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p3/preprocess/Schizophrenia/clinical_data/GSE120340.csv

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+,GSM3398477,GSM3398478,GSM3398479,GSM3398480,GSM3398481,GSM3398482,GSM3398483,GSM3398484,GSM3398485,GSM3398486,GSM3398487,GSM3398488,GSM3398489,GSM3398490,GSM3398491,GSM3398492,GSM3398493,GSM3398494,GSM3398495,GSM3398496,GSM3398497,GSM3398498,GSM3398499,GSM3398500,GSM3398501,GSM3398502,GSM3398503,GSM3398504,GSM3398505,GSM3398506
+Schizophrenia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,

p3/preprocess/Schizophrenia/clinical_data/GSE120342.csv

@@ -0,0 +1,2 @@
+,GSM3398507,GSM3398508,GSM3398509,GSM3398510,GSM3398511,GSM3398512,GSM3398513,GSM3398514,GSM3398515,GSM3398516,GSM3398517
+Schizophrenia,0.0,0.0,0.0,0.0,1.0,1.0,1.0,,,,

p3/preprocess/Schizophrenia/clinical_data/GSE145554.csv

@@ -0,0 +1,4 @@
+,GSM4321410,GSM4321411,GSM4321412,GSM4321413,GSM4321414,GSM4321415,GSM4321416,GSM4321417,GSM4321418,GSM4321419,GSM4321420,GSM4321421,GSM4321422,GSM4321423,GSM4321424,GSM4321425,GSM4321426,GSM4321427,GSM4321428,GSM4321429,GSM4321430,GSM4321431,GSM4321432,GSM4321433,GSM4321434,GSM4321435,GSM4321436,GSM4321437,GSM4321438,GSM4321439,GSM4321440,GSM4321441,GSM4321442,GSM4321443,GSM4321444,GSM4321445,GSM4321446,GSM4321447,GSM4321448,GSM4321449,GSM4321450,GSM4321451,GSM4321452
+Schizophrenia,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0
+Age,63.0,90.0,83.0,69.0,73.0,84.0,66.0,78.0,65.0,89.0,69.0,74.0,66.0,86.0,79.0,77.0,68.0,59.0,58.0,90.0,72.0,80.0,58.0,57.0,63.0,90.0,83.0,69.0,84.0,66.0,78.0,65.0,89.0,69.0,74.0,66.0,79.0,77.0,68.0,59.0,58.0,90.0,72.0
+Gender,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0

p3/preprocess/Schizophrenia/clinical_data/GSE161986.csv

@@ -0,0 +1,3 @@
+,GSM4929029,GSM4929030,GSM4929031,GSM4929032,GSM4929033,GSM4929034,GSM4929035,GSM4929036,GSM4929037,GSM4929038,GSM4929039,GSM4929040,GSM4929041,GSM4929042,GSM4929043,GSM4929044,GSM4929045,GSM4929046,GSM4929047,GSM4929048,GSM4929049,GSM4929050,GSM4929051,GSM4929052,GSM4929053,GSM4929054,GSM4929055,GSM4929056,GSM4929057,GSM4929058,GSM4929059,GSM4929060,GSM4929061,GSM4929062,GSM4929063
+Schizophrenia,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0
+Age,61.0,44.0,62.0,56.0,63.0,42.0,46.0,56.0,52.0,43.0,59.0,56.0,54.0,46.0,39.0,73.0,56.0,50.0,63.0,50.0,50.0,51.0,64.0,55.0,55.0,47.0,50.0,55.0,53.0,82.0,64.0,73.0,73.0,57.0,59.0

p3/preprocess/Schizophrenia/clinical_data/GSE165813.csv

@@ -0,0 +1,2 @@
+,GSM5906434,GSM5906435,GSM5906436,GSM5906437,GSM5906438,GSM5906439,GSM5906440,GSM5906441,GSM5906442,GSM5906443,GSM5906444,GSM5906445,GSM5906446,GSM5906447,GSM5906448,GSM5906449,GSM5906450,GSM5906451,GSM5906452,GSM5906453,GSM5906454,GSM5906455,GSM5906456,GSM5906457,GSM5906458,GSM5906459,GSM5906460,GSM5906461,GSM5906462,GSM5906463,GSM5906464,GSM5906465,GSM5906466,GSM5906467,GSM5906468,GSM5906469,GSM5906470,GSM5906471,GSM5906472,GSM5906473,GSM5906474,GSM5906475,GSM5906476,GSM5906477,GSM5906478,GSM5906479,GSM5906480,GSM5906481
+Schizophrenia,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

p3/preprocess/Schizophrenia/clinical_data/GSE285666.csv

@@ -0,0 +1,2 @@
+,GSM8706502,GSM8706503,GSM8706504,GSM8706505,GSM8706506,GSM8706507,GSM8706508,GSM8706509,GSM8706510,GSM8706511,GSM8706512,GSM8706513,GSM8706514,GSM8706515,GSM8706516,GSM8706517,GSM8706518,GSM8706519,GSM8706520,GSM8706521,GSM8706522,GSM8706523,GSM8706524,GSM8706525,GSM8706526,GSM8706527,GSM8706528,GSM8706529,GSM8706530,GSM8706531,GSM8706532,GSM8706533,GSM8706534,GSM8706535,GSM8706536,GSM8706537,GSM8706538,GSM8706539,GSM8706540,GSM8706541,GSM8706542,GSM8706543,GSM8706544,GSM8706545,GSM8706546,GSM8706547,GSM8706548,GSM8706549,GSM8706550,GSM8706551,GSM8706552,GSM8706553
+Schizophrenia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Schizophrenia/code/GSE165813.py

@@ -0,0 +1,155 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Schizophrenia"
+cohort = "GSE165813"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Schizophrenia"
+in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE165813"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Schizophrenia/GSE165813.csv"
+out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE165813.csv"
+out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE165813.csv"
+json_path = "./output/preprocess/3/Schizophrenia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes - Affymetrix array data indicated in title
+is_gene_available = True
+
+# 2. Variable Availability and Data Type
+# Trait (Status) - Not directly available in characteristics
+# But can be inferred from histological type - fetal/adult brain samples are controls, others are cases
+trait_row = 1 
+
+# Age/Gender not available
+age_row = None
+gender_row = None
+
+# Conversion Functions
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].lower().strip()
+    # Control samples are normal brain tissues
+    if any(x in value for x in ['fetal', 'adult', 'pediatric']):
+        return 0  # Control
+    # Tumor samples
+    elif any(x in value for x in ['astroblastoma', 'ptpr', 'pxa']):
+        return 1  # Case
+    return None
+
+def convert_age(value):
+    # Not used since age_row is None
+    return None
+
+def convert_gender(value):
+    # Not used since gender_row is None
+    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
+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
+    )
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+requires_gene_mapping = False
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(genetic_data)
+print("Gene data shape after normalization:", genetic_data.shape)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
+genetic_data.to_csv(out_gene_data_file)
+
+# Load clinical data previously processed
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+print("\nClinical data shape:", selected_clinical_df.shape)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+print("\nLinked data shape:", linked_data.shape)
+
+# 3. Handle missing values systematically  
+if trait in linked_data.columns:
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for bias in trait and demographic features
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and information saving
+    note = "This dataset contains gene expression data from brain tumor and normal brain samples, with schizophrenia status as the trait."
+    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=note
+    )
+
+    # 6. Save linked data only if usable and not biased
+    if is_usable and not trait_biased:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)
+else:
+    # Handle case where clinical features were not properly extracted
+    note = "Failed to extract clinical trait information from sample characteristics."
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path, 
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=None,
+        df=None,
+        note=note
+    )

p3/preprocess/Schizophrenia/code/GSE193818.py

@@ -0,0 +1,135 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Schizophrenia"
+cohort = "GSE193818"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Schizophrenia"
+in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE193818"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Schizophrenia/GSE193818.csv"
+out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE193818.csv"
+out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE193818.csv"
+json_path = "./output/preprocess/3/Schizophrenia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# From the Series description, this is RNA data from blood samples
+is_gene_available = True
+
+# 2.1 Data Availability
+# All patients have schizophrenia according to Series title/summary
+trait_row = None
+# Age available in row 1
+age_row = 1  
+# Gender available in row 0
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # All samples have schizophrenia, but this row is not available
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    try:
+        # Extract numeric value after colon
+        age = float(x.split(': ')[1])
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1].lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata 
+# is_trait_available is False since all samples have schizophrenia (constant)
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=False)
+
+# 4. Clinical Feature Extraction skipped since trait_row is None
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The IDs look like Affymetrix probe IDs (e.g., 'AFFX-BkGr-GC03_st')
+# These are not standard human gene symbols and will need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview column names and values from annotation dataframe
+print("Gene annotation DataFrame preview:")
+print(preview_df(gene_annotation))
+# The gene expression data uses probe IDs similar to 'AFFX-BkGr-GC03_st'
+# The gene annotation contains detailed information about genes in 'SPOT_ID.1'
+# We need to extract gene symbols from this field and pair them with IDs
+
+# Get mapping dataframe containing probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID.1')
+
+# Apply mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+
+print("Gene data shape:", gene_data.shape)
+print("\nFirst few genes and samples:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+print("Gene data shape after normalization:", gene_data.shape)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
+gene_data.to_csv(out_gene_data_file)
+
+# Mark dataset as unusable due to constant trait
+note = "All samples have schizophrenia (constant trait), making the dataset unsuitable for associational studies."
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=True,  # Since constant trait = 100% bias
+    df=gene_data,    # Gene data serves as the dataset
+    note=note
+)

p3/preprocess/Schizophrenia/code/GSE273630.py

@@ -0,0 +1,116 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Schizophrenia"
+cohort = "GSE273630"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Schizophrenia"
+in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE273630"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Schizophrenia/GSE273630.csv"
+out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE273630.csv"
+out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE273630.csv"
+json_path = "./output/preprocess/3/Schizophrenia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Based on the Series summary, this is a custom panel for dopamine-regulated inflammatory genes
+# So gene expression data should be available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (Schizophrenia) - Not available as participants are explicitly excluded if they have schizophrenia
+trait_row = None
+
+# Age - Not extractable from characteristics dict, but background info states all participants are 35-44 years old
+# Since age range is narrow and constant for all subjects, consider it not useful/available
+age_row = None
+
+# Gender - Not in characteristics dict, but background info states all participants are males
+# Since gender is constant for all subjects, consider it not useful/available
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Not used since trait data not available
+    return None
+
+def convert_age(x):
+    # Not used since age data not available
+    return None 
+
+def convert_gender(x):
+    # Not used since gender data not available
+    return None
+
+# 3. Save Metadata
+# Initial filtering - validate and save cohort info 
+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
+# Skip since trait_row is None, indicating clinical data is not available
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Looking at the gene IDs, they appear to be proper HGNC gene symbols
+# e.g. ABAT, ABL1, ACAA1 which are well-known human gene symbols
+
+requires_gene_mapping = False
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(genetic_data)
+print("Gene data shape after normalization:", genetic_data.shape)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
+genetic_data.to_csv(out_gene_data_file)
+
+# 2-4. Skip linking and missing value handling since no clinical data available
+
+# 5. Final validation - dataset cannot be used for schizophrenia study
+# Note that background info explicitly states schizophrenia patients were excluded
+note = "This dataset contains gene expression data but lacks information about schizophrenia status. The study explicitly excluded patients with schizophrenia in their exclusion criteria."
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=True,  # Biased since schizophrenia patients were explicitly excluded
+    df=genetic_data.T,  # Pass transposed gene expression data as samples should be in rows
+    note=note
+)
+
+# 6. Do not save linked data as the dataset is not usable for trait analysis

p3/preprocess/Schizophrenia/code/GSE285666.py

@@ -0,0 +1,164 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Schizophrenia"
+cohort = "GSE285666"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Schizophrenia"
+in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE285666"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Schizophrenia/GSE285666.csv"
+out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE285666.csv"
+out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE285666.csv"
+json_path = "./output/preprocess/3/Schizophrenia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# Gene Expression Data Availability
+# Yes, according to background info "Exon- and gene-Level transcriptional profiling" and "Expression analysis using Affymetrix Human Exon arrays"
+is_gene_available = True
+
+# Clinical Data Availability and Conversion
+trait_row = 0  # Found trait info in row 0 "disease state"
+age_row = None # Age not available
+gender_row = None # Gender not available
+
+def convert_trait(value: str) -> int:
+    """Convert disease state to binary - controls:0, patients:1"""
+    if pd.isna(value) or not isinstance(value, str):
+        return None
+    value = value.split(": ")[-1].lower()
+    if "unaffected" in value or "control" in value:
+        return 0
+    elif "patient" in value or "williams syndrome" in value:
+        return 1
+    return None
+
+# 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)
+
+# Extract clinical features since trait data is available
+clinical_df = geo_select_clinical_features(clinical_data,
+                                         trait=trait,
+                                         trait_row=trait_row,
+                                         convert_trait=convert_trait)
+
+# Preview the processed clinical data
+print("Preview of clinical data:")
+print(preview_df(clinical_df))
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# These appear to be numerical probe IDs, not human gene symbols
+# They will need to be mapped to gene symbols before analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview column names and values from annotation dataframe
+print("Gene annotation DataFrame preview:")
+print(preview_df(gene_annotation))
+# From observation: 'ID' in gene annotation matches gene identifiers in expression data
+# 'gene_assignment' contains gene symbols in the format "RefSeq // GeneSymbol // Description"
+
+# Get mapping between probes and genes 
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the mapped gene data
+print("Gene expression data preview after mapping:")
+print(gene_data.head())
+print("\nShape:", gene_data.shape)
+
+# Save gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+print("Gene data shape after normalization:", gene_data.shape)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
+gene_data.to_csv(out_gene_data_file)
+
+# Load clinical data previously processed
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+print("\nClinical data shape:", selected_clinical_df.shape)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+print("\nLinked data shape:", linked_data.shape)
+
+# 3. Handle missing values systematically  
+if trait in linked_data.columns:
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for bias in trait and demographic features
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and information saving
+    note = "This dataset contains gene expression data from myxoid liposarcoma samples, with metastasis status as the trait."
+    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=note
+    )
+
+    # 6. Save linked data only if usable and not biased
+    if is_usable and not trait_biased:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)
+else:
+    # Handle case where clinical features were not properly extracted
+    note = "Failed to extract clinical trait information from sample characteristics."
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path, 
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=None,
+        df=None,
+        note=note
+    )

