File size: 6,100 Bytes
f2fc1fc |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Rectal_Cancer"
cohort = "GSE133057"
# Input paths
in_trait_dir = "../DATA/GEO/Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE133057"
# Output paths
out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE133057.csv"
out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE133057.csv"
out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE133057.csv"
json_path = "./output/preprocess/3/Rectal_Cancer/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, from background info this is transcriptomic analysis of rectal cancer biopsies
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# AJCC score is a measure of tumor regression (phenotypic trait)
trait_row = 1
gender_row = 2
age_row = 5
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
"""Convert AJCC score to binary where 0,1 are good response (0) and 2,3 are poor response (1)"""
if not value or ':' not in value:
return None
score = int(value.split(': ')[1])
if score in [0, 1]:
return 0 # good response
elif score in [2, 3]:
return 1 # poor response
return None
def convert_age(value: str) -> float:
"""Convert age string to float"""
if not value or ':' not in value:
return None
try:
return float(value.split(': ')[1])
except:
return None
def convert_gender(value: str) -> int:
"""Convert gender to binary (0=female, 1=male)"""
if not value or ':' not in value:
return None
gender = value.split(': ')[1].lower()
if gender == 'female':
return 0
elif gender == 'male':
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:
clinical_features = 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)
print("Preview of extracted clinical features:")
print(preview_df(clinical_features))
clinical_features.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 "ILMN_" prefix in gene identifiers, these appear to be Illumina probe IDs, not gene symbols
# They will 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))
# 1. From observation:
# - Gene expression data uses 'ILMN_' prefixed IDs
# - In gene annotation, 'ID' column stores these identifiers, and 'Symbol' column stores gene symbols
# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Symbol')
# 3. Apply mapping to convert probe level data to gene level data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview and save the gene expression data
print("\nGene expression data shape:", gene_data.shape)
print("\nPreview of gene expression data:")
print(preview_df(gene_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
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_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 = "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."
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) |