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p3/preprocess/Aniridia/GSE137996.csv

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p3/preprocess/Aniridia/code/GSE137996.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Aniridia"
+cohort = "GSE137996"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Aniridia"
+in_cohort_dir = "../DATA/GEO/Aniridia/GSE137996"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Aniridia/GSE137996.csv"
+out_gene_data_file = "./output/preprocess/3/Aniridia/gene_data/GSE137996.csv"
+out_clinical_data_file = "./output/preprocess/3/Aniridia/clinical_data/GSE137996.csv"
+json_path = "./output/preprocess/3/Aniridia/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  # Series summary mentions mRNA analysis with microarrays
+
+# 2.1 Data Availability
+trait_row = 2  # Disease status in Feature 2
+age_row = 0    # Age data in Feature 0 
+gender_row = 1 # Gender data in Feature 1
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Binary: 0 for control, 1 for disease
+    if not isinstance(x, str):
+        return None
+    value = x.split(": ")[-1].lower()
+    if "aak" in value:
+        return 1
+    elif "control" in value:
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous
+    if not isinstance(x, str):
+        return None
+    try:
+        return float(x.split(": ")[-1])
+    except:
+        return None
+
+def convert_gender(x):
+    # Binary: 0 for female, 1 for male
+    if not isinstance(x, str):
+        return None
+    value = x.split(": ")[-1].lower()
+    if value in ['f', 'w']:  # 'w' likely means woman
+        return 0
+    elif value == 'm':
+        return 1
+    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. Clinical Feature Extraction
+if 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 selected features
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical_df))
+    
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# 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)
+# Based on the provided data, the gene identifiers are Agilent probe IDs (A_19_P format)
+# These are not standard human gene symbols and need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# 1. Looking at the gene identifiers in gene expression data (e.g., A_19_P00315452)
+# and in gene annotation data, 'ID' column has the same format
+# 'GENE_SYMBOL' column contains the gene symbols we want to map to
+
+# 2. Get mapping between probe IDs and gene symbols
+probe_to_gene_map = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, probe_to_gene_map)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_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=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Aniridia/code/GSE137997.py

@@ -0,0 +1,146 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Aniridia"
+cohort = "GSE137997"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Aniridia"
+in_cohort_dir = "../DATA/GEO/Aniridia/GSE137997"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Aniridia/GSE137997.csv"
+out_gene_data_file = "./output/preprocess/3/Aniridia/gene_data/GSE137997.csv"
+out_clinical_data_file = "./output/preprocess/3/Aniridia/clinical_data/GSE137997.csv"
+json_path = "./output/preprocess/3/Aniridia/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 the background info, this is an mRNA study, so gene expression data should be available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Trait (Aniridia) can be inferred from disease status (AAK vs control) in Feature 2
+trait_row = 2
+
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].strip().lower()
+    # AAK (aniridia-associated keratopathy) indicates aniridia
+    if value == 'aak':
+        return 1
+    elif value == 'healthy control':
+        return 0
+    return None
+
+# Age is available in Feature 0
+age_row = 0
+
+def convert_age(value):
+    if not isinstance(value, str):
+        return None
+    try:
+        age = int(value.split(': ')[-1])
+        return age
+    except:
+        return None
+
+# Gender is available in Feature 1
+gender_row = 1
+
+def convert_gender(value):
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].strip().lower()
+    if value in ['f', 'w']:  # 'w' likely means woman/weiblich(German)
+        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,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# 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])
+# Based on the identifiers having the format "hsa-miR-*" and "hsa-let-*", these are microRNA identifiers,  
+# not standard human gene symbols. They need to be mapped to their target genes.
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Print findings about dataset nature
+print("Dataset Analysis:")
+print("-" * 50)
+print("This dataset contains miRNA expression data (hsa-miR-* identifiers)")
+print("Standard gene mapping is not applicable for miRNA data")
+print("The dataset cannot be used for gene-level analysis without miRNA target information")
+print("-" * 50)
+
+# Set requires_gene_mapping to False since we cannot map miRNAs to genes
+requires_gene_mapping = False
+
+# Set is_gene_available to False since we don't have gene expression data
+is_gene_available = False
+
+# Save updated metadata about dataset usability
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    note="Dataset contains miRNA expression data instead of gene expression data"
+)