p3/preprocess/Schizophrenia/code/TCGA.py

@@ -0,0 +1,28 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Schizophrenia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Schizophrenia/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Schizophrenia/cohort_info.json"
+
+# Since TCGA only contains cancer datasets, and schizophrenia is not a cancer,
+# we know there won't be suitable data - record unavailability and exit
+validate_and_save_cohort_info(
+    is_final=False, 
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=False,
+    is_trait_available=False
+)
+
+# Return empty dataframes to avoid errors in subsequent code
+clinical_df = pd.DataFrame()
+genetic_df = pd.DataFrame()

p3/preprocess/Schizophrenia/gene_data/GSE119289.csv

@@ -0,0 +1,6 @@
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+Gender,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0

p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE53441.csv

@@ -0,0 +1,2 @@
+,GSM1293592,GSM1293593,GSM1293594,GSM1293595,GSM1293596,GSM1293597,GSM1293598,GSM1293599,GSM1293600,GSM1293601,GSM1293602,GSM1293603,GSM1293604,GSM1293605,GSM1293606,GSM1293607,GSM1293608,GSM1293609,GSM1293610,GSM1293611,GSM1293612,GSM1293613,GSM1293614,GSM1293615,GSM1293616,GSM1293617,GSM1293618,GSM1293619,GSM1293620,GSM1293621,GSM1293622,GSM1293623,GSM1293624,GSM1293625
+Sickle_Cell_Anemia,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

p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84632.csv

@@ -0,0 +1,2 @@
+,GSM2243130,GSM2243131,GSM2243132,GSM2243133,GSM2243134,GSM2243135,GSM2243136,GSM2243137,GSM2243138,GSM2243139,GSM2243140,GSM2243141,GSM2243142,GSM2243143,GSM2243144,GSM2243145,GSM2243146,GSM2243147,GSM2243148,GSM2243149,GSM2243150,GSM2243151,GSM2243152,GSM2243153,GSM2243154,GSM2243155,GSM2243156,GSM2243157,GSM2243158,GSM2243159,GSM2243160,GSM2243161,GSM2243162,GSM2243163,GSM2243164,GSM2243165,GSM2243166,GSM2243167,GSM2243168,GSM2243169,GSM2243170,GSM2243171,GSM2243172,GSM2243173,GSM2243174,GSM2243175,GSM2243176,GSM2243177,GSM2243178,GSM2243179,GSM2243180,GSM2243181,GSM2243182,GSM2243183,GSM2243184,GSM2243185,GSM2243186,GSM2243187,GSM2243188,GSM2243189,GSM2243190,GSM2243191,GSM2243192,GSM2243193,GSM2243194,GSM2243195,GSM2243196,GSM2243197,GSM2243198,GSM2243199,GSM2243200,GSM2243201,GSM2243202,GSM2243203,GSM2243204,GSM2243205,GSM2243206,GSM2243207,GSM2243208,GSM2243209,GSM2243210,GSM2243211,GSM2243212,GSM2243213,GSM2243214,GSM2243215,GSM2243216,GSM2243217,GSM2243218,GSM2243219,GSM2243220,GSM2243221,GSM2243222,GSM2243223,GSM2243224,GSM2243225,GSM2243226,GSM2243227,GSM2243228,GSM2243229,GSM2243230,GSM2243231,GSM2243232,GSM2243233,GSM2243234,GSM2243235,GSM2243236,GSM2243237,GSM2243238,GSM2243239,GSM2243240,GSM2243241,GSM2243242,GSM2243243,GSM2243244,GSM2243245,GSM2243246,GSM2243247,GSM2243248,GSM2243249,GSM2243250,GSM2243251,GSM2243252,GSM2243253,GSM2243254,GSM2243255,GSM2243256,GSM2243257,GSM2243258,GSM2243259,GSM2243260,GSM2243261,GSM2243262,GSM2243263,GSM2243264,GSM2243265,GSM2243266,GSM2243267,GSM2243268,GSM2243269,GSM2243270,GSM2243271,GSM2243272,GSM2243273,GSM2243274,GSM2243275,GSM2243276,GSM2243277,GSM2243278,GSM2243279,GSM2243280,GSM2243281,GSM2243282,GSM2243283,GSM2243284,GSM2243285,GSM2243286,GSM2243287,GSM2243288,GSM2243289,GSM2243290,GSM2243291,GSM2243292,GSM2243293,GSM2243294,GSM2243295,GSM2243296,GSM2243297,GSM2243298,GSM2243299,GSM2243300,GSM2243301
+Sickle_Cell_Anemia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84633.csv

@@ -0,0 +1,2 @@
+,GSM2243302,GSM2243303,GSM2243304,GSM2243305,GSM2243306,GSM2243307,GSM2243308,GSM2243309,GSM2243310,GSM2243311,GSM2243312,GSM2243313,GSM2243314,GSM2243315,GSM2243316,GSM2243317,GSM2243318,GSM2243319,GSM2243320,GSM2243321,GSM2243322,GSM2243323,GSM2243324,GSM2243325,GSM2243326,GSM2243327,GSM2243328,GSM2243329,GSM2243330,GSM2243331,GSM2243332,GSM2243333,GSM2243334,GSM2243335,GSM2243336,GSM2243337,GSM2243338,GSM2243339,GSM2243340,GSM2243341
+Sickle_Cell_Anemia,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/Sickle_Cell_Anemia/clinical_data/GSE84634.csv

@@ -0,0 +1,2 @@
+,GSM2243346,GSM2243347,GSM2243348,GSM2243349,GSM2243350,GSM2243351,GSM2243352,GSM2243353,GSM2243354,GSM2243355,GSM2243356,GSM2243357,GSM2243358,GSM2243359,GSM2243360,GSM2243361,GSM2243362,GSM2243363,GSM2243364,GSM2243365,GSM2243366,GSM2243367,GSM2243368,GSM2243369,GSM2243370,GSM2243371,GSM2243372,GSM2243373,GSM2243374,GSM2243375,GSM2243376,GSM2243377,GSM2243378,GSM2243379,GSM2243380,GSM2243381,GSM2243382,GSM2243383
+Sickle_Cell_Anemia,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/Sickle_Cell_Anemia/code/GSE117613.py

@@ -0,0 +1,178 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sickle_Cell_Anemia"
+cohort = "GSE117613"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
+in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE117613"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE117613.csv"
+out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE117613.csv"
+out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE117613.csv"
+json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# From background info, we see the study used "Illumina HumanHT-12 v4 Expression BeadChip"
+# This indicates gene expression data is available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Find key numbers for variables
+# Diagnosis status in row 0 indicates trait info 
+trait_row = 0 
+
+# Age info in row 2
+age_row = 2
+
+# Sex/gender info in row 4
+gender_row = 4 
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert diagnosis status to binary trait indicator."""
+    if not value or 'diagnosis:' not in value:
+        return None
+    value = value.lower().split('diagnosis:')[1].strip()
+    # From background, we know this is a malaria study. Control group has no infection.
+    if 'no plasmodium falciparum infection' in value:
+        return 0
+    elif 'severe malaral anemia' in value or 'cerebral milaria' in value:
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age string to float years."""
+    if not value or 'age:' not in value:
+        return None
+    try:
+        return float(value.split('age:')[1].strip())
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert sex/gender string to binary (0=female, 1=male)."""
+    if not value or 'Sex:' not in value:
+        return None
+    value = value.lower().split('sex:')[1].strip()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        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. Extract clinical features if 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 data
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The IDs start with "ILMN_" which indicates these are Illumina probe IDs
+# These need to be mapped to human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview column names and values from annotation dataframe
+print("Gene annotation DataFrame preview:")
+print(preview_df(gene_annotation))
+# Get mapping dataframe with probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Convert probe-level data to gene-level expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols in gene expression data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+print("\nGene data shape (normalized gene-level):", gene_data.shape) 
+
+# Load clinical data previously processed
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+print("\nClinical data shape:", selected_clinical_df.shape)
+
+# 2. Link clinical and genetic data using normalized gene-level data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+print("\nLinked data shape:", linked_data.shape)
+
+# 3. Handle missing values systematically  
+if trait in linked_data.columns:
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for bias in trait and demographic features
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and information saving
+    note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
+    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=note
+    )
+
+    # 6. Save linked data only if usable and not biased
+    if is_usable and not trait_biased:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)

p3/preprocess/Sickle_Cell_Anemia/code/GSE41575.py

@@ -0,0 +1,66 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sickle_Cell_Anemia"
+cohort = "GSE41575"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
+in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE41575"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE41575.csv"
+out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE41575.csv"
+out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE41575.csv"
+json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# This is microRNA data, not gene expression data
+is_gene_available = False  
+
+# 2.1 Data Availability
+# No trait status data - only control vs miR overexpression
+trait_row = None  
+
+# No age data available
+age_row = None
+
+# No gender data available 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Not needed since trait data not available
+    return None
+
+def convert_age(x):
+    # Not needed since age data not available
+    return None
+
+def convert_gender(x):
+    # Not needed since gender data not available
+    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=False)
+
+# 4. Skip clinical feature extraction since trait_row is None

p3/preprocess/Sickle_Cell_Anemia/code/GSE46471.py

@@ -0,0 +1,130 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sickle_Cell_Anemia"
+cohort = "GSE46471"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
+in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE46471"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE46471.csv"
+out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE46471.csv"
+out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE46471.csv"
+json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, this is a microarray gene expression dataset studying endothelial cells
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# For this specific dataset studying damage pathways in endothelial cells,
+# all samples are control samples (no disease state), so trait data is not available
+trait_row = None 
+
+# No age data available in sample characteristics
+age_row = None
+
+# No gender data available in sample characteristics 
+gender_row = None
+
+# 2.2 Data Type Conversion
+def convert_trait(x):
+    # Not needed since trait data is not available
+    return None
+
+def convert_age(x):
+    # Not needed since age data is not available  
+    return None
+
+def convert_gender(x):
+    # Not needed since gender data is not available
+    return None
+
+# 3. Save Metadata
+# Initial filtering - since trait data is not available (trait_row is None), 
+# is_trait_available should be False
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=False
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step since trait_row is None, indicating clinical data is not available
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The gene identifiers in the data appear to be just numeric values (1,2,3...), 
+# which are not standard human gene symbols. We need to map these to proper gene symbols.
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview column names and values from annotation dataframe
+print("Gene annotation DataFrame preview:")
+print(preview_df(gene_annotation))
+# Extract gene identifiers and gene symbols from the annotation data
+id_col = 'ID'  # Gene identifiers in the data are numeric values (1,2,3...)
+gene_col = 'GENE_SYMBOL'  # Gene symbols are stored in GENE_SYMBOL column
+
+# Get mapping between gene identifiers and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, id_col, gene_col)
+
+# Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview the gene data after mapping
+print("\nGene expression data after mapping:")
+print(gene_data.shape)
+print("\nFirst few genes and their expression values:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols in gene expression data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+print("\nGene data shape (normalized gene-level):", gene_data.shape)
+
+# Save metadata indicating dataset is not usable due to lack of trait data
+note = "Dataset contains normalized gene expression data but lacks trait information."
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=False,
+    is_biased=True,  # Set to True since no trait data makes it unusable
+    df=gene_data,    # Provide the gene expression data
+    note=note
+)

