File size: 5,116 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Stroke"
cohort = "GSE68526"
# Input paths
in_trait_dir = "../DATA/GEO/Stroke"
in_cohort_dir = "../DATA/GEO/Stroke/GSE68526"
# Output paths
out_data_file = "./output/preprocess/3/Stroke/GSE68526.csv"
out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE68526.csv"
out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE68526.csv"
json_path = "./output/preprocess/3/Stroke/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)
# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")
# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
print(f"Feature: {feature}")
print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Based on series title and overall design, this is RNA transcriptome data from blood samples
is_gene_available = True
# 2.1 Data Availability
# trait (stroke) info is in feature 5 'diabcvdcastr' which includes stroke status
trait_row = 5
age_row = 0
gender_row = 1 # female: 0/1 in feature 1
# 2.2 Data Type Conversion Functions
def convert_trait(x):
# diabcvdcastr includes multiple conditions, but since we're looking for stroke specifically,
# any positive value indicates stroke presence
if not x or 'missing' in x.lower():
return None
try:
value = int(x.split(': ')[1])
return value
except:
return None
def convert_age(x):
if not x or 'missing' in x.lower():
return None
try:
# Extract numbers after colon
age = int(x.split(': ')[1])
return age
except:
return None
def convert_gender(x):
if not x or 'missing' in x.lower():
return None
try:
# female: 0/1 needs to be flipped since we want male=1
female = int(x.split(': ')[1])
return 1 - female # converts female:1 to 0 and female:0 to 1
except:
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:
clinical_features = 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
preview = preview_df(clinical_features)
# Save to CSV
clinical_features.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)
# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])
# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
lines = []
for i, line in enumerate(f):
if "!series_matrix_table_begin" in line:
# Get the next 5 lines after the marker
for _ in range(5):
lines.append(next(f).strip())
break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
print(line)
# Based on the raw data shown, we can see that the IDs like A1BG, A1CF are human gene symbols
# These are standard HGNC gene symbols, so no mapping is needed
requires_gene_mapping = False
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# 2. Load clinical data and link with 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
linked_data = handle_missing_values(linked_data, trait)
# 4. Evaluate bias
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate and save cohort info
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="Study examining transcriptome profiles from peripheral blood of older adults, including some with stroke history."
)
# 6. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file) |