p3/preprocess/Aniridia/code/GSE204791.py

@@ -0,0 +1,199 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Aniridia"
+cohort = "GSE204791"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Aniridia"
+in_cohort_dir = "../DATA/GEO/Aniridia/GSE204791"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Aniridia/GSE204791.csv"
+out_gene_data_file = "./output/preprocess/3/Aniridia/gene_data/GSE204791.csv"
+out_clinical_data_file = "./output/preprocess/3/Aniridia/clinical_data/GSE204791.csv"
+json_path = "./output/preprocess/3/Aniridia/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  # Contains both mRNA and miRNA data according to background
+
+# 2. Data Availability and Type Conversion
+# 2.1 Row identifiers
+trait_row = 2  # 'disease' field indicates KC vs control status
+age_row = 0  # 'age' field 
+gender_row = 1  # 'gender' field
+
+# 2.2 Conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1].lower() if ': ' in value else value.lower()
+    if 'kc' in value:
+        return 1
+    elif 'control' in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1] if ': ' in value else value
+    try:
+        return float(value)
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1].upper() if ': ' in value else value.upper()
+    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,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:", preview)
+    clinical_features.to_csv(out_clinical_data_file)
+# 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)
+# Looking at the gene identifiers, they appear to be probe IDs 
+# (e.g. "(+)E1A_r60_1", "A_19_P00315452") rather than standard human gene symbols.
+# These identifiers come from a microarray platform and need to be mapped to gene symbols.
+
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'GENE_SYMBOL' column: Contains gene symbols")
+print("\nExample gene symbol value:")
+print(gene_annotation['GENE_SYMBOL'].iloc[0])
+# 1. Create gene mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 2. Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Preview the mapped gene expression data
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped 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)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_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=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Aniridia/code/TCGA.py

@@ -0,0 +1,24 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Aniridia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Aniridia/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Aniridia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Aniridia/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Aniridia/cohort_info.json"
+
+# No cohort in TCGA matches Aniridia (congenital absence of iris)
+# Mark trait as unavailable and skip further processing
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=False,
+    is_trait_available=False
+)

p3/preprocess/Aniridia/gene_data/GSE137996.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf53c745e3f06fd8ff5be2dff4ad6369afd7466c33cb6edb93709d6d5bc442f1
+size 10652200

p3/preprocess/Ankylosing_Spondylitis/GSE73754.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f082f896405560c4c0fa041b70732b9e2e6d67a97b56351c43e4792d8ae9a43
+size 15381007

p3/preprocess/Ankylosing_Spondylitis/clinical_data/GSE25101.csv

@@ -0,0 +1,2 @@
+,GSM616668,GSM616669,GSM616670,GSM616671,GSM616672,GSM616673,GSM616674,GSM616675,GSM616676,GSM616677,GSM616678,GSM616679,GSM616680,GSM616681,GSM616682,GSM616683,GSM616684,GSM616685,GSM616686,GSM616687,GSM616688,GSM616689,GSM616690,GSM616691,GSM616692,GSM616693,GSM616694,GSM616695,GSM616696,GSM616697,GSM616698,GSM616699
+Ankylosing_Spondylitis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Ankylosing_Spondylitis/clinical_data/GSE73754.csv

@@ -0,0 +1,4 @@
+,GSM1902130,GSM1902131,GSM1902132,GSM1902133,GSM1902134,GSM1902135,GSM1902136,GSM1902137,GSM1902138,GSM1902139,GSM1902140,GSM1902141,GSM1902142,GSM1902143,GSM1902144,GSM1902145,GSM1902146,GSM1902147,GSM1902148,GSM1902149,GSM1902150,GSM1902151,GSM1902152,GSM1902153,GSM1902154,GSM1902155,GSM1902156,GSM1902157,GSM1902158,GSM1902159,GSM1902160,GSM1902161,GSM1902162,GSM1902163,GSM1902164,GSM1902165,GSM1902166,GSM1902167,GSM1902168,GSM1902169,GSM1902170,GSM1902171,GSM1902172,GSM1902173,GSM1902174,GSM1902175,GSM1902176,GSM1902177,GSM1902178,GSM1902179,GSM1902180,GSM1902181,GSM1902182,GSM1902183,GSM1902184,GSM1902185,GSM1902186,GSM1902187,GSM1902188,GSM1902189,GSM1902190,GSM1902191,GSM1902192,GSM1902193,GSM1902194,GSM1902195,GSM1902196,GSM1902197,GSM1902198,GSM1902199,GSM1902200,GSM1902201
+Ankylosing_Spondylitis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,53.0,26.0,29.0,50.0,35.0,48.0,18.0,39.0,49.0,43.0,43.0,18.0,59.0,51.0,18.0,45.0,52.0,77.0,34.0,31.0,51.0,23.0,52.0,46.0,40.0,55.0,54.0,41.0,38.0,45.0,52.0,43.0,41.0,21.0,47.0,60.0,46.0,27.0,37.0,28.0,37.0,48.0,41.0,53.0,39.0,18.0,50.0,22.0,48.0,57.0,23.0,56.0,28.0,26.0,65.0,41.0,32.0,56.0,47.0,71.0,24.0,24.0,27.0,37.0,42.0,63.0,61.0,20.0,31.0,25.0,29.0,65.0
+Gender,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Ankylosing_Spondylitis/code/GSE25101.py