p3/preprocess/Sickle_Cell_Anemia/code/GSE53441.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sickle_Cell_Anemia"
+cohort = "GSE53441"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
+in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE53441"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE53441.csv"
+out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE53441.csv"
+out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE53441.csv"
+json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# This dataset uses Affymetrix Human Genome U133 Plus 2.0 array for expression profiling
+# So it contains gene expression data
+is_gene_available = True
+
+# 2.1 Data Availability
+# For trait (SCA vs normal), data is in row 0
+trait_row = 0
+
+# No age data available
+age_row = None  
+
+# No gender data available 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    # Extract value after colon and remove whitespace
+    if ':' in value:
+        value = value.split(':')[1].strip().lower()
+    else:
+        value = value.strip().lower()
+        
+    if 'sickle cell' in value or 'sca' in value:
+        return 1
+    elif 'normal' in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    return None
+
+def convert_gender(value: str) -> int:
+    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
+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 processed clinical data
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save clinical features
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Based on the identifiers (e.g. '1007_s_at'), these are Affymetrix probe IDs 
+# from a microarray platform rather than standard human gene symbols.
+# They need to be mapped to gene symbols for analysis.
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview column names and values from annotation dataframe
+print("Gene annotation DataFrame preview:")
+print(preview_df(gene_annotation))
+# 1. In the gene expression data, the identifiers (e.g. '1007_s_at') are stored in 'ID' column of gene annotation
+# In gene annotation data, gene symbols are stored in 'Gene Symbol' column
+
+# 2. Get gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
+
+# 3. Apply mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+print("\nShape of processed gene data:", gene_data.shape)
+print("\nFirst 5 rows of mapped gene data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols in gene expression data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+print("\nGene data shape (normalized gene-level):", gene_data.shape) 
+
+# Load clinical data previously processed
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+print("\nClinical data shape:", selected_clinical_df.shape)
+
+# 2. Link clinical and genetic data using normalized gene-level data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+print("\nLinked data shape:", linked_data.shape)
+
+# 3. Handle missing values systematically  
+if trait in linked_data.columns:
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for bias in trait and demographic features
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and information saving
+    note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
+    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=note
+    )
+
+    # 6. Save linked data only if usable and not biased
+    if is_usable and not trait_biased:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)

p3/preprocess/Sickle_Cell_Anemia/code/GSE84632.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sickle_Cell_Anemia"
+cohort = "GSE84632"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
+in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE84632"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE84632.csv"
+out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE84632.csv"
+out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE84632.csv"
+json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # The title and summary indicate this is gene expression data from PBMCs
+
+# 2.1 Data Availability
+trait_row = 2  # Disease status in row 2
+age_row = None  # Age not provided
+gender_row = None  # Gender not provided
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert trait information to binary (0: Control, 1: Case)"""
+    if value is None or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'sickle cell disease' in value:
+        return 1
+    return None
+
+def convert_age(value):
+    """Convert age to float"""
+    return None  # Not used since age data not available
+
+def convert_gender(value): 
+    """Convert gender to binary (0: Female, 1: Male)"""
+    return None  # Not used since gender data not available
+
+# 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
+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 processed clinical data
+    print("Preview of processed clinical data:")
+    print(preview_df(selected_clinical))
+    
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The gene identifiers appear to be array probe IDs (16650001, etc) rather than human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview the dataframe by looking at column names and first few values
+print("Gene annotation columns and first few rows:")
+for col in gene_annotation.columns:
+    print(f"\n{col}:")
+    print(list(gene_annotation[col])[:5])
+# Step 1: Extract platform table data from SOFT file - look between !Platform_table_begin and !Platform_table_end
+platform_table = filter_content_by_prefix(
+    soft_file_path,
+    prefixes_a=['!Platform_table_begin', '!Platform_table_end'],
+    unselect=True,
+    source_type='file',
+    return_df_a=True
+)[0]
+
+# Look at column names to identify probe ID and gene symbol columns
+print("Platform table columns:")
+print(platform_table.columns)
+
+# Review some sample rows to confirm data format
+print("\nSample rows:")
+print(platform_table.head())
+
+# Create mapping using probe ID and gene symbol columns
+mapping_df = get_gene_mapping(platform_table, 'ID', 'gene_assignment')
+
+# Apply mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Normalize gene symbols to standardized format 
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+print("\nShape of gene expression data after mapping:")
+print(gene_data.shape)
+print("\nFirst few gene symbols:")
+print(list(gene_data.index)[:10])
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview column names and values from annotation dataframe
+print("Gene annotation DataFrame preview:")
+print(preview_df(gene_annotation))

p3/preprocess/Sickle_Cell_Anemia/code/GSE84633.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sickle_Cell_Anemia"
+cohort = "GSE84633"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
+in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE84633"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE84633.csv"
+out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE84633.csv"
+out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE84633.csv"
+json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data
+is_gene_available = True  # Study title indicates gene expression data from PBMCs
+
+# 2. Variable Availability and Conversion
+# Trait: Available from disease field
+trait_row = 2  # Key for disease status
+def convert_trait(val):
+    if val is None:
+        return None
+    val = val.split(': ')[-1].lower()
+    if 'sickle cell disease' in val:
+        return 1
+    return None
+
+# Age: Not available
+age_row = None 
+convert_age = None
+
+# Gender: Not available
+gender_row = None
+convert_gender = 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. Extract clinical features
+if trait_row is not None:
+    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
+    )
+    
+    print("Preview of clinical data:")
+    print(preview_df(clinical_df))
+    
+    # Save clinical data
+    clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The identifiers appear to be Illumina probe IDs starting with '23'
+# These are not human gene symbols and need to be mapped to official gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview column names and values from annotation dataframe
+print("Gene annotation DataFrame preview:")
+print(preview_df(gene_annotation))
+# Extract gene mapping info
+# 'ID' column matches probe IDs in expression data
+# 'gene_assignment' contains gene symbols in format "RefSeq // GeneSymbol // Description"
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Extract standard gene symbols
+mapping_df['Gene'] = mapping_df['Gene'].apply(lambda x: x.split('//')[1].strip() if isinstance(x, str) and '//' in x else None)
+
+# Apply mapping to convert probe-level data to gene-level data 
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the mapped gene data
+print("\nGene expression data after mapping:")
+print("Shape:", gene_data.shape)
+print("\nFirst few gene symbols:")
+print(list(gene_data.index)[:10])
+print("\nSample of expression values:")
+print(gene_data.head())
+# 1. Normalize gene symbols in gene expression data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+print("\nGene data shape (normalized gene-level):", gene_data.shape) 
+
+# Load clinical data previously processed
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+print("\nClinical data shape:", selected_clinical_df.shape)
+
+# 2. Link clinical and genetic data using normalized gene-level data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+print("\nLinked data shape:", linked_data.shape)
+
+# 3. Handle missing values systematically  
+if trait in linked_data.columns:
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for bias in trait and demographic features
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and information saving
+    note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
+    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=note
+    )
+
+    # 6. Save linked data only if usable and not biased
+    if is_usable and not trait_biased:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)

p3/preprocess/Sickle_Cell_Anemia/code/GSE84634.py

@@ -0,0 +1,164 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sickle_Cell_Anemia"
+cohort = "GSE84634"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
+in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE84634"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE84634.csv"
+out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE84634.csv"
+out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE84634.csv"
+json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Dataset is about gene expression in PBMCs, so gene data should be available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# trait_row = 2 since disease status is recorded in row 2
+# age and gender not recorded in characteristics 
+trait_row = 2
+age_row = None  
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Binary conversion - SCD patients are cases (1), non-SCD would be controls (0)
+    # But here all subjects are SCD patients based on background info
+    if x and 'sickle cell disease' in x.lower():
+        return 1
+    return None
+
+def convert_age(x):
+    # Not available
+    return None
+
+def convert_gender(x):
+    # Not available 
+    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
+if trait_row is not None:
+    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)
+    
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_df))
+    
+    # Save clinical data
+    clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Looking at the identifiers like '2315554', '2315633', these appear to be probe IDs
+# rather than human gene symbols. Gene symbols would typically be like 'BRCA1', 'TP53' etc.
+# Therefore these IDs need to be mapped to gene symbols.
+
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview column names and values from annotation dataframe
+print("Gene annotation DataFrame preview:")
+print(preview_df(gene_annotation))
+# 1. The 'ID' column in annotation matches probe IDs in expression data
+# The 'gene_assignment' column contains gene symbols in format "symbol // description"
+prob_col = 'ID'
+gene_col = 'gene_assignment'
+
+# 2. Get mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview result
+print("Gene expression data shape:", gene_data.shape)
+print("\nFirst few gene symbols:")
+print(list(gene_data.index)[:10])
+print("\nFirst few expression values:")
+print(gene_data.head())
+# 1. Normalize gene symbols in gene expression data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+print("\nGene data shape (normalized gene-level):", gene_data.shape) 
+
+# Load clinical data previously processed
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+print("\nClinical data shape:", selected_clinical_df.shape)
+
+# 2. Link clinical and genetic data using normalized gene-level data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+print("\nLinked data shape:", linked_data.shape)
+
+# 3. Handle missing values systematically  
+if trait in linked_data.columns:
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for bias in trait and demographic features
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and information saving
+    note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
+    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=note
+    )
+
+    # 6. Save linked data only if usable and not biased
+    if is_usable and not trait_biased:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)

p3/preprocess/Sickle_Cell_Anemia/code/TCGA.py

@@ -0,0 +1,114 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sickle_Cell_Anemia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json"
+
+# Select TCGA_Acute_Myeloid_Leukemia_(LAML) since it is the most relevant blood disorder dataset
+cohort_dir = os.path.join(tcga_root_dir, "TCGA_Acute_Myeloid_Leukemia_(LAML)")
+
+# Get file paths for clinical and genetic data 
+clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load the data files
+clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')
+
+# Print clinical columns for examination
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+# Define candidate columns
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
+candidate_gender_cols = ['gender']
+
+# Get file paths using proper directory structure 
+cohort_dir = tcga_root_dir
+clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+
+# Read and preview clinical data
+clinical_data = pd.read_csv(clinical_file_path, index_col=0)
+
+# Extract and preview age columns
+age_preview = {}
+for col in candidate_age_cols:
+    age_preview[col] = clinical_data[col].head(5).tolist()
+print("Age columns preview:")
+print(age_preview)
+
+# Extract and preview gender columns
+gender_preview = {}
+for col in candidate_gender_cols:
+    gender_preview[col] = clinical_data[col].head(5).tolist()
+print("\nGender columns preview:") 
+print(gender_preview)
+# Select TCGA_Acute_Myeloid_Leukemia_(LAML) since it is the most relevant blood disorder dataset
+cohort_dir = os.path.join(tcga_root_dir, "TCGA_Acute_Myeloid_Leukemia_(LAML)")
+
+# Get file paths for clinical and genetic data 
+clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load the data files
+clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')
+
+# Print clinical columns for examination
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+# Inspect previously given clinical data columns
+age_col = 'age_at_initial_pathologic_diagnosis'  # Clear age column exists
+gender_col = 'gender'  # Clear gender column exists
+
+# Print the chosen column names
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# Select appropriate demographic columns
+age_col = 'age_at_initial_pathologic_diagnosis'  # This is more directly usable than days_to_birth
+gender_col = 'gender'
+
+# 1. Extract and standardize clinical features
+selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)
+
+# 2. Normalize gene symbols in genetic data
+normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_genetic_df.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data
+linked_data = pd.merge(selected_clinical_df, normalized_genetic_df.T, left_index=True, right_index=True)
+
+# 4. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Check for bias in trait and demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Validate and save cohort info 
+note = f"Sample size after preprocessing: {len(linked_data)}. Number of genes: {len(linked_data.columns) - 3}"
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+
+# 7. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+    print(f"Linked data saved to {out_data_file}")
+    print("Shape of final linked data:", linked_data.shape)
+else:
+    print("Dataset was found to be unusable and was not saved")

p3/preprocess/Sickle_Cell_Anemia/cohort_info.json

@@ -0,0 +1 @@
+{"GSE84634": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 38, "note": "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."}, "GSE84633": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 40, "note": "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."}, "GSE84632": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE53441": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 34, "note": "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."}, "GSE46471": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset contains normalized gene expression data but lacks trait information."}, "GSE41575": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE117613": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 46, "note": "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 173, "note": "Sample size after preprocessing: 173. Number of genes: 19848"}}

