File size: 6,382 Bytes
4144951 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Von_Hippel_Lindau"
cohort = "GSE33093"
# Input paths
in_trait_dir = "../DATA/GEO/Von_Hippel_Lindau"
in_cohort_dir = "../DATA/GEO/Von_Hippel_Lindau/GSE33093"
# Output paths
out_data_file = "./output/preprocess/3/Von_Hippel_Lindau/GSE33093.csv"
out_gene_data_file = "./output/preprocess/3/Von_Hippel_Lindau/gene_data/GSE33093.csv"
out_clinical_data_file = "./output/preprocess/3/Von_Hippel_Lindau/clinical_data/GSE33093.csv"
json_path = "./output/preprocess/3/Von_Hippel_Lindau/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
# Based on series description, this is a gene expression study
is_gene_available = True
# 2.1 Data Availability
# Looking at sample characteristics, no explicit trait/age/gender data found in the rows
# The data does not contain VHL status information needed for the trait
trait_row = None
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(x):
# No VHL status data available, function not used
return None
def convert_age(x):
# Age conversion function not used since data not available
return None
def convert_gender(x):
# Gender conversion function not used since data not available
return None
# 3. Save Initial 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
# Skip since trait_row is None, indicating no clinical data available
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs and shape of data
print("Shape of genetic data:", genetic_data.shape)
print("\nFirst 5 rows with sample columns:")
print(genetic_data.head())
print("\nFirst 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# Print first few lines of raw matrix file to inspect format
print("\nFirst few lines of raw matrix file:")
with gzip.open(matrix_file_path, 'rt') as f:
for i, line in enumerate(f):
if i < 10: # Print first 10 lines
print(line.strip())
elif "!series_matrix_table_begin" in line:
print("\nFound table marker at line", i)
# Print next 3 lines after marker
for _ in range(3):
print(next(f).strip())
break
# The gene identifiers appear to be simple numeric indices (1,2,3...) rather than official gene symbols
# This indicates they are likely probe IDs that need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file with adjusted prefix filtering
gene_annotation = get_gene_annotation(soft_file_path, prefixes=['!Platform_table_begin', '!Platform_table_end'])
# Preview both headers and first few rows
print("Gene annotation column names:")
print(gene_annotation.columns.tolist())
print("\nGene annotation preview:")
preview = preview_df(gene_annotation)
print(preview)
# Also check raw file content around platform table section
print("\nChecking raw SOFT file content around platform table:")
with gzip.open(soft_file_path, 'rt') as f:
in_platform_table = False
for i, line in enumerate(f):
if '!Platform_table_begin' in line:
print(f"\nFound table begin at line {i}:")
in_platform_table = True
# Print header and first few lines
for _ in range(5):
print(next(f).strip())
break
# Since the SOFT file seems to have a non-standard format, let's examine the raw file first
platform_data_lines = []
with gzip.open(soft_file_path, 'rt') as f:
in_platform_table = False
for line in f:
if '!Platform_table_begin' in line:
in_platform_table = True
continue
elif '!Platform_table_end' in line:
in_platform_table = False
break
elif in_platform_table:
platform_data_lines.append(line.strip())
# Check if we got any platform data
if len(platform_data_lines) > 0:
# Convert platform data to dataframe
platform_data = pd.read_csv(io.StringIO('\n'.join(platform_data_lines)), sep='\t', low_memory=False)
# Print columns to verify we have the platform data
print("Platform data columns:", platform_data.columns.tolist())
print("\nFirst few rows of platform data:")
print(platform_data.head())
# Create mapping and apply it if we have the required columns
id_col = [col for col in platform_data.columns if 'ID' in col.upper()][0] if any('ID' in col.upper() for col in platform_data.columns) else None
gene_col = [col for col in platform_data.columns if 'GENE' in col.upper() and 'SYMBOL' in col.upper()][0] if any('GENE' in col.upper() and 'SYMBOL' in col.upper() for col in platform_data.columns) else None
if id_col and gene_col:
mapping_df = get_gene_mapping(platform_data, prob_col=id_col, gene_col=gene_col)
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Save gene expression data
if gene_data is not None:
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("\nGene data shape:", gene_data.shape)
print("First few genes and their expression values:")
print(gene_data.head())
else:
print("Could not identify ID and Gene Symbol columns in platform data")
gene_data = None
else:
print("Failed to extract platform table with probe-gene mappings")
gene_data = None |