File size: 4,979 Bytes
13fd1a3 |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "COVID-19"
cohort = "GSE185658"
# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE185658"
# Output paths
out_data_file = "./output/preprocess/3/COVID-19/GSE185658.csv"
out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE185658.csv"
out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE185658.csv"
json_path = "./output/preprocess/3/COVID-19/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 from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Get unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Yes, this contains microarray gene expression data (mentioned in background)
is_gene_available = True
# 2.1 Data Availability
# Trait (asthma) is available in group field (row 1)
# Using asthma status as relevant trait for COVID-19 research
trait_row = 1
# Age and gender are not available
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
"""Convert asthma status to binary (0: healthy, 1: asthma)"""
if not value:
return None
# Extract value after colon
value = value.split(': ')[-1].strip().lower()
if 'asthma' in value:
return 1
elif 'healthy' in value:
return 0
return None
def convert_age(value: str) -> Optional[float]:
"""Convert age to float"""
return None # Not used since age data not available
def convert_gender(value: str) -> Optional[int]:
"""Convert gender to binary (0: female, 1: male)"""
return None # Not used since gender data not available
# 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. Extract Clinical Features
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
)
print("Preview of selected clinical features:")
print(preview_df(selected_clinical_df))
# Save to CSV
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data
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])
# The gene identifiers appear to be probe IDs from an array platform (7892XXX format)
# These numeric identifiers are not standard human gene symbols and will need to be mapped
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path)
# Preview column names and first few values
print("Gene annotation columns preview:")
print(preview_df(gene_metadata))
# Extract probe IDs and gene assignments from gene annotation data
# The 'ID' column contains probe IDs matching gene expression data
# The 'gene_assignment' column contains gene symbols
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')
# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Save gene data to CSV
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
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
# 3. Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# 4. Determine if features are biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and metadata saving
note = ""
if os.path.getsize(out_gene_data_file) == 0:
note = "Gene mapping failed - empty gene expression data"
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 if usable
if is_usable:
linked_data.to_csv(out_data_file) |