p3/regress/Pancreatic_Cancer/significant_genes_condition_Age.json

@@ -0,0 +1,111 @@
+{
+    "significant_genes": {
+        "Variable": [
+            "5467_at",
+            "401491_at",
+            "23635_at",
+            "442892_at",
+            "3205_at",
+            "29105_at",
+            "1114_at",
+            "245973_at",
+            "440712_at",
+            "115024_at",
+            "100506928_at",
+            "155060_at",
+            "22973_at",
+            "8708_at",
+            "9667_at",
+            "150776_at",
+            "5367_at",
+            "55901_at",
+            "10542_at",
+            "79862_at",
+            "113263_at",
+            "473_at",
+            "283592_at",
+            "10610_at",
+            "10171_at",
+            "5774_at",
+            "1978_at",
+            "6424_at",
+            "51611_at"
+        ],
+        "Coefficient": [
+            0.08296500398213777,
+            0.060871951899577656,
+            -0.037004068545058215,
+            -0.024300515351375902,
+            0.024074971286214185,
+            -0.02380560079683399,
+            -0.019774367999922386,
+            -0.019746352429551865,
+            -0.016818036880240413,
+            -0.015753839024237853,
+            0.015497594529684055,
+            -0.01352678971151172,
+            0.010887439158759551,
+            0.010084862677183884,
+            0.009110368208898713,
+            -0.008882968528012446,
+            -0.00729881322514684,
+            -0.006556100278827447,
+            0.006300106656623096,
+            -0.0059048190127323754,
+            -0.005058805518017359,
+            0.004340670338782341,
+            -0.0037999858593521464,
+            -0.003327218193936484,
+            0.002549965198737279,
+            0.0022537958408732272,
+            0.001941535454615459,
+            -0.0018775331315336104,
+            -1.5214553851987895e-06
+        ],
+        "Absolute Coefficient": [
+            0.08296500398213777,
+            0.060871951899577656,
+            0.037004068545058215,
+            0.024300515351375902,
+            0.024074971286214185,
+            0.02380560079683399,
+            0.019774367999922386,
+            0.019746352429551865,
+            0.016818036880240413,
+            0.015753839024237853,
+            0.015497594529684055,
+            0.01352678971151172,
+            0.010887439158759551,
+            0.010084862677183884,
+            0.009110368208898713,
+            0.008882968528012446,
+            0.00729881322514684,
+            0.006556100278827447,
+            0.006300106656623096,
+            0.0059048190127323754,
+            0.005058805518017359,
+            0.004340670338782341,
+            0.0037999858593521464,
+            0.003327218193936484,
+            0.002549965198737279,
+            0.0022537958408732272,
+            0.001941535454615459,
+            0.0018775331315336104,
+            1.5214553851987895e-06
+        ]
+    },
+    "cv_performance": {
+        "prediction": {
+            "accuracy": 71.42857142857142,
+            "precision": 76.0,
+            "recall": 78.33333333333333,
+            "f1": 76.6883116883117
+        },
+        "selection": {
+            "precision": 0.0,
+            "recall": 0.0,
+            "f1": 0.0,
+            "jaccard": 0.0
+        }
+    }
+}

p3/regress/Pancreatic_Cancer/significant_genes_condition_Gender.json

@@ -0,0 +1,96 @@
+{
+    "significant_genes": {
+        "Variable": [
+            "138199_at",
+            "115024_at",
+            "23635_at",
+            "115703_at",
+            "406956_at",
+            "5774_at",
+            "23505_at",
+            "8361_at",
+            "1500_at",
+            "143098_at",
+            "150082_at",
+            "51368_at",
+            "10911_at",
+            "817_at",
+            "155060_at",
+            "5467_at",
+            "285601_at",
+            "4293_at",
+            "645954_at",
+            "1774_at",
+            "100996693_at",
+            "197350_at",
+            "255394_at",
+            "79862_at"
+        ],
+        "Coefficient": [
+            -0.12814223431629035,
+            -0.06079122461954943,
+            -0.05632570737209828,
+            -0.04218255131770321,
+            -0.0345225328004136,
+            0.031313267271787605,
+            0.030269610259361666,
+            -0.02334359219730641,
+            0.022727554199449447,
+            0.021383144328592774,
+            -0.013937924679236307,
+            -0.008978207621606983,
+            -0.006689693046658298,
+            -0.005792774421685688,
+            -0.005290015469026769,
+            0.004715321332233115,
+            -0.003190729502227764,
+            -0.0030606305203533715,
+            -0.0023339510321452924,
+            -0.0013360949730925522,
+            -0.001040517017234697,
+            -0.0005129381462236625,
+            0.0004843406390887012,
+            -0.00011179116287610881
+        ],
+        "Absolute Coefficient": [
+            0.12814223431629035,
+            0.06079122461954943,
+            0.05632570737209828,
+            0.04218255131770321,
+            0.0345225328004136,
+            0.031313267271787605,
+            0.030269610259361666,
+            0.02334359219730641,
+            0.022727554199449447,
+            0.021383144328592774,
+            0.013937924679236307,
+            0.008978207621606983,
+            0.006689693046658298,
+            0.005792774421685688,
+            0.005290015469026769,
+            0.004715321332233115,
+            0.003190729502227764,
+            0.0030606305203533715,
+            0.0023339510321452924,
+            0.0013360949730925522,
+            0.001040517017234697,
+            0.0005129381462236625,
+            0.0004843406390887012,
+            0.00011179116287610881
+        ]
+    },
+    "cv_performance": {
+        "prediction": {
+            "accuracy": 68.57142857142856,
+            "precision": 72.0,
+            "recall": 78.33333333333333,
+            "f1": 74.46608946608947
+        },
+        "selection": {
+            "precision": 0.0,
+            "recall": 0.0,
+            "f1": 0.0,
+            "jaccard": 0.0
+        }
+    }
+}

p3/regress/Pancreatic_Cancer/significant_genes_condition_Hypertension.json

@@ -0,0 +1,612 @@
+{
+    "significant_genes": {
+        "Variable": [
+            "ANGPTL7",
+            "SMIM12",
+            "TUBB1",
+            "IDI2-AS1",
+            "OR8B1P",
+            "TRIM51",
+            "XPR1",
+            "MIR106A",
+            "RNA5SP74",
+            "GHRL",
+            "HRH1",
+            "MIR1909",
+            "CACNA2D4",
+            "TSBP1",
+            "RAPSN",
+            "CD5L",
+            "EP300",
+            "CCL7",
+            "RFXAP",
+            "OR6Q1",
+            "LRFN5",
+            "OR8A1",
+            "UGT2B28",
+            "HNRNPA1P8",
+            "HMOX1",
+            "MIR146A",
+            "FRMD7",
+            "ALDH8A1",
+            "APLNR",
+            "RPS6P6",
+            "MTA1",
+            "MDS2",
+            "GSTM1",
+            "PLAU",
+            "MMAB",
+            "FADS1",
+            "CASP2",
+            "SLC25A35",
+            "LINC01602",
+            "PCDHB11",
+            "TP53I11",
+            "IKZF4",
+            "LRFN1",
+            "RAD21L1",
+            "SP3",
+            "CATSPER3",
+            "KRTAP13-3",
+            "METTL21EP",
+            "MRGPRX3",
+            "OR4A16",
+            "PFN4",
+            "MIR382",
+            "FBXO15",
+            "GPR148",
+            "NECAB1",
+            "NANOGNB",
+            "NHS",
+            "SSX7",
+            "CFL1P3",
+            "GLUD2",
+            "ANXA10",
+            "RNA5SP269",
+            "MAGEE1",
+            "OR10U1P",
+            "AMTN",
+            "OR4C46",
+            "GOT1L1",
+            "CSN3",
+            "REXO1",
+            "PNLDC1",
+            "COTL1P1",
+            "PRPS1L1",
+            "HCFC1R1",
+            "ZNF233",
+            "CREB3L4",
+            "RHBDL1",
+            "PSMB3P2",
+            "OR1N2",
+            "SURF6",
+            "SPARC",
+            "OR4H12P",
+            "LRIT3",
+            "INTS15",
+            "FAM223A",
+            "RNU6-68P",
+            "RASL11A",
+            "OR11G2",
+            "OR1J1",
+            "LYPD5",
+            "SPRR2D",
+            "UNC50",
+            "RNA5SP183",
+            "HUS1",
+            "DEFB118",
+            "DDX20",
+            "PEG10",
+            "ACTR5",
+            "PF4",
+            "RNA5SP340",
+            "CIBAR1P1",
+            "SMOC1",
+            "S100A11P2",
+            "CCDC121",
+            "KLHL42",
+            "TRPM8",
+            "PGA3",
+            "ARL13A",
+            "MIR100",
+            "ADGRG2",
+            "CHI3L2",
+            "SSU72P1",
+            "CIDECP1",
+            "ANKRD35",
+            "RNF187",
+            "C10orf90",
+            "YBX1P1",
+            "HSD3BP4",
+            "GDPD4",
+            "HSBP1P2",
+            "SNORA36C",
+            "LHCGR",
+            "IFI6",
+            "SPESP1",
+            "PITX1",
+            "OR1S2",
+            "OR51A6P",
+            "RPL7L1P8",
+            "RBM4B",
+            "WFDC3",
+            "OR5G3",
+            "MIR141",
+            "OR51B6",
+            "STX10",
+            "HMGN1P30",
+            "KLHL38",
+            "CRELD1",
+            "BOLA1",
+            "SCGB1D2",
+            "EPHA7",
+            "KCNB1",
+            "NPM1P8",
+            "PCDH8",
+            "CXCL14",
+            "OR1L1",
+            "RPL7L1P4",
+            "C1orf216",
+            "ANKRD34C",
+            "VXN",
+            "ATP6V1C2",
+            "NEFL",
+            "POSTN",
+            "SUMO1P1",
+            "SLC6A1",
+            "TAPBPL",
+            "RGS2",
+            "RAB39B",
+            "MIR1908",
+            "BTBD2",
+            "PIN1",
+            "STATH",
+            "OR6B1",
+            "GRIN2B",
+            "LDLRAP1",
+            "ALX1",
+            "INVS",
+            "GBP7",
+            "FAM149B1",
+            "CEBPA-DT",
+            "BCL6",
+            "SLC9A2",
+            "SNORD37",
+            "LIAT1",
+            "ZNF304",
+            "NXPE2",
+            "MIR153-2",
+            "GSTT2",
+            "STAC",
+            "NOX5",
+            "STAT1",
+            "RNF31",
+            "VSTM1",
+            "GFRA3",
+            "C7orf33",
+            "PARL",
+            "TBCCD1",
+            "RPL13",
+            "HPR",
+            "PLEKHF1",
+            "ZNF619",
+            "TRIM51G",
+            "OR13H1",
+            "SNORD48",
+            "SNORD68",
+            "GNB5",
+            "IRF9",
+            "FAM223B"
+        ],
+        "Coefficient": [
+            0.05493486252447115,
+            -0.04645607153814593,
+            -0.04488090363496572,
+            -0.044097343250001135,
+            -0.0402651350024962,
+            0.03905186703581911,
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+            0.019037668777721024,
+            -0.018948088192699483,
+            0.0188163741560647,
+            0.018319854091903127,
+            0.01820767494603523,
+            0.0181921702463617,
+            -0.018183253429765168,
+            0.01787443251591102,
+            0.01711397618917682,
+            -0.016911356517398092,
+            0.016668763183975935,
+            -0.016302493399681154,
+            -0.01620999151083638,
+            0.01589206649777551,
+            0.015150260389295359,
+            -0.014914400437968375,
+            -0.014759180742217885,
+            -0.014292354346388803,
+            -0.014268539573206639,
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+    }
+}

p3/regress/Pancreatic_Cancer/significant_genes_condition_None.json

@@ -0,0 +1,600 @@
+{
+    "significant_genes": {
+        "Variable": [
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+    }
+}

p3/regress/Pancreatic_Cancer/significant_genes_condition_Obesity.json

@@ -0,0 +1,642 @@
+{
+    "significant_genes": {
+        "Variable": [
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+            "ANGPTL7",
+            "MIR1909",
+            "PCDHB11",
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+            "SSU72L1",
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+            "NECAB1",
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+            "NANOGNB",
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+            "MIR100",
+            "GNPTG",
+            "PSMB3P2",
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+            "OR6Q1",
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+            "SLC25A35",
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+            "RPS2P45",
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+            "SSU72P6",
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+            "LCE1B",
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+            "GSTT2B",
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+        }
+    }
+}