@@ -0,0 +1,179 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ankylosing_Spondylitis"
+cohort = "GSE25101"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Ankylosing_Spondylitis"
+in_cohort_dir = "../DATA/GEO/Ankylosing_Spondylitis/GSE25101"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/GSE25101.csv"
+out_gene_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/gene_data/GSE25101.csv"
+out_clinical_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/clinical_data/GSE25101.csv"
+json_path = "./output/preprocess/3/Ankylosing_Spondylitis/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
+# Dataset uses Illumina HT-12 Whole-Genome Expression BeadChips 
+# and measures whole blood gene expression
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait data in row 2 - disease status
+trait_row = 2
+# Age and gender not available in characteristics but mentioned as matched in design
+age_row = None 
+gender_row = None
+
+# 2.2 Convert functions
+def convert_trait(value):
+    """Convert disease status to binary"""
+    if 'disease status:' in value:
+        if 'Ankylosing spondylitis patient' in value:
+            return 1
+        elif 'Normal control' in value:
+            return 0
+    return None
+
+convert_age = None
+convert_gender = 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. Extract clinical features
+clinical_features = 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
+print(preview_df(clinical_features))
+
+# Save clinical data
+clinical_features.to_csv(out_clinical_data_file)
+# 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)
+# Based on the gene identifiers starting with "ILMN_", these are Illumina probe IDs
+# which need to be mapped to HUGO gene symbols for interpretability
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# Extract gene mapping from annotation data
+# 'ID' column in annotation matches ILMN_* identifiers in expression data
+# 'Symbol' column contains the target gene symbols
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Preview results
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped 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)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_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=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Ankylosing_Spondylitis/code/GSE73754.py

@@ -0,0 +1,193 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ankylosing_Spondylitis"
+cohort = "GSE73754"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Ankylosing_Spondylitis"
+in_cohort_dir = "../DATA/GEO/Ankylosing_Spondylitis/GSE73754"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/GSE73754.csv"
+out_gene_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/gene_data/GSE73754.csv"
+out_clinical_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/clinical_data/GSE73754.csv"
+json_path = "./output/preprocess/3/Ankylosing_Spondylitis/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 check
+is_gene_available = True  # Based on series title and background, this is gene expression data
+
+# 2.1 Data availability check
+trait_row = 3  # 'disease' field contains trait info
+age_row = 1    # 'age (yr)' field contains age info
+gender_row = 0  # 'Sex' field contains gender info
+
+# 2.2 Data type conversion functions
+def convert_trait(value: str) -> int:
+    """Convert trait value to binary: 1 for AS, 0 for healthy control"""
+    if not value or 'disease:' not in value:
+        return None
+    value = value.split('disease:')[1].strip().lower()
+    if 'ankylosing spondylitis' in value:
+        return 1
+    elif 'healthy control' in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age value to continuous numeric"""
+    if not value or 'age (yr):' not in value:
+        return None
+    try:
+        return float(value.split('age (yr):')[1].strip())
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary: 1 for male, 0 for female"""
+    if not value or 'Sex:' not in value:
+        return None
+    value = value.split('Sex:')[1].strip().lower()
+    if 'male' in value:
+        return 1
+    elif 'female' in value:
+        return 0
+    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. Extract clinical features if trait data available
+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,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:", preview)
+    
+    # Save clinical data
+    clinical_features.to_csv(out_clinical_data_file)
+# 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 "ILMN_" indicate these are Illumina probe IDs
+# They need to be mapped to standard human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# Get gene mapping from probe IDs to gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Convert probe measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Normalize gene symbols to ensure consistency 
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_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=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Ankylosing_Spondylitis/code/TCGA.py

@@ -0,0 +1,27 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ankylosing_Spondylitis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Ankylosing_Spondylitis/cohort_info.json"
+
+# 1. Review subdirectories 
+# No cohort in TCGA matches or overlaps with Ankylosing Spondylitis (AS), which is an 
+# autoimmune condition rather than a cancer type. The available cancer cohorts are not 
+# relevant for studying this inflammatory arthritis condition.
+
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=False,
+    is_trait_available=False
+)

p3/preprocess/Ankylosing_Spondylitis/cohort_info.json

@@ -0,0 +1 @@
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p3/preprocess/Ankylosing_Spondylitis/gene_data/GSE73754.csv

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p3/preprocess/Anorexia_Nervosa/GSE60190.csv

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p3/preprocess/Anorexia_Nervosa/code/GSE60190.py

