File size: 5,717 Bytes
d5514d2 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Rheumatoid_Arthritis"
cohort = "GSE224842"
# Input paths
in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE224842"
# Output paths
out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE224842.csv"
out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE224842.csv"
out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE224842.csv"
json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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 background info, this is DNA microarray data of PBMCs, so gene expression data is available
is_gene_available = True
# 2.1 Data Availability
# The only feature indicates all samples are RA patients
trait_row = 0 # From Feature 0: "disease state: rheumatoid arthritis"
age_row = None # Age data not available
gender_row = None # Gender data not available
# 2.2 Data Type Conversion Functions
def convert_trait(value):
"""Convert trait values to binary"""
if pd.isna(value):
return None
# Extract value after colon if present
if ':' in str(value):
value = value.split(':')[1].strip()
# Convert to binary where 1 = has disease
if 'rheumatoid arthritis' in value.lower():
return 1
return 0
def convert_age(value):
"""Convert age values - not used since age not available"""
return None
def convert_gender(value):
"""Convert gender values - not used since gender not available"""
return None
# 3. Save metadata for initial filtering
# trait_row is not None, so trait data is available
is_trait_available = True if trait_row is not None else 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
)
# 4. Extract clinical features since trait_row is not None
selected_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 processed clinical data
print("Preview of processed clinical data:")
print(preview_df(selected_clinical_df))
# Save to CSV
selected_clinical_df.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)
# The probe IDs starting with "A_23_P" indicate this is an Agilent microarray dataset
# These are probe IDs and need to be mapped to human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)
# Preview the annotation data
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Extract ID and GENE_SYMBOL columns from gene annotation for mapping
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')
# Apply gene mapping to convert probe measurements to gene expression values
gene_data = apply_gene_mapping(gene_data, mapping_data)
# Preview the processed gene data
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for bias
trait_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=trait_biased,
df=linked_data,
note="Study examining transcriptome profiles in rheumatoid arthritis."
)
# 6. Save if usable
if is_usable:
linked_data.to_csv(out_data_file) |