File size: 5,499 Bytes
ee5a411 |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Glucocorticoid_Sensitivity"
cohort = "GSE15820"
# Input paths
in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE15820"
# Output paths
out_data_file = "./output/preprocess/3/Glucocorticoid_Sensitivity/GSE15820.csv"
out_gene_data_file = "./output/preprocess/3/Glucocorticoid_Sensitivity/gene_data/GSE15820.csv"
out_clinical_data_file = "./output/preprocess/3/Glucocorticoid_Sensitivity/clinical_data/GSE15820.csv"
json_path = "./output/preprocess/3/Glucocorticoid_Sensitivity/cohort_info.json"
# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")
# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
print(f"{row}:")
print(f" {values}")
print()
# 1. Check gene expression data availability
# Based on the background info, this is a gene expression profiling study
# using Exon 1.0 microarrays examining ZBTB16 response genes
is_gene_available = True
# 2. Variable availability and type conversion
# 2.1 Data availability
trait_row = 1 # Treatment indicates glucocorticoid sensitivity
age_row = None # No age data available - cell line study
gender_row = None # No gender data - cell line study
# 2.2 Data type conversion functions
def convert_trait(x):
"""Convert treatment info to binary GC sensitivity.
Dex/DexDox treatment indicates GC exposure (1), none/Dox indicates control (0)"""
if not isinstance(x, str):
return None
value = x.split(': ')[1].strip() if ': ' in x else x
if value in ['Dex', 'DexDox']:
return 1
elif value in ['none', 'Dox']:
return 0
return None
def convert_age(x):
return None # No age data
def convert_gender(x):
return None # No gender data
# 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. Extract clinical features
if trait_row is not None:
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 extracted features
print("Preview of clinical features:")
print(preview_df(clinical_df))
# Save to CSV
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These identifiers appear to be probe IDs, not human gene symbols
# They need to be mapped to actual gene symbols for biological interpretation
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)
# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
print(f"\n{col}:")
print(values)
# 1. Based on the preview, we need to map between 'ID' and 'symbol' columns
# in gene_annotation to convert probe IDs to gene symbols
# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='symbol')
# 3. Apply gene mapping to convert probe-level measurements to gene expression
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)
# Read the processed clinical data file
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# Validate data quality and save cohort info
note = "Gene expression data from glucocorticoid sensitivity study."
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
)
# 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)
else:
print(f"Dataset {cohort} did not pass quality validation and will not be saved.") |