Ovarian_Cancer/clinical_data/GSE126132.csv"
json_path = "./output/preprocess/3/Ovarian_Cancer/cohort_info.json"
# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
print(f"\n{feature}:")
print(values)
# 1. Gene Expression Data
is_gene_available = True # The series title and design indicate RNA extraction from cells
# 2.1. Data Availability
trait_row = 1 # 'tissue' field indicates ovarian cancer status
age_row = None # Age data not available
gender_row = None # Gender data not available
# 2.2. Data Type Conversion Functions
def convert_trait(value: str) -> int:
"""Convert tissue type to binary where HGSOC=1"""
if not isinstance(value, str):
return None
val = value.split(': ')[-1].lower()
if 'high-grade serous ovarian cancer' in val or 'hgsoc' in val:
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. Extract Clinical Features
if trait_row is not None:
selected_clinical = geo_select_clinical_features(
clinical_df=clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
age_row=age_row,
convert_age=None,
gender_row=gender_row,
convert_gender=None
)
# Preview the processed clinical data
preview = preview_df(selected_clinical)
print("Preview of processed clinical data:")
print(preview)
# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)
# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
# Verify this is gene expression data and check identifiers
is_gene_available = True
# Save updated 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)
)
# Save gene expression data
genetic_data.to_csv(out_gene_data_file)
# The identifiers start with "ILMN_" which indicates they are Illumina probe IDs
# These need to be mapped to human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path)
# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# Get the mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')
# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Preview the gene-level expression data
print("Gene expression data preview:")
print("\nColumn names:")
print(list(gene_data.columns)[:5])
print("\nFirst 5 rows:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=True,
is_biased=trait_biased,
df=linked_data,
note="Gene expression data comparing ovarian cancer cell lines (HEY, SKOV3) with prostate cancer cell line (PC3), examining miRNA effects on MET."
)
# 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) |