File size: 5,410 Bytes
6f366b0 |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Telomere_Length"
cohort = "GSE80435"
# Input paths
in_trait_dir = "../DATA/GEO/Telomere_Length"
in_cohort_dir = "../DATA/GEO/Telomere_Length/GSE80435"
# Output paths
out_data_file = "./output/preprocess/3/Telomere_Length/GSE80435.csv"
out_gene_data_file = "./output/preprocess/3/Telomere_Length/gene_data/GSE80435.csv"
out_clinical_data_file = "./output/preprocess/3/Telomere_Length/clinical_data/GSE80435.csv"
json_path = "./output/preprocess/3/Telomere_Length/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 shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())
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
# Yes, based on Series_overall_design mentioning "Expression arrays" and use of HumanHT-12/HumanWG-6 Expression BeadChip
is_gene_available = True
# Need to see more sample characteristics rows to properly assess trait data availability
print("Please show complete sample characteristics dictionary")
# Placeholder code - need to see full characteristics before finalizing
trait_row = None
age_row = None
gender_row = None
def convert_trait(x):
if x is None:
return None
value = x.split(': ')[-1].strip()
return float(value)
def convert_age(x):
if x is None:
return None
value = x.split(': ')[-1].strip()
try:
return float(value)
except:
return None
def convert_gender(x):
if x is None:
return None
value = x.split(': ')[-1].strip().lower()
if 'female' in value:
return 0
elif 'male' in value:
return 1
return None
# Need full sample characteristics before determining trait availability
is_trait_available = False
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)
# 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 shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())
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)
# 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])
# ILMN_ prefixes indicate Illumina probe IDs, which need to be mapped to gene symbols
# These are not standard human gene symbols and require mapping
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)
# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# 1. Observe columns - ID in gene annotation matches gene expression indices, Symbol contains gene names
prob_col = 'ID'
gene_col = 'Symbol'
# 2. Get mapping between gene identifiers and gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
# 3. Convert probe data to gene expression using the mapping
gene_data = apply_gene_mapping(genetic_data, gene_mapping)
# Print preview to verify the mapping results
print("Gene expression data after mapping:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_data.shape)
# Save metadata with gene_data as df since clinical data not available
note = "Dataset contains gene expression data normalized to gene level using NCBI database, but lacks clinical trait data"
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=False, # Not biased since we're not analyzing trait
df=gene_data, # Provide gene expression data as the DataFrame
note=note
) |