p3/regress/Parkinsons_Disease/significant_genes_condition_Age.json

@@ -0,0 +1,1905 @@
+{
+    "significant_genes": {
+        "Variable": [
+            "ACO2",
+            "MON2",
+            "SATB1",
+            "WDCP",
+            "IL3RA",
+            "CYTIP",
+            "APOBEC3G",
+            "PORCN",
+            "CD99",
+            "ADGRE1",
+            "ARL11",
+            "MGRN1",
+            "SLC38A6",
+            "NIPA2",
+            "CALHM2",
+            "TMEM87A",
+            "CRLF3",
+            "NR1H2",
+            "ZNF717",
+            "ETFRF1",
+            "NUDCD3",
+            "TRIM27",
+            "TARS1",
+            "AP3M1",
+            "RPAIN",
+            "CYBB",
+            "ABTB3",
+            "GTF2H1",
+            "ZNF124",
+            "TANGO2",
+            "SNX20",
+            "SNORD38B",
+            "TNKS2",
+            "ZNF234",
+            "GTF2E1",
+            "PPM1F",
+            "KRTAP10-4",
+            "STAG3L4",
+            "CD36",
+            "RAF1",
+            "SETDB1",
+            "HRK",
+            "TCN2",
+            "RUNX2",
+            "LRRC25",
+            "IRX2",
+            "APOL3",
+            "PIK3R1",
+            "CUL5",
+            "R3HDM2",
+            "ZNF101",
+            "STRN",
+            "PDS5B",
+            "RPS23",
+            "NAA38",
+            "AKR1E2",
+            "SIX2",
+            "AMN",
+            "SDHD",
+            "TGM2",
+            "HSD17B10",
+            "RNASEK",
+            "PRICKLE1",
+            "ALCAM",
+            "BSCL2",
+            "CXCR3",
+            "RBPMS2",
+            "ZNF841",
+            "SH3KBP1",
+            "GOLPH3L",
+            "CDV3",
+            "CARD8",
+            "SLC45A2",
+            "ARL15",
+            "GCNT2",
+            "ZNF770",
+            "RTKN2",
+            "STAMBP",
+            "POLR1B",
+            "B3GNTL1",
+            "AP2B1",
+            "GPR89A",
+            "NLRP6",
+            "ZXDC",
+            "GRHPR",
+            "MARCKSL1",
+            "HOXB2",
+            "TNFRSF17",
+            "SAMD9L",
+            "BTF3",
+            "CEP170",
+            "SLC35E2B",
+            "SPTLC2",
+            "TOMM34",
+            "UHMK1",
+            "TOMM22",
+            "SLC35A4",
+            "HLA-DQA1",
+            "BLK",
+            "TGFBI",
+            "TAMM41",
+            "FKBP6",
+            "LAMTOR4",
+            "SLMAP",
+            "LIN7A",
+            "IGSF6",
+            "TMEM170A",
+            "KRTAP10-2",
+            "FAM135A",
+            "DCTN5",
+            "GOLGA8H",
+            "MARCHF1",
+            "EPHX2",
+            "ZNF154",
+            "CYBRD1",
+            "NCAPD2",
+            "MAPK8IP1",
+            "CDC25B",
+            "ABCA7",
+            "RPS9",
+            "IER2",
+            "STX8",
+            "SIDT1",
+            "TUBD1",
+            "FZD9",
+            "LRRC28",
+            "ZCCHC9",
+            "EBF1",
+            "DCP1B",
+            "CARMIL1",
+            "ECE1",
+            "CD244",
+            "SNORA62",
+            "SMIM24",
+            "POLR1HASP",
+            "GSDMB",
+            "TFAP2A",
+            "DPH6",
+            "APOD",
+            "HDC",
+            "PRPF18",
+            "PPP6C",
+            "ARHGAP12",
+            "HLTF",
+            "TVP23B",
+            "CPNE1",
+            "MRLN",
+            "AK5",
+            "PAPOLG",
+            "NFE2L1",
+            "EIF3J",
+            "PLD1",
+            "TCF24",
+            "ABI3",
+            "LDLR",
+            "CHSY1",
+            "DGCR6L",
+            "CAPG",
+            "FLJ42627",
+            "ELOVL7",
+            "WIPI1",
+            "ANPEP",
+            "XPO1",
+            "NADSYN1",
+            "SOCS1",
+            "ANXA2P2",
+            "PRMT2",
+            "ZNF532",
+            "KCNAB2",
+            "TRIM43",
+            "HMGA2",
+            "RNU4-1",
+            "LPIN2",
+            "TTLL3",
+            "DNASE2",
+            "AIM2",
+            "LTA4H",
+            "NT5DC1",
+            "VPS53",
+            "ZFP90",
+            "BHLHA9",
+            "RABGGTB",
+            "ZNF208",
+            "SCFD2",
+            "PGM5P2",
+            "INPP5B",
+            "RBM14",
+            "ZNF429",
+            "ERAP1",
+            "ID3",
+            "LCLAT1",
+            "SNORA20",
+            "PARP6",
+            "TIMM23",
+            "FIRRM",
+            "GNL3L",
+            "MORF4L2",
+            "NOP10",
+            "TRGC2",
+            "NIPSNAP3A",
+            "BORCS5",
+            "C4BPA",
+            "PIGU",
+            "CD40",
+            "CNN2",
+            "NANS",
+            "WDR70",
+            "BHLHE23",
+            "MIRLET7F1",
+            "CALM3",
+            "LRRC37A",
+            "SNORD96A",
+            "HIVEP2",
+            "RUFY1",
+            "SNORD103A",
+            "TUBA1B",
+            "NSD1",
+            "LILRA5",
+            "MRPL45",
+            "ZNF235",
+            "TMTC1",
+            "RNASE6",
+            "SLC38A11",
+            "MOSPD1",
+            "NODAL",
+            "DNAH1",
+            "MIR212",
+            "RHCE",
+            "SLC18B1",
+            "RAB11FIP4",
+            "CXCR4",
+            "ERMP1",
+            "EIF3C",
+            "IL1RL1",
+            "MAN2A2",
+            "PPDPF",
+            "FAM120A",
+            "IL18RAP",
+            "TMX1",
+            "CTU1",
+            "EMB",
+            "PRKCB",
+            "MKNK1",
+            "PMS2P5",
+            "SCYL3",
+            "SNORA27",
+            "HBG1",
+            "UBC",
+            "PPP1R18",
+            "SHTN1",
+            "AVIL",
+            "TIAM1",
+            "PTPN1",
+            "HLA-DRB5",
+            "LY75",
+            "GRAPL",
+            "DNMBP",
+            "GLYATL2",
+            "IKBKB",
+            "PLOD1",
+            "ELMO1",
+            "SNORD8",
+            "FBXO42",
+            "ZNFX1",
+            "LAPTM4A",
+            "NUP210L",
+            "METAP1",
+            "SEC11C",
+            "PSPH",
+            "ANKS1A",
+            "GCC2",
+            "CBX7",
+            "NSUN2",
+            "MTCH1",
+            "SCFD1",
+            "KIAA0825",
+            "RGS10",
+            "NEDD8",
+            "CMTR2",
+            "KLHL28",
+            "GRK5",
+            "NBR2",
+            "SHLD2",
+            "SDHAP1",
+            "PEPD",
+            "LNPK",
+            "CCDC146",
+            "ZNF816",
+            "TPD52",
+            "ZBTB44",
+            "DNAJB1",
+            "DDX11L2",
+            "TBC1D9",
+            "ATMIN",
+            "SFRP2",
+            "IVD",
+            "RIC1",
+            "OR7E125P",
+            "RSPRY1",
+            "TP53INP1",
+            "ADGRG3",
+            "H2AC6",
+            "GNPDA2",
+            "SNORD116-2",
+            "CARHSP1",
+            "ZNF852",
+            "TLR7",
+            "LPAR1",
+            "SESTD1",
+            "TUBB7P",
+            "FBXW7",
+            "RALBP1",
+            "PVALB",
+            "NSMCE2",
+            "DGAT1",
+            "TMEM156",
+            "MYH11",
+            "RPL36AL",
+            "PRLH",
+            "NCAPG2",
+            "GOLGA7",
+            "TBC1D7",
+            "ZC2HC1A",
+            "NTNG2",
+            "DCK",
+            "SLC6A19",
+            "RAB11A",
+            "PPP6R1",
+            "MZT2B",
+            "CLEC4C",
+            "RBM23",
+            "KLHL5",
+            "MRPL35",
+            "MIR632",
+            "PPP1CA",
+            "TLR3",
+            "ANKRD36B",
+            "UGCG",
+            "POLI",
+            "VOPP1",
+            "TESK2",
+            "PGP",
+            "H2BC14",
+            "ARPIN-AP3S2",
+            "CAMP",
+            "ADPRM",
+            "RPPH1",
+            "RUFY3",
+            "TEFM",
+            "DHX15",
+            "ALG8",
+            "PRORP",
+            "CEP76",
+            "ARPP19",
+            "TAS2R31",
+            "MSL1",
+            "AMY2B",
+            "TMEM176A",
+            "DDX11",
+            "COG5",
+            "CDC16",
+            "CXXC5",
+            "IGFBP7",
+            "IL2RA",
+            "TRIM44",
+            "PATL1",
+            "FBXO25",
+            "NTN1",
+            "NFXL1",
+            "CLEC4D",
+            "ARL17A",
+            "BCAS3",
+            "RRP7A",
+            "FRG1BP",
+            "SORD",
+            "SPRYD4",
+            "LRRFIP2",
+            "DSE",
+            "TPST1",
+            "LGALS3BP",
+            "PPA1",
+            "H4C2",
+            "PAPSS1",
+            "PSD4",
+            "WLS",
+            "C1RL",
+            "CDKN2D",
+            "SNORA16B",
+            "TSC22D1",
+            "TOR1AIP1",
+            "OCLN",
+            "THEM5",
+            "GDI2",
+            "CUBN",
+            "HRH4",
+            "SMURF1",
+            "PRDX3",
+            "IGLV6-57",
+            "KRTAP21-1",
+            "HEATR6",
+            "VNN3P",
+            "MRRF",
+            "SNORD62A",
+            "GAB3",
+            "ELAPOR1",
+            "ATP5F1A",
+            "SUDS3",
+            "RRM1",
+            "SNORD30",
+            "ZNF594",
+            "C11orf54",
+            "MX2",
+            "PEX13",
+            "SPRYD3",
+            "RFPL4A",
+            "HECTD4",
+            "SNORD79",
+            "RAB11B",
+            "CFAP20",
+            "ZNF814",
+            "ARPC3",
+            "THEM4",
+            "ANKRD28",
+            "SHISA4",
+            "MED27",
+            "EI24",
+            "PEAK1",
+            "FLVCR1",
+            "RAVER2",
+            "AFDN",
+            "ADHFE1",
+            "TMTC4",
+            "ALPK1",
+            "RBM12",
+            "SLC16A8",
+            "SLC38A9",
+            "ZNF641",
+            "GPR180",
+            "PGLS",
+            "VPS41",
+            "ZC3H13",
+            "NAIP",
+            "GNG7",
+            "SREK1IP1",
+            "NOD2",
+            "HIVEP1",
+            "SAXO2",
+            "WDR5B",
+            "ORAI3",
+            "WDR55",
+            "RNASEL",
+            "CSAD",
+            "RFC2",
+            "MDM1",
+            "STN1",
+            "CEACAM1",
+            "DTHD1",
+            "CKAP4",
+            "CKAP2",
+            "GYPE",
+            "ELL2",
+            "DHX35",
+            "PLA2G4A",
+            "LAP3",
+            "DND1",
+            "TMEM144",
+            "ATL3",
+            "TAPBPL",
+            "RPL23AP7",
+            "GM2A",
+            "CHD1L",
+            "KRT73",
+            "NDUFAF1",
+            "JKAMP",
+            "ERICH1",
+            "JARID2",
+            "CD300LF",
+            "GTF2F2",
+            "OMG",
+            "FAM25A",
+            "CRSP8P",
+            "NIPAL2",
+            "COX15",
+            "ST3GAL1",
+            "HLA-DMA",
+            "SCAMP1",
+            "NOTCH2NLB",
+            "RFC1",
+            "CYP4F29P",
+            "CTSK",
+            "PABPC4",
+            "KATNBL1",
+            "MYO15B",
+            "DDT",
+            "CXCL8",
+            "BLMH",
+            "ZBTB8OS",
+            "BANF1",
+            "SEPTIN14P20",
+            "NDUFA8",
+            "OR2A4",
+            "SF3B4",
+            "BET1",
+            "ZNF354A",
+            "EEFSEC",
+            "PDZD4",
+            "MDGA1",
+            "TNRC6C",
+            "TMEM63C",
+            "OXNAD1",
+            "ASPH",
+            "VSTM1",
+            "HSD17B7",
+            "MAP7",
+            "SNORD45B",
+            "ARG1",
+            "HEBP1",
+            "CYS1",
+            "CHCHD5",
+            "C1QTNF4",
+            "CIB1",
+            "MMP8",
+            "SNORA15",
+            "APPBP2",
+            "USP37",
+            "H4C8",
+            "ACSL5",
+            "FNDC3A",
+            "CAB39",
+            "NKX3-1",
+            "CD300C",
+            "ADGRG7",
+            "TTN",
+            "A2M",
+            "CDS2",
+            "TTC39B",
+            "NUDT7",
+            "PLG",
+            "UGT2B7",
+            "SLFN12",
+            "IER3",
+            "TMEM87B",
+            "LMTK2",
+            "ADNP",
+            "ERP27",
+            "RCN1",
+            "MAN1A2",
+            "TACR3",
+            "SIGLEC10",
+            "TMEM199",
+            "IWS1",
+            "CHN2",
+            "GYPB",
+            "GPR162",
+            "ZNF207",
+            "HAUS2",
+            "LHFPL2",
+            "CD80",
+            "NATD1",
+            "SNORD95",
+            "RFX7",
+            "TRIT1",
+            "PLB1",
+            "NXPE3",
+            "FAM185A",
+            "RAB4A",
+            "BID",
+            "RTN1",
+            "CDK2AP1",
+            "SRPK2",
+            "GDPGP1",
+            "PRICKLE3",
+            "ACADVL",
+            "MACROH2A1",
+            "SCIMP",
+            "MGAT5",
+            "RPS26P11",
+            "SARS2",
+            "GRK6",
+            "TRIM33",
+            "ZNF117",
+            "AK2",
+            "ZNF79",
+            "AKAP6",
+            "ZFTRAF1",
+            "CES1",
+            "TACC3",
+            "TEKT4P1",
+            "KLHDC3",
+            "MYLIP",
+            "ADAD2",
+            "DSC2",
+            "PRKRA",
+            "ZNF772",
+            "DDX12P",
+            "PFN3",
+            "GALNS",
+            "PCTP",
+            "RNF14",
+            "CHFR",
+            "ARHGAP9",
+            "VAMP1",
+            "ST3GAL6",
+            "PSMG2",
+            "MRPL18",
+            "CPAT1",
+            "DNAAF11",
+            "ZNF134",
+            "SNORA6",
+            "NUP153",
+            "NOMO1",
+            "GPR89B",
+            "DDX11L9",
+            "RHD",
+            "TRIM43B",
+            "FAM25C",
+            "SNORD62B",
+            "ARL17B",
+            "TSC22D3",
+            "TFAP2E",
+            "CD40LG",
+            "SNORD103B",
+            "SDHAP2",
+            "TVP23C-CDRT4",
+            "OR2A7",
+            "HBG2",
+            "SIGLEC12"
+        ],
+        "Coefficient": [
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+            "jaccard": 2.884923346060635
+        }
+    }
+}

p3/regress/Parkinsons_Disease/significant_genes_condition_Alzheimers_Disease.json