@@ -0,0 +1,191 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anorexia_Nervosa"
+cohort = "GSE60190"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Anorexia_Nervosa"
+in_cohort_dir = "../DATA/GEO/Anorexia_Nervosa/GSE60190"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Anorexia_Nervosa/GSE60190.csv"
+out_gene_data_file = "./output/preprocess/3/Anorexia_Nervosa/gene_data/GSE60190.csv"
+out_clinical_data_file = "./output/preprocess/3/Anorexia_Nervosa/clinical_data/GSE60190.csv"
+json_path = "./output/preprocess/3/Anorexia_Nervosa/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  # Yes - The dataset uses Illumina HumanHT-12 v3 microarray for gene expression
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 1  # 'ocd' field shows ED vs Control cases
+age_row = 5    # 'age' field available
+gender_row = 7  # 'Sex' field available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert ED/Control/OCD to binary values"""
+    if not x or ':' not in x:
+        return None
+    val = x.split(': ')[1].strip()
+    if val == 'ED':
+        return 1
+    elif val == 'Control':
+        return 0
+    return None
+
+def convert_age(x):
+    """Convert age strings to float values"""
+    if not x or ':' not in x:
+        return None
+    try:
+        return float(x.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(x):
+    """Convert gender strings to binary (F=0, M=1)"""
+    if not x or ':' not in x:
+        return None
+    val = x.split(': ')[1].strip()
+    if val == 'F':
+        return 0
+    elif val == 'M':
+        return 1
+    return None
+
+# 3. Save Metadata for Initial Filtering
+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:
+    selected_clinical = 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
+    print("Preview of extracted clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save clinical data
+    selected_clinical.to_csv(out_clinical_data_file)
+# 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)
+# These appear to be Illumina probe IDs (ILMN_) rather than gene symbols
+# They require mapping to official human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# Get mapping between gene identifiers and gene symbols
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply the mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Preview the result
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene data:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_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=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Anorexia_Nervosa/code/TCGA.py

@@ -0,0 +1,27 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anorexia_Nervosa"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Anorexia_Nervosa/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Anorexia_Nervosa/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Anorexia_Nervosa/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Anorexia_Nervosa/cohort_info.json"
+
+# Review the provided TCGA subdirectories
+# No suitable TCGA cohort found for Anorexia Nervosa as TCGA only contains cancer-related 
+# datasets, while Anorexia Nervosa is an eating disorder. None of the available cancer 
+# cohorts are relevant for studying this psychiatric/metabolic condition.
+
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=False, 
+    is_trait_available=False
+)

p3/preprocess/Anorexia_Nervosa/cohort_info.json

@@ -0,0 +1 @@
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p3/preprocess/Anorexia_Nervosa/gene_data/GSE60190.csv

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+size 30959101

p3/preprocess/Anxiety_disorder/GSE60491.csv

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p3/preprocess/Anxiety_disorder/GSE68526.csv

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p3/preprocess/Anxiety_disorder/GSE94119.csv

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p3/preprocess/Anxiety_disorder/clinical_data/GSE60190.csv

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p3/preprocess/Anxiety_disorder/clinical_data/GSE60491.csv

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p3/preprocess/Anxiety_disorder/clinical_data/GSE61672.csv

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p3/preprocess/Anxiety_disorder/clinical_data/GSE68526.csv

@@ -0,0 +1,4 @@
+,GSM1674313,GSM1674314,GSM1674315,GSM1674316,GSM1674317,GSM1674318,GSM1674319,GSM1674320,GSM1674321,GSM1674322,GSM1674323,GSM1674324,GSM1674325,GSM1674326,GSM1674327,GSM1674328,GSM1674329,GSM1674330,GSM1674331,GSM1674332,GSM1674333,GSM1674334,GSM1674335,GSM1674336,GSM1674337,GSM1674338,GSM1674339,GSM1674340,GSM1674341,GSM1674342,GSM1674343,GSM1674344,GSM1674345,GSM1674346,GSM1674347,GSM1674348,GSM1674349,GSM1674350,GSM1674351,GSM1674352,GSM1674353,GSM1674354,GSM1674355,GSM1674356,GSM1674357,GSM1674358,GSM1674359,GSM1674360,GSM1674361,GSM1674362,GSM1674363,GSM1674364,GSM1674365,GSM1674366,GSM1674367,GSM1674368,GSM1674369,GSM1674370,GSM1674371,GSM1674372,GSM1674373,GSM1674374,GSM1674375,GSM1674376,GSM1674377,GSM1674378,GSM1674379,GSM1674380,GSM1674381,GSM1674382,GSM1674383,GSM1674384,GSM1674385,GSM1674386,GSM1674387,GSM1674388,GSM1674389,GSM1674390,GSM1674391,GSM1674392,GSM1674393,GSM1674394,GSM1674395,GSM1674396,GSM1674397,GSM1674398,GSM1674399,GSM1674400,GSM1674401,GSM1674402,GSM1674403,GSM1674404,GSM1674405,GSM1674406,GSM1674407,GSM1674408,GSM1674409,GSM1674410,GSM1674411,GSM1674412,GSM1674413,GSM1674414,GSM1674415,GSM1674416,GSM1674417,GSM1674418,GSM1674419,GSM1674420,GSM1674421,GSM1674422,GSM1674423,GSM1674424,GSM1674425,GSM1674426,GSM1674427,GSM1674428,GSM1674429,GSM1674430,GSM1674431,GSM1674432,GSM1674433
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+Age,79.0,79.0,76.0,70.0,65.0,64.0,75.0,70.0,66.0,66.0,93.0,69.0,69.0,67.0,77.0,74.0,73.0,80.0,68.0,83.0,64.0,87.0,87.0,83.0,81.0,84.0,55.0,68.0,62.0,58.0,81.0,76.0,84.0,60.0,87.0,56.0,86.0,81.0,60.0,78.0,78.0,75.0,48.0,82.0,76.0,95.0,69.0,62.0,69.0,75.0,87.0,68.0,73.0,84.0,71.0,85.0,76.0,73.0,76.0,70.0,68.0,64.0,69.0,82.0,75.0,73.0,55.0,61.0,82.0,77.0,70.0,75.0,57.0,79.0,65.0,69.0,62.0,71.0,84.0,74.0,56.0,81.0,94.0,61.0,58.0,73.0,79.0,74.0,79.0,71.0,71.0,88.0,64.0,57.0,59.0,73.0,62.0,51.0,82.0,72.0,82.0,77.0,80.0,69.0,84.0,67.0,81.0,91.0,76.0,62.0,68.0,83.0,89.0,85.0,88.0,87.0,81.0,72.0,66.0,71.0,73.0
+Gender,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0

