File size: 5,338 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 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Rheumatoid_Arthritis"
cohort = "GSE236924"
# Input paths
in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE236924"
# Output paths
out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE236924.csv"
out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE236924.csv"
out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE236924.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
# From background, this is a gene array study of joint tissue comparing RA, OA and control
# So gene expression data should be available
is_gene_available = True
# 2.1 Data Availability
# Disease status (trait) is in row 0
trait_row = 0
# No age data available
age_row = None
# No gender data available
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value):
"""Convert trait values to binary (RA=1, non-RA=0)"""
if not isinstance(value, str):
return None
val = value.split(': ')[-1].strip().upper()
if val == 'RA':
return 1
elif val in ['OA', 'CONTROL']:
return 0
return None
def convert_age(value):
"""Not used since age data not available"""
return None
def convert_gender(value):
"""Not used since gender data not available"""
return None
# 3. Save initial 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. Extract clinical features since trait data is available
clinical_df = geo_select_clinical_features(clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait)
# Preview the extracted features
print(preview_df(clinical_df))
# Save clinical data
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)
# Gene identifiers in this GEO dataset appear to be Affymetrix probe IDs rather than gene symbols
# This is indicated by the format like "1007_s_at", "1053_at" etc.
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))
# 1. Gene identifiers are in 'ID' column, gene symbols in 'Gene Symbol' column
# Extract mapping info
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
# 2. Apply the mapping to convert probe-level measurements to gene-level expression
gene_data = apply_gene_mapping(gene_data, mapping_data)
# 3. Save the gene expression data
gene_data.to_csv(out_gene_data_file)
# 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(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) |