File size: 4,811 Bytes
8c96bfd |
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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Stroke"
cohort = "GSE186798"
# Input paths
in_trait_dir = "../DATA/GEO/Stroke"
in_cohort_dir = "../DATA/GEO/Stroke/GSE186798"
# Output paths
out_data_file = "./output/preprocess/3/Stroke/GSE186798.csv"
out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE186798.csv"
out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE186798.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
is_gene_available = True # Based on background info mentioning microarray analysis
# 2.1 Data Availability
trait_row = 1 # 'condition' row contains stroke/control status
gender_row = 0 # 'gender' row contains gender info
age_row = None # Age information not available
# 2.2 Data Type Conversion Functions
def convert_trait(x):
if not isinstance(x, str):
return None
value = x.split(': ')[-1].strip()
if value == 'Control':
return 0
elif value in ['PSND', 'PSD']: # Both are post-stroke cases
return 1
return None
def convert_gender(x):
if not isinstance(x, str):
return None
value = x.split(': ')[-1].strip()
if value == 'F':
return 0
elif value == 'M':
return 1
return None
# 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. 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,
gender_row=gender_row,
convert_gender=convert_gender
)
# Preview the processed clinical data
print("Preview of clinical features:")
print(preview_df(clinical_features))
# Save clinical features
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)
# From the identifiers visible in the first few rows (e.g., "AFFX-BkGr-GC03_st"),
# these appear to be Affymetrix probe IDs rather than standard human gene symbols.
# They need to be mapped to their corresponding gene symbols.
requires_gene_mapping = True
# From looking at the annotation data, we can see this is mouse data (Mus musculus)
# rather than human data. This makes the dataset unsuitable for human stroke studies.
# Therefore we need to stop processing this cohort.
# Save metadata indicating this dataset is not usable
validate_and_save_cohort_info(is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=False, # Set to False since mouse data can't be used
is_trait_available=True, # We did find stroke/control data
note="Dataset contains mouse rather than human gene expression data")
# Exit further processing as dataset is not suitable
print("WARNING: This dataset contains mouse gene expression data rather than human data.")
print("Stopping processing as mouse data is not suitable for human stroke studies.") |