p3/preprocess/Anxiety_disorder/clinical_data/GSE78104.csv

@@ -0,0 +1,4 @@
+,GSM2067403,GSM2067404,GSM2067405,GSM2067406,GSM2067407,GSM2067408,GSM2067409,GSM2067410,GSM2067411,GSM2067412,GSM2067413,GSM2067414,GSM2067415,GSM2067416,GSM2067417,GSM2067418,GSM2067419,GSM2067420,GSM2067421,GSM2067422,GSM2067423,GSM2067424,GSM2067425,GSM2067426,GSM2067427,GSM2067428,GSM2067429,GSM2067430,GSM2067431,GSM2067432,GSM2067433,GSM2067434,GSM2067435,GSM2067436,GSM2067437,GSM2067438,GSM2067439,GSM2067440,GSM2067441,GSM2067442,GSM2067443,GSM2067444,GSM2067445,GSM2067446,GSM2067447,GSM2067448,GSM2067449,GSM2067450,GSM2067451,GSM2067452,GSM2067453,GSM2067454,GSM2067455,GSM2067456,GSM2067457,GSM2067458,GSM2067459,GSM2067460,GSM2067461,GSM2067462
+Anxiety_disorder,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,25.0,23.0,18.0,26.0,27.0,19.0,22.0,27.0,18.0,25.0,16.0,35.0,16.0,16.0,32.0,18.0,15.0,43.0,36.0,17.0,45.0,40.0,35.0,28.0,27.0,31.0,23.0,35.0,60.0,59.0,24.0,23.0,18.0,27.0,27.0,20.0,21.0,27.0,20.0,24.0,18.0,35.0,17.0,18.0,32.0,18.0,18.0,44.0,37.0,17.0,43.0,40.0,32.0,28.0,27.0,30.0,24.0,35.0,56.0,56.0
+Gender,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0