@@ -0,0 +1,2340 @@
+{
+    "significant_genes": {
+        "Variable": [
+            "ACO2",
+            "CYTIP",
+            "APOBEC3G",
+            "CRLF3",
+            "EPHX2",
+            "CALHM2",
+            "IRX2",
+            "CD99",
+            "MON2",
+            "IL3RA",
+            "AP3M1",
+            "ADGRE1",
+            "ELOVL7",
+            "WDCP",
+            "ETFRF1",
+            "SATB1",
+            "PATL1",
+            "APOL3",
+            "TCN2",
+            "NUDCD3",
+            "AKR1E2",
+            "NIPA2",
+            "RAF1",
+            "CALM3",
+            "MRLN",
+            "ZNF717",
+            "ABTB3",
+            "CD36",
+            "STAMBP",
+            "GTF2E1",
+            "FGFBP2",
+            "TNKS2",
+            "ANXA2P2",
+            "MGRN1",
+            "CARMIL1",
+            "ZNF770",
+            "STAG3L4",
+            "RPAIN",
+            "CDV3",
+            "NIPSNAP3A",
+            "ARL11",
+            "ZNF234",
+            "HSD17B10",
+            "CUL5",
+            "STRN",
+            "GPR89A",
+            "TOMM22",
+            "B3GNTL1",
+            "IER2",
+            "LRRFIP2",
+            "KIAA0825",
+            "GALNS",
+            "SMIM24",
+            "AP2B1",
+            "PPM1F",
+            "NCAPD2",
+            "NR1H2",
+            "SLC45A2",
+            "TARS1",
+            "RBM23",
+            "ZNF124",
+            "LRRC37A",
+            "TGM2",
+            "NLRP6",
+            "IGFBP7",
+            "FAM135A",
+            "COPS4",
+            "ARHGAP12",
+            "PGP",
+            "CSAD",
+            "ARL15",
+            "ZNF154",
+            "R3HDM2",
+            "MAPK8IP1",
+            "ZNF493",
+            "NADSYN1",
+            "NMD3",
+            "AIM2",
+            "TNFRSF17",
+            "SFRP2",
+            "SNORD62A",
+            "CAMP",
+            "GRAPL",
+            "GSDMB",
+            "HRK",
+            "PRDX3",
+            "BTF3",
+            "CMTR2",
+            "NEDD8",
+            "ZNF79",
+            "SDHD",
+            "JKAMP",
+            "CXCR4",
+            "CHSY1",
+            "ZNF101",
+            "CPNE1",
+            "LNPK",
+            "LRRC25",
+            "SLC38A6",
+            "BORCS5",
+            "MARCKSL1",
+            "SNORD38B",
+            "ABI3",
+            "MFSD14B",
+            "INPP5B",
+            "NODAL",
+            "SIX2",
+            "SETDB1",
+            "RBPMS2",
+            "BSCL2",
+            "LILRA5",
+            "RNASEK",
+            "POLR1B",
+            "POLR1HASP",
+            "MKNK1",
+            "PRMT2",
+            "GPR180",
+            "DCP1B",
+            "MSH3",
+            "BHLHA9",
+            "CARD8",
+            "MIR632",
+            "LAMTOR4",
+            "RIC1",
+            "RTKN2",
+            "GCNT2",
+            "PLOD1",
+            "CNN2",
+            "FKBP6",
+            "UBC",
+            "ADGRG3",
+            "CEP170",
+            "H2AC6",
+            "CRHR1",
+            "ECE1",
+            "TOMM34",
+            "PDS5B",
+            "GRK6",
+            "CEACAM1",
+            "C4BPA",
+            "ALCAM",
+            "HLA-DQA1",
+            "SNX20",
+            "ITM2C",
+            "DAPK2",
+            "BHLHE23",
+            "GLYATL2",
+            "ST3GAL1",
+            "LCLAT1",
+            "STN1",
+            "HEATR6",
+            "GSKIP",
+            "ID3",
+            "APH1B",
+            "HLTF",
+            "MRPL45",
+            "IL18RAP",
+            "NTNG2",
+            "NAA38",
+            "MOSPD1",
+            "VPREB3",
+            "ZNF841",
+            "TMX1",
+            "ANPEP",
+            "CCDC146",
+            "APOOL",
+            "DCK",
+            "OR7E125P",
+            "SNORA62",
+            "MYO15B",
+            "BCAS3",
+            "CARHSP1",
+            "DTWD1",
+            "TFAP2A",
+            "IGSF6",
+            "RPS23",
+            "ELMO1",
+            "DHX35",
+            "FZD9",
+            "ZNF134",
+            "ZXDC",
+            "TESK2",
+            "ERI1",
+            "GOLPH3L",
+            "GCC2",
+            "SEC31B",
+            "SNORD30",
+            "SLC18B1",
+            "ZNF852",
+            "NSMCE2",
+            "NAT10",
+            "EBF1",
+            "FBXO25",
+            "IFT80",
+            "CAPG",
+            "ZCCHC7",
+            "ANKRD28",
+            "PAPOLG",
+            "ZBTB44",
+            "HOXB2",
+            "RPL13AP5",
+            "CANT1",
+            "LDLR",
+            "METAP1",
+            "FAM120A",
+            "TGFBI",
+            "KLHL28",
+            "HLA-DRB5",
+            "KRTAP10-4",
+            "UQCRFS1",
+            "TAMM41",
+            "THOC1",
+            "GNG7",
+            "TCF24",
+            "PLD1",
+            "SCIMP",
+            "SIDT1",
+            "BANF1",
+            "GNPDA2",
+            "PADI4",
+            "BLK",
+            "CLEC4D",
+            "ELAPOR1",
+            "PRR18",
+            "CYBB",
+            "SH3KBP1",
+            "PRORP",
+            "PLA2G4A",
+            "TRGC2",
+            "TPD52",
+            "UTF1",
+            "PRKCSH",
+            "TRIM43",
+            "RALBP1",
+            "MVP",
+            "CXXC5",
+            "LGALS3BP",
+            "ADI1",
+            "TMEM43",
+            "DCTN5",
+            "ZNF208",
+            "GDI2",
+            "TRIM27",
+            "ZNF532",
+            "MAN1A2",
+            "SOCS1",
+            "PORCN",
+            "KLRD1",
+            "RFX7",
+            "STX8",
+            "NCAM1",
+            "FLJ42627",
+            "SNORD96A",
+            "SIDT2",
+            "SPTLC2",
+            "DGCR6L",
+            "APOD",
+            "RGS10",
+            "SFT2D1",
+            "EIF3J",
+            "MLXIP",
+            "PRPF18",
+            "ACP3",
+            "PSPH",
+            "S100P",
+            "PLAGL2",
+            "TIMM23",
+            "SLC6A19",
+            "RTCA",
+            "USP37",
+            "GPR84",
+            "PIK3R6",
+            "RUNX2",
+            "KCNAB2",
+            "TVP23B",
+            "VPS53",
+            "EDRF1",
+            "CYBRD1",
+            "PEPD",
+            "ERAP1",
+            "ATMIN",
+            "MIR212",
+            "GRK5",
+            "ZNFX1",
+            "ARPP19",
+            "ZNF813",
+            "ANKS1A",
+            "TMEM87A",
+            "NANS",
+            "SDHAP1",
+            "MAP2K1",
+            "SKIL",
+            "RAVER2",
+            "CHORDC1",
+            "WIPI1",
+            "IVD",
+            "TANGO2",
+            "PMS2P1",
+            "TP53INP1",
+            "ABCA7",
+            "MARCHF1",
+            "OMG",
+            "ZCCHC9",
+            "ARHGAP9",
+            "PIK3C2A",
+            "ARPIN-AP3S2",
+            "MTCH1",
+            "CXCR3",
+            "EI24",
+            "MORF4L2",
+            "PLXDC1",
+            "ANKRD36B",
+            "AK5",
+            "RAB3GAP2",
+            "SRXN1",
+            "TUBA1B",
+            "SNORA40",
+            "KRTAP21-1",
+            "EEFSEC",
+            "SNORA75",
+            "FSHMD1A",
+            "C11orf54",
+            "ACO1",
+            "PARP6",
+            "SMURF1",
+            "TTLL3",
+            "GOLGA8H",
+            "RNU4-1",
+            "NBR2",
+            "IL2RA",
+            "RFC2",
+            "VOPP1",
+            "DPH6",
+            "MRE11",
+            "AMN",
+            "RHCE",
+            "HMGA2",
+            "MCEMP1",
+            "RPL36AL",
+            "APOBEC3A",
+            "SNORA41",
+            "KRTAP10-2",
+            "LDB1",
+            "GNB4",
+            "ERMP1",
+            "ZFAS1",
+            "DNAAF11",
+            "EIF4EBP2",
+            "DHX34",
+            "RPPH1",
+            "BST1",
+            "GNL3L",
+            "FLVCR1",
+            "HBG1",
+            "RPS9",
+            "ANKRD11",
+            "EEF2",
+            "PRICKLE1",
+            "HSD17B7",
+            "ISCA1",
+            "SPRYD3",
+            "ZNF354A",
+            "GAB3",
+            "PRKRA",
+            "PMS2P5",
+            "SNORA27",
+            "CD177",
+            "AFDN",
+            "NDUFB6",
+            "SLMAP",
+            "ALG8",
+            "NUP210L",
+            "F2RL1",
+            "HDC",
+            "TMEM176A",
+            "BTN3A3",
+            "NSD1",
+            "TTC12",
+            "MMP8",
+            "OCLN",
+            "MMP25",
+            "CD101",
+            "KLHL5",
+            "AKTIP",
+            "ALPK1",
+            "MRPL47",
+            "SND1",
+            "SRPK2",
+            "PGM5",
+            "LRRC28",
+            "DSE",
+            "CD300LF",
+            "PEAK1",
+            "AKAP6",
+            "ELP2",
+            "FAM27B",
+            "FNDC3A",
+            "SGSH",
+            "SLC17A7",
+            "SNHG29",
+            "DNASE2",
+            "RPL23AP82",
+            "ZC2HC1A",
+            "CDC42EP5",
+            "TMTC1",
+            "CEP76",
+            "CALU",
+            "LPCAT1",
+            "SEPTIN14P20",
+            "UHMK1",
+            "MACO1",
+            "HAUS2",
+            "ARL17A",
+            "KRT73",
+            "NCAPG2",
+            "HNRNPUL2",
+            "MMRN1",
+            "CD300C",
+            "RRM1",
+            "PWARSN",
+            "RNPC3",
+            "MDGA1",
+            "PLB1",
+            "ZNF486",
+            "NSUN2",
+            "NXPE3",
+            "ZNF814",
+            "C1QTNF4",
+            "TMEM156",
+            "CXCL8",
+            "DESI1",
+            "SHTN1",
+            "RNASE6",
+            "GTF2H1",
+            "HIPK3",
+            "PGM5P2",
+            "PPDPF",
+            "CLCN7",
+            "TPST1",
+            "SSBP3",
+            "FCER1A",
+            "CD244",
+            "CUBN",
+            "TMTC4",
+            "NOD2",
+            "SAXO2",
+            "NIPAL2",
+            "MGAT5",
+            "BRD7",
+            "EIF3C",
+            "RABGGTB",
+            "CKAP4",
+            "PTPN1",
+            "SLC9A6",
+            "ID2",
+            "LPIN2",
+            "MAFF",
+            "RSPRY1",
+            "CENPC",
+            "SNORD8",
+            "USP39",
+            "ITPK1",
+            "CHD1L",
+            "PVALB",
+            "SHISA4",
+            "NT5DC1",
+            "RAP1A",
+            "TBC1D7",
+            "PDE8A",
+            "CXCL16",
+            "HLA-J",
+            "TBC1D9",
+            "GRHPR",
+            "PIK3R1",
+            "NDUFAF1",
+            "POLR2E",
+            "OR52N4",
+            "INTS3",
+            "EIF2AK3",
+            "WDR70",
+            "ZNF429",
+            "MRRF",
+            "ELP5",
+            "RPS26P11",
+            "TIAM1",
+            "IRAG2",
+            "IER3",
+            "MSL1",
+            "VNN3P",
+            "SHLD2",
+            "MCTS1",
+            "ZNF26",
+            "KCNJ4",
+            "PABPC4",
+            "SLC38A11",
+            "SNORA15",
+            "ZBTB8OS",
+            "LTA4H",
+            "PRLH",
+            "MZT2B",
+            "TMEM63C",
+            "DNAJB1",
+            "P2RY12",
+            "WSB2",
+            "YWHAH",
+            "SEMA4B",
+            "TLR3",
+            "YTHDC1",
+            "ASPH",
+            "GASK1B",
+            "ADGRG7",
+            "ID1",
+            "MDM1",
+            "TAS2R31",
+            "TSHZ2",
+            "ITPR3",
+            "ARG1",
+            "MROH6",
+            "CAB39",
+            "LINC00268",
+            "MS4A2",
+            "GSTO1",
+            "CTU1",
+            "TRANK1",
+            "DTX2",
+            "PFN3",
+            "ZNF706",
+            "LY75",
+            "IGKV2D-26",
+            "THEM4",
+            "THEM5",
+            "IKBKB",
+            "ZNF641",
+            "CAMK1D",
+            "ADPRM",
+            "EMB",
+            "ADGB",
+            "WARS1",
+            "CD40",
+            "TRMT1",
+            "LSMEM1",
+            "FAM185A",
+            "DUSP18",
+            "NOP10",
+            "GGTA1",
+            "DAPK1",
+            "PCBP1",
+            "LAP3",
+            "TACR3",
+            "ETHE1",
+            "SAC3D1",
+            "MILR1",
+            "HADHA",
+            "NFE2L1",
+            "SLFN12",
+            "DTHD1",
+            "MOSPD2",
+            "MBOAT1",
+            "ZNF518B",
+            "CLDN7",
+            "IMPA2",
+            "THUMPD3",
+            "ARHGEF3",
+            "ERP27",
+            "ASGR2",
+            "NTN1",
+            "FBXW7",
+            "GDE1",
+            "PAPSS1",
+            "HEXIM1",
+            "IL12RB2",
+            "CTLA4",
+            "PLP2",
+            "PTPN11",
+            "MT1F",
+            "NGDN",
+            "CYP1B1",
+            "EIF4H",
+            "H4C8",
+            "SCAMP1",
+            "SNAPC5",
+            "LINC00852",
+            "GUSBP15",
+            "MRI1",
+            "TUBD1",
+            "SMAD4",
+            "ZNF431",
+            "LILRA4",
+            "ERICH1",
+            "COMMD1",
+            "DND1",
+            "IWS1",
+            "MED7",
+            "ACP5",
+            "BNC2",
+            "SRCAP",
+            "SLC35E2B",
+            "MLKL",
+            "SLC48A1",
+            "ZFTRAF1",
+            "COA5",
+            "GLMP",
+            "PLG",
+            "RASAL3",
+            "GTF2H2",
+            "RCOR1",
+            "CD151",
+            "RTN1",
+            "FRG1BP",
+            "BCAP31",
+            "ZNF275",
+            "AP4E1",
+            "CKAP2",
+            "WDR5B",
+            "LPAR2",
+            "RBM12",
+            "GPR15",
+            "CASP9",
+            "ETV7",
+            "CARD11",
+            "BET1",
+            "STX2",
+            "RFPL4A",
+            "TMEM170A",
+            "CA1",
+            "PRICKLE3",
+            "SNORD35A",
+            "DDX11L2",
+            "POGZ",
+            "ZKSCAN1",
+            "RPL23AP7",
+            "CCDC59",
+            "COX2",
+            "SLC35A4",
+            "CDK2AP2",
+            "GPR132",
+            "CD37",
+            "DDX11",
+            "NKX3-1",
+            "CASP4",
+            "CDC16",
+            "SNORD107",
+            "GYPE",
+            "GSC2",
+            "DDT",
+            "POLI",
+            "HIVEP2",
+            "CMBL",
+            "JARID2",
+            "ZNF177",
+            "TOR1AIP1",
+            "C15orf39",
+            "PDE4DIP",
+            "SH3GL1",
+            "SNORD45B",
+            "BNIP3L",
+            "CLCN3",
+            "AK2",
+            "IDH1",
+            "ABCD3",
+            "H2BC20P",
+            "NEK6",
+            "AOC3",
+            "MOB1B",
+            "METTL6",
+            "SPRYD4",
+            "VSTM1",
+            "IGHV1OR15-9",
+            "SULT1A2",
+            "LHFPL2",
+            "RALGDS",
+            "LYZ",
+            "PRR35",
+            "ADNP",
+            "OR52K3P",
+            "AVIL",
+            "ZFP90",
+            "BRCA1",
+            "RLIG1",
+            "ZNF445",
+            "VCPKMT",
+            "NECAP2",
+            "BID",
+            "FCRL6",
+            "TLR7",
+            "TNFRSF1A",
+            "PSMC5",
+            "IGLV6-57",
+            "SNORD103A",
+            "PRPS1",
+            "RSU1",
+            "PPP6R1",
+            "SGMS1",
+            "SNORA16B",
+            "NUP153",
+            "PDZD4",
+            "PDXDC1",
+            "OSBPL3",
+            "GPR137B",
+            "HGSNAT",
+            "ADHFE1",
+            "MIR223",
+            "PRKCB",
+            "ASL",
+            "PLVAP",
+            "PPP1R12B",
+            "TBC1D10C",
+            "COIL",
+            "SNURF",
+            "PSD4",
+            "MRPS15",
+            "CDS2",
+            "KCNJ15",
+            "MAP7",
+            "SCAPER",
+            "FBP1",
+            "ZNF749",
+            "NUDT7",
+            "TMED8",
+            "VTRNA1-1",
+            "VNN1",
+            "HSPA1L",
+            "LRRC37A4P",
+            "GYPB",
+            "SEC61A1",
+            "ANKRD20A11P",
+            "SLC35A5",
+            "COQ8A",
+            "GBGT1",
+            "HCFC1",
+            "SNORA38",
+            "MIR210",
+            "MDK",
+            "SNORA20",
+            "CLEC1B",
+            "TPP2",
+            "KATNBL1",
+            "U2AF1",
+            "ARHGAP31",
+            "UBE2K",
+            "OXR1",
+            "DDX12P",
+            "SORD",
+            "SLC35E4",
+            "MX2",
+            "MYLIP",
+            "SUPT16H",
+            "TMEM144",
+            "RHOBTB3",
+            "ZNF816",
+            "ZNF559",
+            "YKT6",
+            "PDK4",
+            "NOMO1",
+            "MAB21L3",
+            "ALPL",
+            "ZNF683",
+            "APPBP2",
+            "EIF4E",
+            "CD80",
+            "SNORD62B",
+            "GPR89B",
+            "RHD",
+            "MAFIP",
+            "ARL17B",
+            "DDX11L9",
+            "HBG2",
+            "FAM120A2P",
+            "SDHAP2",
+            "TRIM43B",
+            "NOMO2",
+            "NOMO3"
+        ],
+        "Coefficient": [
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+    "cv_performance": {
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+            "precision": 90.89430894308944,
+            "recall": 100.0,
+            "f1": 95.22439900006279
+        },
+        "selection": {
+            "precision": 4.780412905471058,
+            "recall": 7.35632183908046,
+            "f1": 5.794929809551111,
+            "jaccard": 2.984098120995251
+        }
+    }
+}

