File size: 5,570 Bytes
324058b |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE41177"
# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE41177"
# Output paths
out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE41177.csv"
out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE41177.csv"
out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE41177.csv"
json_path = "./output/preprocess/3/Atrial_Fibrillation/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 Availability
is_gene_available = True # Dataset contains microarray gene expression data per background info
# 2.1 Data Availability
trait_row = 3 # 'af duration' indicates AF status duration
age_row = 2 # Age data available
gender_row = 1 # Gender data available
# 2.2 Data Type Conversion Functions
def convert_trait(x):
if not isinstance(x, str):
return None
value = x.split(': ')[-1].strip()
# Convert AF duration to binary - any duration indicates AF presence
if value == '0M':
return 0
elif 'M' in value: # Has months duration
return 1
return None
def convert_age(x):
if not isinstance(x, str):
return None
value = x.split(': ')[-1].strip()
if value.endswith('Y'):
try:
return float(value[:-1]) # Remove 'Y' and convert to float
except:
return None
return None
def convert_gender(x):
if not isinstance(x, str):
return None
value = x.split(': ')[-1].strip().lower()
if value == 'female':
return 0
elif value == 'male':
return 1
return None
# 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
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 processed clinical data
print(preview_df(selected_clinical_df))
# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# These are Affymetrix probe IDs (starting with numbers and containing "_at"), not human gene symbols
# They need to be mapped to standard 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 gene mapping dataframe from annotation data
# 'ID' column contains probe IDs matching genetic_data, 'Gene Symbol' contains gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
# Apply gene mapping to convert from probes to genes
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview the first few rows and columns of the mapped gene data
print("\nFirst few rows of mapped gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save gene data
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(selected_clinical_df, 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="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."
)
# 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) |