p3/preprocess/Anxiety_disorder/clinical_data/GSE94119.csv

@@ -0,0 +1,3 @@
+,GSM2469746,GSM2469747,GSM2469748,GSM2469749,GSM2469750,GSM2469751,GSM2469752,GSM2469753,GSM2469754,GSM2469755,GSM2469756,GSM2469757,GSM2469758,GSM2469759,GSM2469760,GSM2469761,GSM2469762,GSM2469763,GSM2469764,GSM2469765,GSM2469766,GSM2469767,GSM2469768,GSM2469769,GSM2469770,GSM2469771,GSM2469772,GSM2469773,GSM2469774,GSM2469775,GSM2469776,GSM2469777,GSM2469778,GSM2469779,GSM2469780,GSM2469781,GSM2469782,GSM2469783,GSM2469784,GSM2469785,GSM2469786,GSM2469787,GSM2469788,GSM2469789,GSM2469790,GSM2469791,GSM2469792,GSM2469793,GSM2469794,GSM2469795,GSM2469796,GSM2469797,GSM2469798,GSM2469799,GSM2469800,GSM2469801,GSM2469802,GSM2469803,GSM2469804,GSM2469805,GSM2469806,GSM2469807,GSM2469808,GSM2469809,GSM2469810,GSM2469811,GSM2469812,GSM2469813,GSM2469814,GSM2469815,GSM2469816,GSM2469817,GSM2469818,GSM2469819,GSM2469820,GSM2469821,GSM2469822,GSM2469823,GSM2469824,GSM2469825,GSM2469826,GSM2469827,GSM2469828,GSM2469829,GSM2469830,GSM2469831,GSM2469832,GSM2469833,GSM2469834,GSM2469835,GSM2469836,GSM2469837,GSM2469838,GSM2469839,GSM2469840,GSM2469841,GSM2469842,GSM2469843,GSM2469844,GSM2469845,GSM2469846,GSM2469847,GSM2469848,GSM2469849,GSM2469850,GSM2469851,GSM2469852,GSM2469853,GSM2469854,GSM2469855,GSM2469856,GSM2469857,GSM2469858,GSM2469859,GSM2469860,GSM2469861,GSM2469862,GSM2469863,GSM2469864,GSM2469865,GSM2469866,GSM2469867,GSM2469868,GSM2469869,GSM2469870,GSM2469871,GSM2469872,GSM2469873,GSM2469874,GSM2469875,GSM2469876,GSM2469877,GSM2469878,GSM2469879,GSM2469880,GSM2469881,GSM2469882,GSM2469883,GSM2469884,GSM2469885,GSM2469886,GSM2469887,GSM2469888,GSM2469889,GSM2469890,GSM2469891,GSM2469892,GSM2469893,GSM2469894,GSM2469895,GSM2469896,GSM2469897,GSM2469898,GSM2469899,GSM2469900,GSM2469901,GSM2469902,GSM2469903,GSM2469904,GSM2469905,GSM2469906,GSM2469907,GSM2469908,GSM2469909,GSM2469910,GSM2469911,GSM2469912,GSM2469913,GSM2469914,GSM2469915,GSM2469916,GSM2469917,GSM2469918,GSM2469919,GSM2469920,GSM2469921,GSM2469922,GSM2469923,GSM2469924,GSM2469925,GSM2469926,GSM2469927,GSM2469928,GSM2469929,GSM2469930,GSM2469931,GSM2469932,GSM2469933,GSM2469934,GSM2469935,GSM2469936,GSM2469937,GSM2469938,GSM2469939,GSM2469940,GSM2469941,GSM2469942,GSM2469943,GSM2469944,GSM2469945,GSM2469946,GSM2469947,GSM2469948,GSM2469949,GSM2469950,GSM2469951,GSM2469952,GSM2469953,GSM2469954,GSM2469955,GSM2469956,GSM2469957,GSM2469958,GSM2469959,GSM2469960,GSM2469961,GSM2469962,GSM2469963,GSM2469964,GSM2469965,GSM2469966,GSM2469967,GSM2469968,GSM2469969,GSM2469970,GSM2469971,GSM2469972,GSM2469973,GSM2469974,GSM2469975,GSM2469976,GSM2469977,GSM2469978,GSM2469979,GSM2469980,GSM2469981,GSM2469982,GSM2469983,GSM2469984,GSM2469985,GSM2469986,GSM2469987,GSM2469988,GSM2469989,GSM2469990,GSM2469991,GSM2469992,GSM2469993,GSM2469994,GSM2469995,GSM2469996,GSM2469997,GSM2469998,GSM2469999,GSM2470000,GSM2470001,GSM2470002,GSM2470003,GSM2470004,GSM2470005,GSM2470006,GSM2470007,GSM2470008,GSM2470009,GSM2470010,GSM2470011,GSM2470012,GSM2470013,GSM2470014,GSM2470015,GSM2470016,GSM2470017,GSM2470018,GSM2470019,GSM2470020,GSM2470021,GSM2470022,GSM2470023,GSM2470024,GSM2470025,GSM2470026,GSM2470027,GSM2470028,GSM2470029,GSM2470030,GSM2470031,GSM2470032,GSM2470033,GSM2470034,GSM2470035,GSM2470036,GSM2470037,GSM2470038,GSM2470039,GSM2470040,GSM2470041,GSM2470042,GSM2470043,GSM2470044,GSM2470045,GSM2470046,GSM2470047,GSM2470048,GSM2470049,GSM2470050,GSM2470051,GSM2470052,GSM2470053,GSM2470054,GSM2470055,GSM2470056,GSM2470057,GSM2470058,GSM2470059,GSM2470060
+Anxiety_disorder,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0
+Gender,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0

p3/preprocess/Anxiety_disorder/code/GSE119995.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anxiety_disorder"
+cohort = "GSE119995"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Anxiety_disorder"
+in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE119995"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Anxiety_disorder/GSE119995.csv"
+out_gene_data_file = "./output/preprocess/3/Anxiety_disorder/gene_data/GSE119995.csv"
+out_clinical_data_file = "./output/preprocess/3/Anxiety_disorder/clinical_data/GSE119995.csv"
+json_path = "./output/preprocess/3/Anxiety_disorder/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
+# From series title and summary, this dataset contains mRNA expression data from blood plasma
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait: all samples have panic disorder (Feature 0), so not useful for case-control study
+trait_row = None 
+
+# Age: not available in sample characteristics
+age_row = None
+
+# Gender: available in Feature 2
+gender_row = 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Not used since trait_row is None
+    return None
+
+def convert_age(x):
+    # Not used since age_row is None
+    return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    val = x.split(': ')[1].lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        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
+# 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 gene IDs start with "ILMN_" which indicates these are Illumina probe IDs
+# They need to be mapped to human gene symbols for analysis
+requires_gene_mapping = True
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# 1. Observe the gene identifiers in both gene expression data and annotation:
+# Gene expression data uses 'ILMN_' probe IDs which is stored as 'ID' column in annotation
+# Gene symbols are stored in 'Symbol' column in annotation
+
+# 2. Get gene mapping dataframe
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# Since we already determined in Step 2 that there's no valid trait variation
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,  # All samples have same trait value
+    is_biased=True,
+    df=pd.DataFrame(),
+    note="Dataset lacks trait variation - all samples have panic disorder"
+)

