File size: 5,119 Bytes
75faa94 |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Eczema"
cohort = "GSE182740"
# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE182740"
# Output paths
out_data_file = "./output/preprocess/3/Eczema/GSE182740.csv"
out_gene_data_file = "./output/preprocess/3/Eczema/gene_data/GSE182740.csv"
out_clinical_data_file = "./output/preprocess/3/Eczema/clinical_data/GSE182740.csv"
json_path = "./output/preprocess/3/Eczema/cohort_info.json"
# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values for each feature (row) in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Series title and summary indicate this is a gene expression microarray study
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# For trait (Eczema): Key 1 contains disease info with Atopic_dermatitis
trait_row = 1
# Age and gender are not recorded in the sample characteristics
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value):
if pd.isna(value):
return None
value = value.split(": ")[-1].strip()
# Convert to binary based on presence of atopic dermatitis
if value == "Atopic_dermatitis":
return 1
elif value == "Mixed": # Mixed includes atopic dermatitis
return 1
elif value in ["Psoriasis", "Normal_skin"]:
return 0
return None
def convert_age(value):
# No age data
return None
def convert_gender(value):
# No gender data
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:
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 data
print(preview_df(selected_clinical_df))
# Save to CSV
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)
# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])
print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# From the ID format '1007_s_at', '1053_at' etc., these appear to be Affymetrix probe IDs
# rather than human gene symbols and will need to be mapped to standard gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file)
# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# Get ID to Gene Symbol mapping using columns from gene metadata
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
# Apply the mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_df, mapping_df)
# Print shape and preview first few rows
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
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
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and metadata saving
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="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
)
# 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) |