p3/preprocess/Rheumatoid_Arthritis/code/GSE224330.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rheumatoid_Arthritis"
cohort = "GSE224330"

# Input paths
in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE224330"

# Output paths
out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE224330.csv"
out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE224330.csv"
out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE224330.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 mentioning "gene expression profiling", "transcriptomic profile", "whole-genome transcriptomics"
is_gene_available = True

# 2.1 Variable Availability
trait_row = 0  # Can infer RA status from tissue source
age_row = 1  # Age data available in feature 1
gender_row = 2  # Gender data available in feature 2

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x):
        return None
    # First 10 samples (GSM7019507-GSM7019516) are from healthy controls based on background info
    # Rest are RA patients on different treatments
    sample_id = x.name
    sample_num = int(sample_id.replace('GSM',''))
    if 7019507 <= sample_num <= 7019516:
        return 0  # Healthy control
    else:
        return 1  # RA patient

def convert_age(x):
    if pd.isna(x):
        return None
    # Extract numeric value before 'y'
    try:
        age = int(x.split(':')[1].strip().replace('y',''))
        return age
    except:
        return None

def convert_gender(x):
    if pd.isna(x):
        return None
    value = x.split(':')[1].strip().lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    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
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 extracted features
preview = preview_df(selected_clinical_df)
print("Preview of extracted clinical features:")
print(preview)

# Save to CSV
selected_clinical_df.to_csv(out_clinical_data_file)
# The previous step output was not provided. Without it, we cannot properly:
# 1. Determine gene expression data availability
# 2. Identify row numbers for clinical features
# 3. Design appropriate conversion logic based on actual data values

# Therefore, this step cannot be completed until we receive:
# - Background information about the dataset
# - Sample characteristics dictionary showing available clinical data

raise ValueError("Previous step output with dataset information is required to analyze data availability and implement conversion logic")
# 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 identifiers starting with "A_19_P" appear to be Agilent microarray probe IDs
# These are not standard human gene symbols and need to be mapped to 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))
# 1. Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# 2. Extract gene mapping from annotation data
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

# 3. Apply mapping to convert probe-level data to gene-level data
gene_expression_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)

# Save processed gene data 
gene_expression_data.to_csv(out_gene_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)
# 1. Extract gene annotation data and observe identifiers 
# From previous outputs, we can see:
# - Gene expression data uses identifiers like 'A_19_P00315452'
# - Gene annotation data has matching IDs in the 'ID' column and gene symbols in 'GENE_SYMBOL'
gene_metadata = get_gene_annotation(soft_file)

# 2. Extract gene mapping from annotation data
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

# 3. Apply mapping to convert probe-level data to gene-level data
gene_expression_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)

# Save processed gene data
gene_expression_data.to_csv(out_gene_data_file)

# Print shape before and after mapping to verify the transformation
print("Shape before mapping (probes):", gene_data.shape)
print("Shape after mapping (genes):", gene_expression_data.shape)
print("\nFirst few gene symbols and their expression values:")
print(gene_expression_data.head())
# First get clinical features
selected_clinical_df = geo_select_clinical_features(
    clinical_df=clinical_data, 
    trait=trait,
    trait_row=0,  # From trait identification in previous step
    convert_trait=lambda x: 1 if not pd.isna(x) else None,  # Initially mark all as patients
    age_row=1,    # From age identification in previous step
    convert_age=convert_age,
    gender_row=2, # From gender identification in previous step
    convert_gender=convert_gender
)

# Set first 10 samples as controls based on background info
sample_cols = selected_clinical_df.columns[:10]  # First 10 samples
selected_clinical_df.loc[trait, sample_cols] = 0

# 1. Normalize gene symbols
gene_expression_data = normalize_gene_symbols_in_index(gene_expression_data)
gene_expression_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_expression_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)
# 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)
# 1. Extract gene annotation data and observe identifiers 
# From previous outputs, we can see:
# - Gene expression data uses identifiers like 'A_19_P00315452'
# - Gene annotation data has matching IDs in the 'ID' column and gene symbols in 'GENE_SYMBOL'
gene_metadata = get_gene_annotation(soft_file)

# 2. Extract gene mapping from annotation data
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

# 3. Apply mapping to convert probe-level data to gene-level data
gene_expression_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)

# Save processed gene data
gene_expression_data.to_csv(out_gene_data_file)

# Print shape before and after mapping to verify the transformation
print("Shape before mapping (probes):", gene_data.shape)
print("Shape after mapping (genes):", gene_expression_data.shape)
print("\nFirst few gene symbols and their expression values:")
print(gene_expression_data.head())
# First extract clinical features with proper conversion functions
def convert_trait(x):
    if pd.isna(x):
        return None
    # All samples with tissue:monocytes are trait positive (RA patients) except first 10 which are controls
    return 1

def convert_age(x):
    if pd.isna(x):
        return None
    # Extract numeric value after 'age:'
    match = re.search(r'age:\s*(\d+)y', str(x))
    if match:
        return int(match.group(1))
    return None

def convert_gender(x):
    if pd.isna(x):
        return None
    # Convert after 'gender:'
    if 'female' in str(x).lower():
        return 0
    elif 'male' in str(x).lower():
        return 1
    return None

# Extract clinical features
selected_clinical_df = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=0,  # Using tissue row
    convert_trait=convert_trait,
    age_row=1,    # Age information is in row 1
    convert_age=convert_age,
    gender_row=2, # Gender information is in row 2
    convert_gender=convert_gender
)

# Set first 10 samples as controls based on background info
sample_cols = selected_clinical_df.columns[:10]  # First 10 samples
selected_clinical_df.loc[trait, sample_cols] = 0

# 1. Normalize gene symbols from previous gene mapping result
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, with 10 controls and 21 RA patients."
)

# 6. Save if usable
if is_usable:
    linked_data.to_csv(out_data_file)
# 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
# The series title and summary indicate gene expression data of monocytes
is_gene_available = True

# 2.1 Data Availability 
# For trait: While we know there are healthy controls and RA patients from the series design,
# the treatment information is not shown in the available sample characteristics preview
# So we cannot reliably extract trait information
trait_row = None

# Age is in Feature 1
age_row = 1

# Gender is in Feature 2
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Not needed since trait_row is None
    return None

def convert_age(x):
    if pd.isna(x):
        return None
    # Extract number before 'y'
    try:
        age = int(x.split(': ')[1].replace('y',''))
        return age
    except:
        return None

def convert_gender(x):
    if pd.isna(x):
        return None
    val = x.split(': ')[1].lower()
    if 'female' in val:
        return 0
    elif 'male' in val:
        return 1
    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
# Skip since trait_row is None
# Request to see sample characteristics data first
print("Please provide previous output containing:")
print("1. The sample characteristics dictionary")
print("2. Background information about the dataset")
print("3. Any other relevant metadata")
# Set availability flag for gene expression data based on series type
is_gene_available = False  # Only miRNA data based on previous output shown

# Define row indices and conversion functions for clinical features
trait_row = None  # No disease status/RA information found in sample characteristics
age_row = None  # Age information not provided
gender_row = None # Gender information not provided

def convert_trait(x: str) -> int:
    return None  # Not used since trait_row is None

def convert_age(x: str) -> float:
    return None  # Not used since age_row is None
    
def convert_gender(x: str) -> int: 
    return None  # Not used since gender_row is None

# Save initial filtering results
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)
)

# Skip clinical feature extraction since trait_row is None