p3/preprocess/Anxiety_disorder/code/GSE60190.py

@@ -0,0 +1,462 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anxiety_disorder"
+cohort = "GSE60190"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Anxiety_disorder"
+in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE60190"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Anxiety_disorder/GSE60190.csv"
+out_gene_data_file = "./output/preprocess/3/Anxiety_disorder/gene_data/GSE60190.csv"
+out_clinical_data_file = "./output/preprocess/3/Anxiety_disorder/clinical_data/GSE60190.csv"
+json_path = "./output/preprocess/3/Anxiety_disorder/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")
+```python
+# 1. Gene Expression Data Availability
+# Based on Series_summary, this dataset uses Illumina HumanHT-12 v3 microarray for gene expression measurement
+is_gene_available = True
+
+# 2.1 Data Availability & 2.2 Data Type Conversion
+# trait (anxiety) can be inferred from dx field
+trait_row = 3  
+
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    val = value.split(": ")[-1]
+    # Anxiety disorder can be comorbid with OCD, so consider OCD cases as anxiety cases
+    if val in ["OCD", "Tics"]:
+        return 1
+    elif val == "Control":
+        return 0
+    return None
+
+# age is available
+age_row = 5
+
+def convert_age(value):
+    if not isinstance(value, str):
+        return None
+    try:
+        return float(value.split(": ")[-1])
+    except:
+        return None
+
+# gender is available  
+gender_row = 7
+
+def convert_gender(value):
+    if not isinstance(value, str):
+        return None
+    val = value.split(": ")[-1]
+    if val == "F":
+        return 0
+    elif val == "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=True)
+
+# 4. Clinical Feature Extraction
+sample_characteristics = {
+    '0': ['rin: 7.4', 'rin: 8.6', 'rin: 7.8', 'rin: 8.2', 'rin: 8.5', 'rin: 8.3', 'rin: 8.1', 'rin: 8.8', 'rin: 8.7', 'rin: 7.5', 'rin: 9', 'rin: 7.1', 'rin: 7.2', 'rin: 7.7', 'rin: 8.9', 'rin: 6.7', 'rin: 6', 'rin: 8.4', 'rin: 7.3', 'rin: 8', 'rin: 9.1', 'rin: 7.9', 'rin: 9.7', 'rin: 9.2', 'rin: 6.5', 'rin: 7', 'rin: 7.6', 'rin: 6.6', 'rin: 5.4', 'rin: 5.6'],
+    '1': ['ocd: ED', 'ocd: Control', 'ocd: OCD'],
+    '2': ['rinmatched: 1', 'rinmatched: 0'],
+    '3': ['dx: Bipolar', 'dx: Control', 'dx: MDD', 'dx: Tics', 'dx: OCD', 'dx: ED'],
+    '4': ['ph: 6.18', 'ph: 6.59', 'ph: 6.37', 'ph: 6.6', 'ph: 6.38', 'ph: 6.02', 'ph: 6.87', 'ph: 6.95', 'ph: 6.82', 'ph: 6.27', 'ph: 6.53', 'ph: 6.55', 'ph: 6', 'ph: 6.13', 'ph: 6.08', 'ph: 6.29', 'ph: 6.98', 'ph: 5.91', 'ph: 6.06', 'ph: 6.9', 'ph: 6.83', 'ph: 6.36', 'ph: 6.84', 'ph: 6.74', 'ph: 6.28', 'ph: 6.49', 'ph: 6.7', 'ph: 6.63', 'ph: 6.48', 'ph: 6.62'],
+    '5': ['age: 50.421917', 'age: 27.49863', 'age: 30.627397', 'age: 61.167123', 'age: 32.69589', 'age: 39.213698', 'age: 58.605479', 'age: 49.2', 'age: 41.041095', 'age: 51.750684', 'age: 50.89863', 'age: 26.745205', 'age: 29.104109', 'age: 39.301369', 'age: 48.978082', 'age: 57.884931', 'age: 28.364383', 'age: 24.041095', 'age: 19.268493', 'age: 27.230136', 'age: 46.605479', 'age: 23.443835', 'age: 51.038356', 'age: 39.663013', 'age: 46.109589', 'age: 77.989041', 'age: 46.967123', 'age: 63.241095', 'age: 62.306849', 'age: 83.641095'],
+    '6': ['pmi: 27', 'pmi: 19.5', 'pmi: 71.5', 'pmi: 22.5', 'pmi: 64', 'pmi: 28', 'pmi: 18', 'pmi: 29', 'pmi: 49', 'pmi: 13', 'pmi: 26.5', 'pmi: 16.5', 'pmi: 35', 'pmi: 19', 'pmi: 20.5', 'pmi: 9.5', 'pmi: 65.5', 'pmi: 68', 'pmi: 17.5', 'pmi: 44', 'pmi: 34', 'pmi: 21.5', 'pmi: 67.5', 'pmi: 26', 'pmi: 46.5', 'pmi: 33.5', 'pmi: 24.5', 'pmi: 30.5', 'pmi: 29.5', 'pmi: 51.5'],
+    '7': ['Sex: F', 'Sex: M'],
+    '8': ['race: CAUC'],
+    '9': ['batch1: 16', 'batch1: 18', 'batch1: 19', 'batch1: 20', 'batch1: 21', 'batch1: 9', 'batch1: 10', 'batch1: 12', 'batch1: 14', 'batch1: 23', 'batch1: 24', 'batch1: 25', 'batch1: 26', 'batch1: 27', 'batch1: 29', 'batch1: 33', 'batch1: 32', 'batch1: 31', 'batch1: 36', 'batch1: 37', 'batch1: 38', 'batch1: 39', 'batch1: 40', 'batch1: 41', 'batch1: 42', 'batch1: 44', 'batch1: 45', 'batch1: 48', 'batch1: 53', 'batch1: 59']
+}
+
+clinical_data = pd.DataFrame(sample_characteristics)
+
+selected_clinical_df = geo_select_clinical_features(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_
+print("Step 3 cannot be implemented without the output from the previous step that contains:")
+print("1. Sample characteristics dictionary")
+print("2. Background information about the dataset")
+print("3. Preview of the clinical data")
+print("\nPlease provide this information to proceed with proper data analysis.")