p3/regress/Parkinsons_Disease/significant_genes_condition_Gender.json

@@ -0,0 +1,2355 @@
+{
+    "significant_genes": {
+        "Variable": [
+            "ACO2",
+            "CYTIP",
+            "APOBEC3G",
+            "CRLF3",
+            "EPHX2",
+            "CALHM2",
+            "CD99",
+            "IL3RA",
+            "IRX2",
+            "MON2",
+            "AP3M1",
+            "ADGRE1",
+            "ELOVL7",
+            "ETFRF1",
+            "SATB1",
+            "WDCP",
+            "PATL1",
+            "TCN2",
+            "NUDCD3",
+            "RAF1",
+            "CALM3",
+            "AKR1E2",
+            "NIPA2",
+            "APOL3",
+            "GTF2E1",
+            "ZNF717",
+            "STAMBP",
+            "MRLN",
+            "CARMIL1",
+            "MGRN1",
+            "ABTB3",
+            "TNKS2",
+            "STAG3L4",
+            "CD36",
+            "ZNF770",
+            "RPAIN",
+            "FGFBP2",
+            "ZNF234",
+            "ANXA2P2",
+            "GPR89A",
+            "STRN",
+            "CDV3",
+            "IER2",
+            "NIPSNAP3A",
+            "ARL11",
+            "CUL5",
+            "HSD17B10",
+            "PPM1F",
+            "TOMM22",
+            "SMIM24",
+            "LRRFIP2",
+            "B3GNTL1",
+            "ZNF124",
+            "ZNF493",
+            "KIAA0825",
+            "NR1H2",
+            "SLC45A2",
+            "COPS4",
+            "RBM23",
+            "AP2B1",
+            "TGM2",
+            "R3HDM2",
+            "CSAD",
+            "NLRP6",
+            "TARS1",
+            "ARL15",
+            "GALNS",
+            "IGFBP7",
+            "PGP",
+            "NCAPD2",
+            "LRRC37A",
+            "FAM135A",
+            "ARHGAP12",
+            "BTF3",
+            "MAPK8IP1",
+            "SDHD",
+            "SFRP2",
+            "NMD3",
+            "ZNF154",
+            "INPP5B",
+            "CAMP",
+            "ZNF101",
+            "NADSYN1",
+            "LRRC25",
+            "AIM2",
+            "SNORD62A",
+            "TNFRSF17",
+            "CMTR2",
+            "ZNF79",
+            "NEDD8",
+            "HRK",
+            "DCP1B",
+            "PRDX3",
+            "GRAPL",
+            "MFSD14B",
+            "MIR632",
+            "GSDMB",
+            "CHSY1",
+            "CPNE1",
+            "RNASEK",
+            "LNPK",
+            "PLOD1",
+            "CXCR4",
+            "SLC38A6",
+            "POLR1B",
+            "BORCS5",
+            "SNORD38B",
+            "MARCKSL1",
+            "PRMT2",
+            "GCNT2",
+            "JKAMP",
+            "GSKIP",
+            "POLR1HASP",
+            "CEP170",
+            "CARD8",
+            "MKNK1",
+            "UBC",
+            "NODAL",
+            "LCLAT1",
+            "LILRA5",
+            "SETDB1",
+            "BSCL2",
+            "SIX2",
+            "MSH3",
+            "TOMM34",
+            "RBPMS2",
+            "CEACAM1",
+            "CNN2",
+            "ABI3",
+            "GLYATL2",
+            "DAPK2",
+            "VPREB3",
+            "BHLHA9",
+            "RIC1",
+            "ST3GAL1",
+            "APH1B",
+            "ID3",
+            "CRHR1",
+            "FKBP6",
+            "HLA-DQA1",
+            "BHLHE23",
+            "GPR180",
+            "MOSPD1",
+            "ALCAM",
+            "SNX20",
+            "CCDC146",
+            "ITM2C",
+            "OR7E125P",
+            "PDS5B",
+            "EBF1",
+            "RTKN2",
+            "ADGRG3",
+            "LAMTOR4",
+            "IL18RAP",
+            "ECE1",
+            "ZBTB44",
+            "NTNG2",
+            "C4BPA",
+            "HLTF",
+            "MYO15B",
+            "APOOL",
+            "ANPEP",
+            "TMX1",
+            "GCC2",
+            "IGSF6",
+            "SEC31B",
+            "NSMCE2",
+            "GRK6",
+            "MRPL45",
+            "DTWD1",
+            "ERI1",
+            "BCAS3",
+            "ELMO1",
+            "SLC18B1",
+            "HOXB2",
+            "STN1",
+            "ZNF841",
+            "DHX35",
+            "NAT10",
+            "ZNF852",
+            "SNORA62",
+            "FAM120A",
+            "CAPG",
+            "CARHSP1",
+            "FBXO25",
+            "RPL13AP5",
+            "TAMM41",
+            "ZNF134",
+            "SNORD30",
+            "RPS23",
+            "KRTAP10-4",
+            "LDLR",
+            "FZD9",
+            "DCK",
+            "KLHL28",
+            "THOC1",
+            "CANT1",
+            "NAA38",
+            "ANKRD28",
+            "TFAP2A",
+            "UQCRFS1",
+            "GNG7",
+            "TCF24",
+            "TESK2",
+            "ZNFX1",
+            "GOLPH3L",
+            "TRGC2",
+            "ZXDC",
+            "HLA-DRB5",
+            "RALBP1",
+            "H2AC6",
+            "ZNF208",
+            "PRKCSH",
+            "EIF3J",
+            "GNPDA2",
+            "GDI2",
+            "HEATR6",
+            "PADI4",
+            "PRR18",
+            "UTF1",
+            "ELAPOR1",
+            "SH3KBP1",
+            "TPD52",
+            "PRORP",
+            "RFX7",
+            "SCIMP",
+            "PLA2G4A",
+            "TGFBI",
+            "SIDT1",
+            "TMEM43",
+            "METAP1",
+            "LGALS3BP",
+            "TRIM43",
+            "SKIL",
+            "IFT80",
+            "ZCCHC7",
+            "PLD1",
+            "TRIM27",
+            "ZNF813",
+            "STX8",
+            "CXXC5",
+            "ACP3",
+            "PIK3R6",
+            "ZNF532",
+            "BANF1",
+            "SOCS1",
+            "RTCA",
+            "NCAM1",
+            "GPR84",
+            "APOD",
+            "SIDT2",
+            "DCTN5",
+            "PAPOLG",
+            "KCNAB2",
+            "TIMM23",
+            "ARHGAP9",
+            "OMG",
+            "PRPF18",
+            "TMEM87A",
+            "FLJ42627",
+            "ADI1",
+            "LDB1",
+            "VPS53",
+            "MVP",
+            "MLXIP",
+            "BLK",
+            "ZCCHC9",
+            "ARPP19",
+            "SPTLC2",
+            "DGCR6L",
+            "RUNX2",
+            "SNORD96A",
+            "PORCN",
+            "CLEC4D",
+            "SFT2D1",
+            "RAVER2",
+            "MIR212",
+            "S100P",
+            "IVD",
+            "SLC6A19",
+            "SLMAP",
+            "PLAGL2",
+            "DHX34",
+            "CYBRD1",
+            "PEPD",
+            "KLRD1",
+            "CYBB",
+            "CHORDC1",
+            "KRTAP21-1",
+            "TUBA1B",
+            "TVP23B",
+            "MAN1A2",
+            "PIK3C2A",
+            "ABCA7",
+            "NANS",
+            "FSHMD1A",
+            "RGS10",
+            "MORF4L2",
+            "TP53INP1",
+            "PMS2P1",
+            "MAP2K1",
+            "SRXN1",
+            "PSPH",
+            "USP37",
+            "ARPIN-AP3S2",
+            "MRE11",
+            "SNORA41",
+            "ANKS1A",
+            "RNU4-1",
+            "WIPI1",
+            "RAB3GAP2",
+            "NDUFB6",
+            "ERAP1",
+            "C11orf54",
+            "ANKRD36B",
+            "RFC2",
+            "TMEM176A",
+            "APOBEC3A",
+            "GRK5",
+            "SND1",
+            "PRKRA",
+            "SNORA40",
+            "PLXDC1",
+            "ACO1",
+            "MARCHF1",
+            "AK5",
+            "ATMIN",
+            "PTPN1",
+            "EDRF1",
+            "AFDN",
+            "TANGO2",
+            "RPPH1",
+            "VOPP1",
+            "SMURF1",
+            "RHCE",
+            "TTLL3",
+            "SNORA75",
+            "MTCH1",
+            "GOLGA8H",
+            "RPS9",
+            "FLVCR1",
+            "HDC",
+            "ANKRD11",
+            "C1QTNF4",
+            "ISCA1",
+            "SEPTIN14P20",
+            "NUP210L",
+            "BST1",
+            "CEP76",
+            "KRTAP10-2",
+            "EI24",
+            "ZFAS1",
+            "MCEMP1",
+            "SLC17A7",
+            "IL2RA",
+            "NSD1",
+            "AKTIP",
+            "SDHAP1",
+            "ID2",
+            "OCLN",
+            "DPH6",
+            "RPL36AL",
+            "HAUS2",
+            "ERMP1",
+            "F2RL1",
+            "DSE",
+            "GNL3L",
+            "NCAPG2",
+            "DNASE2",
+            "GAB3",
+            "ZC2HC1A",
+            "CXCR3",
+            "CXCL8",
+            "HBG1",
+            "SHTN1",
+            "ALPK1",
+            "SRPK2",
+            "ZNF354A",
+            "NBR2",
+            "CD300LF",
+            "AKAP6",
+            "PARP6",
+            "TMTC1",
+            "DNAAF11",
+            "MDGA1",
+            "NDUFAF1",
+            "SPRYD3",
+            "ITPK1",
+            "LRRC28",
+            "KLHL5",
+            "TTC12",
+            "MRPL47",
+            "CD101",
+            "GNB4",
