File size: 5,158 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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE115574"
# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE115574"
# Output paths
out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE115574.csv"
out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE115574.csv"
out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE115574.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
# The background info mentions "Affymetrix human gene expression microarrays"
is_gene_available = True
# 2.1 Variable Availability
trait_row = 0 # Disease state field contains AF vs SR status
age_row = None # Age not available
gender_row = None # Gender not available
# 2.2 Data Type Conversion
def convert_trait(value):
if not isinstance(value, str):
return None
val = value.lower().split(': ')[-1].strip()
if 'atrial fibrillation' in val:
return 1
elif 'sinus rhythm' in val:
return 0
return None
def convert_age(value):
return None # Not used but defined for completeness
def convert_gender(value):
return None # Not used but defined for completeness
# 3. Save Metadata
is_usable = 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. 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
)
# Preview the extracted features
preview = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview)
# Save to CSV
clinical_features.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 appear to be Affymetrix probe IDs (e.g. '1007_s_at') rather than human gene symbols
# They will 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)
# 1. ID and Gene Symbol columns identified from the annotation preview
# ID column matches the probe IDs in gene expression data (e.g. '1007_s_at')
# Gene Symbol column contains the target gene symbols
prob_col = 'ID'
gene_col = 'Gene Symbol'
# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)
# 3. Apply gene mapping to convert probe measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Preview the first few rows to verify the mapping
print("\nFirst few rows of mapped gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save gene data
genetic_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_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, genetic_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 postoperative atrial fibrillation vs sinus rhythm."
)
# 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) |