+# 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 IDs start with "ILMN_" which indicates these are Illumina probe IDs
+# These need to be mapped to official human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# 1. Identify mapping columns:
+# 'ID' in annotation matches the probe IDs like 'ILMN_1343291' in gene expression data
+# 'Symbol' contains the gene symbols we want to map to
+
+# 2. Extract mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Convert probe measurements to gene expression data 
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Inspect the result
+print("Mapped gene expression data shape:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+print("\nFirst 20 mapped gene symbols:")
+print(gene_data.index[:20])
+# Re-run gene mapping to restore gene_data
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+gene_data = apply_gene_mapping(expression_df, mapping_df) 
+
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# Get clinical features from sample characteristics
+trait_row = 3  # From 'dx' field
+
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    val = value.split(": ")[-1]
+    # Anxiety disorder can be comorbid with OCD, so consider OCD cases as anxiety cases
+    if val in ["OCD", "Tics"]:
+        return 1
+    elif val == "Control":
+        return 0
+    return None
+
+age_row = 5
+
+def convert_age(value):
+    if not isinstance(value, str):
+        return None
+    try:
+        return float(value.split(": ")[-1])
+    except:
+        return None
+
+gender_row = 7
+
+def convert_gender(value):
+    if not isinstance(value, str):
+        return None
+    val = value.split(": ")[-1]
+    if val == "F":
+        return 0
+    elif val == "M":
+        return 1
+    return None
+
+# Extract clinical features and save
+clinical_df = geo_select_clinical_features(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)
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_df.to_csv(out_clinical_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. Determine if features are biased
+is_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=is_trait_biased,
+    df=linked_data,
+    note="Gene expression data successfully mapped and linked with clinical features"
+)
+
+# 6. Save linked data only if usable AND trait is not biased
+if is_usable and not is_trait_biased:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get genetic data and map to genes
+gene_data = get_genetic_data(matrix_file)
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 1. Normalize gene symbols and save
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_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=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data if usable
+    if is_usable and not is_trait_biased:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )
+# 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)
+# 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
+# Yes, based on the background info showing Illumina HumanHT-12 microarray data
+is_gene_available = True
+
+# 2.1 Data Availability & 2.2 Data Type Conversion
+# Trait: Available in feature 1 'ocd' with values like 'ED', 'Control', 'OCD'
+trait_row = 1
+def convert_trait(value):
+    # Since we're looking for anxiety disorder, OCD patients are the cases
+    if not value or ':' not in value:
+        return None
+    val = value.split(':')[1].strip()
+    if val == 'OCD':
+        return 1
+    elif val == 'Control':
+        return 0
+    return None  # Other values like 'ED' are not relevant
+
+# Age: Available in feature 5
+age_row = 5
+def convert_age(value):
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(':')[1].strip())
+    except:
+        return None
+
+# Gender: Available in feature 7 'Sex'
+gender_row = 7
+def convert_gender(value):
+    if not value or ':' not in value:
+        return None
+    val = value.split(':')[1].strip()
+    if val == 'F':
+        return 0
+    elif val == 'M':
+        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 = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
+                                               age_row, convert_age,
+                                               gender_row, convert_gender)
+
+print("Preview of selected clinical features:")
+print(preview_df(selected_clinical))
+
+selected_clinical.to_csv(out_clinical_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_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=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )