File size: 5,259 Bytes
1f52ac2 |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Mesothelioma"
cohort = "GSE64738"
# Input paths
in_trait_dir = "../DATA/GEO/Mesothelioma"
in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE64738"
# Output paths
out_data_file = "./output/preprocess/3/Mesothelioma/GSE64738.csv"
out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE64738.csv"
out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE64738.csv"
json_path = "./output/preprocess/3/Mesothelioma/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
# Dataset mentions Affymetrix microarrays for gene expression profiling
is_gene_available = True
# 2.1 Data Availability
# From sample characteristics, trait status can be extracted from index 0
trait_row = 0
# Age and gender data not available in sample characteristics
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value):
"""Convert cell type to binary trait - 1 for mesothelioma cell/xenograft, 0 for normal"""
if not value:
return None
value = value.lower()
if 'mesothelioma' in value:
return 1
return None
# No age/gender conversion functions needed since data not available
convert_age = None
convert_gender = None
# 3. Save Initial 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. Extract Clinical Features
# Since trait_row is not None, proceed with feature extraction
selected_clinical = geo_select_clinical_features(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 selected features
preview_df(selected_clinical)
# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical.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])
# Review the gene identifiers
# The IDs have the format like "1007_s_at" which are Affymetrix probe IDs
# These need to be mapped to human gene symbols for consistency
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))
# Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
# Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Save gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 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, 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 mesothelioma samples, but case/control ratio is heavily imbalanced."
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) |