File size: 5,337 Bytes
54d4d57 |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Huntingtons_Disease"
cohort = "GSE135589"
# Input paths
in_trait_dir = "../DATA/GEO/Huntingtons_Disease"
in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE135589"
# Output paths
out_data_file = "./output/preprocess/3/Huntingtons_Disease/GSE135589.csv"
out_gene_data_file = "./output/preprocess/3/Huntingtons_Disease/gene_data/GSE135589.csv"
out_clinical_data_file = "./output/preprocess/3/Huntingtons_Disease/clinical_data/GSE135589.csv"
json_path = "./output/preprocess/3/Huntingtons_Disease/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
# From the background information, we can see this is a study of gene expression in blood
is_gene_available = True
# 2. Data Availability and Type Conversion
# 2.1 Row identifiers
trait_row = 4 # "disease stage" contains HD status
age_row = 2 # "age at year 1" contains age information
gender_row = 1 # "Sex" contains gender information
# 2.2 Data type conversion functions
def convert_trait(value: str) -> int:
"""Convert disease stage to binary (0: control, 1: HD-related)"""
if not value or ':' not in value:
return None
stage = value.split(':')[1].strip().lower()
if 'control' in stage:
return 0
elif any(x in stage for x in ['prehd', 'zhd']):
return 1
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].strip())
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].strip().lower()
if 'female' in gender:
return 0
elif 'male' in gender:
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. Clinical Feature Extraction
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=convert_age,
gender_row=gender_row,
convert_gender=convert_gender
)
# Preview the data
preview = preview_df(selected_clinical)
# Save to CSV
selected_clinical.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())
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())
print("\nPreview of first few rows:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
# Apply the gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Preview first few rows of gene expression data
print("Preview of gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# Get clinical features
clinical_features = geo_select_clinical_features(
clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
gender_row=gender_row,
convert_gender=convert_gender
)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 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 contains gene expression data from human embryonic stem cells and their neural stem cell progeny, comparing HD mutation carriers with wild type controls"
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) |