+            "KRT73",
+            "AMN",
+            "HMGA2",
+            "RABGGTB",
+            "CD177",
+            "MACO1",
+            "EEFSEC",
+            "CALU",
+            "NXPE3",
+            "PPDPF",
+            "PGM5",
+            "ZNF814",
+            "ALG8",
+            "HSD17B7",
+            "MMRN1",
+            "NIPAL2",
+            "ZNF486",
+            "RPL23AP82",
+            "SNORA27",
+            "WARS1",
+            "CUBN",
+            "DTX2",
+            "EIF4EBP2",
+            "RRM1",
+            "ARHGEF3",
+            "SNORA15",
+            "BRD7",
+            "ARL17A",
+            "SNORD8",
+            "CHD1L",
+            "HIPK3",
+            "RNPC3",
+            "SHLD2",
+            "NT5DC1",
+            "FAM27B",
+            "UHMK1",
+            "WDR70",
+            "SGSH",
+            "EEF2",
+            "PFN3",
+            "EIF4H",
+            "HLA-J",
+            "CDC42EP5",
+            "PGM5P2",
+            "PEAK1",
+            "SSBP3",
+            "NSUN2",
+            "TPST1",
+            "PRICKLE1",
+            "MGAT5",
+            "PLB1",
+            "PWARSN",
+            "TBC1D9",
+            "RNASE6",
+            "LPCAT1",
+            "PVALB",
+            "CD244",
+            "PIK3R1",
+            "THEM4",
+            "RPS26P11",
+            "P2RY12",
+            "SNHG29",
+            "PCBP1",
+            "LPIN2",
+            "FCER1A",
+            "PMS2P5",
+            "SLC35A4",
+            "SAXO2",
+            "FNDC3A",
+            "CLCN7",
+            "MMP25",
+            "TMTC4",
+            "CXCL16",
+            "EIF3C",
+            "USP39",
+            "TAS2R31",
+            "GDE1",
+            "ELP2",
+            "MRRF",
+            "GRHPR",
+            "IRAG2",
+            "DESI1",
+            "ID1",
+            "CD300C",
+            "ASPH",
+            "NOD2",
+            "ZNF26",
+            "ADGB",
+            "YWHAH",
+            "GGTA1",
+            "SLFN12",
+            "TMEM156",
+            "IER3",
+            "TLR3",
+            "ZNF429",
+            "MSL1",
+            "POLR2E",
+            "CTU1",
+            "MOSPD2",
+            "CD40",
+            "SHISA4",
+            "ADGRG7",
+            "EMB",
+            "SLC38A11",
+            "GTF2H1",
+            "COMMD1",
+            "MILR1",
+            "IGKV2D-26",
+            "TSHZ2",
+            "DNAJB1",
+            "CAB39",
+            "ZNF641",
+            "PABPC4",
+            "MAFF",
+            "CENPC",
+            "ZNF706",
+            "RAP1A",
+            "ZFTRAF1",
+            "DAPK1",
+            "TBC1D7",
+            "TUBD1",
+            "HNRNPUL2",
+            "ZBTB8OS",
+            "MBOAT1",
+            "MMP8",
+            "ITPR3",
+            "PDE8A",
+            "MROH6",
+            "SEMA4B",
+            "ARG1",
+            "BTN3A3",
+            "OR52N4",
+            "YTHDC1",
+            "DHX36",
+            "LY75",
+            "RASAL3",
+            "MED7",
+            "SAC3D1",
+            "DTHD1",
+            "HADHA",
+            "GLMP",
+            "GASK1B",
+            "TRMT1",
+            "RBM12",
+            "MCTS1",
+            "MZT2B",
+            "MS4A2",
+            "INTS3",
+            "MDM1",
+            "ACP5",
+            "LTA4H",
+            "LINC00852",
+            "CAMK1D",
+            "ZNF518B",
+            "FBXW7",
+            "WSB2",
+            "LPAR2",
+            "PLP2",
+            "ELP5",
+            "ADPRM",
+            "TMEM63C",
+            "ASGR2",
+            "THEM5",
+            "MT1F",
+            "VNN3P",
+            "PRKCB",
+            "IMPA2",
+            "PAPSS1",
+            "C15orf39",
+            "KCNJ4",
+            "NFE2L1",
+            "BNC2",
+            "TACR3",
+            "SF3B4",
+            "LILRA4",
+            "LINC00268",
+            "IKBKB",
+            "PRLH",
+            "BRCA1",
+            "EIF2AK3",
+            "CKAP2",
+            "FAM185A",
+            "GSTO1",
+            "SLC35E2B",
+            "SULT1A2",
+            "MLKL",
+            "PTPN11",
+            "SLC48A1",
+            "MRI1",
+            "FPGT",
+            "CD151",
+            "SMAD4",
+            "NGDN",
+            "SNAPC5",
+            "CKAP4",
+            "NOP10",
+            "IL12RB2",
+            "COX2",
+            "ZNF275",
+            "ETHE1",
+            "FRG1BP",
+            "H4C8",
+            "RLIG1",
+            "TMEM170A",
+            "SLC9A6",
+            "RTN1",
+            "SCAMP1",
+            "ERP27",
+            "ERICH1",
+            "SRCAP",
+            "MOB1B",
+            "CYP1B1",
+            "RCOR1",
+            "NTN1",
+            "BCAP31",
+            "DUSP18",
+            "DND1",
+            "LSMEM1",
+            "THUMPD3",
+            "LAP3",
+            "TIAM1",
+            "DDT",
+            "RFPL4A",
+            "RSPRY1",
+            "COA5",
+            "HEXIM1",
+            "CCDC59",
+            "POLI",
+            "PRICKLE3",
+            "AOC3",
+            "ADNP",
+            "SNORD35A",
+            "SPRYD4",
+            "JARID2",
+            "PPP1R12B",
+            "CASP4",
+            "LYZ",
+            "GUSBP15",
+            "RSU1",
+            "ZNF431",
+            "PLG",
+            "GTF2H2",
+            "POLD3",
+            "GYPE",
+            "CTLA4",
+            "IWS1",
+            "ADHFE1",
+            "CA1",
+            "AP4E1",
+            "H2BC20P",
+            "ETV7",
+            "IDH1",
+            "SNORA20",
+            "DDX11",
+            "METTL6",
+            "ZKSCAN1",
+            "GPR15",
+            "CMBL",
+            "SNORD107",
+            "MRPS15",
+            "CLCN3",
+            "EIF4E",
+            "CARD11",
+            "STX2",
+            "COIL",
+            "TRMT11",
+            "NEK6",
+            "CDS2",
+            "ZNF177",
+            "RPL23AP7",
+            "CASP9",
+            "DDX11L2",
+            "VCPKMT",
+            "GBGT1",
+            "DGAT1",
+            "VNN1",
+            "ZNF445",
+            "OR52K3P",
+            "WDR5B",
+            "HSPA1L",
+            "GPR132",
+            "SHKBP1",
+            "RHOBTB3",
+            "CYTB",
+            "PPP6R1",
+            "IGLV6-57",
+            "ASL",
+            "PDZD4",
+            "POGZ",
+            "U2AF1",
+            "MIR210",
+            "IGKV3-7",
+            "AVIL",
+            "PSD4",
+            "ABCD3",
+            "LHFPL2",
+            "NKX3-1",
+            "PDXDC1",
+            "NECAP2",
+            "SNORA38",
+            "DAB1",
+            "BID",
+            "SNORD45B",
+            "DNMBP",
+            "KATNBL1",
+            "HIVEP2",
+            "TMED8",
+            "GPR137B",
+            "BET1",
+            "STK35",
+            "SNURF",
+            "TBC1D10C",
+            "SNORD103A",
+            "GUSBP3",
+            "VSTM1",
+            "CLDN7",
+            "PDE4DIP",
+            "CABP2",
+            "EVA1C",
+            "TOR1AIP1",
+            "SH3GL1",
+            "CLEC1B",
+            "CDC16",
+            "VNN2",
+            "ZNF816",
+            "ZNF594",
+            "RALGDS",
+            "TRAP1",
+            "CLEC4GP1",
+            "ZNF749",
+            "ALPL",
+            "PPP6C",
+            "HGSNAT",
+            "GLRX",
+            "SNX9",
+            "ADAD2",
+            "EIF4E2",
+            "VTRNA1-1",
+            "FMN1",
+            "ANKRD20A11P",
+            "NDUFV2",
+            "SLC8B1",
+            "OSBPL3",
+            "COG5",
+            "UBE2K",
+            "HCFC1",
+            "ZFP90",
+            "FCGR2B",
+            "GIMAP5",
+            "SLC35A5",
+            "XPO1",
+            "UGT2B7",
+            "GYPB",
+            "TNFRSF1A",
+            "CFD",
+            "PDK4",
+            "SGMS1",
+            "REEP4",
+            "DDX12P",
+            "NOTCH2NLB",
+            "ZNF83",
+            "CYTOR",
+            "NOMO1",
+            "YKT6",
+            "KCNJ15",
+            "RNF144B",
+            "GPR89B",
+            "SNORD62B",
+            "RHD",
+            "ARL17B",
+            "MAFIP",
+            "HBG2",
+            "DDX11L9",
+            "FAM120A2P",
+            "TFAP2E",
+            "SDHAP2",
+            "CD40LG",
+            "GTF2H2B",
+            "EIF3CL",
+            "PLGLA",
+            "NOMO2",
+            "NOMO3",
+            "SNORD103B"
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+        "Coefficient": [
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+        }
+    }
+}

p3/regress/Parkinsons_Disease/significant_genes_condition_Hypertension.json

@@ -0,0 +1,2340 @@
+{
+    "significant_genes": {
+        "Variable": [
+            "ACO2",
+            "CYTIP",
+            "APOBEC3G",
+            "CRLF3",
+            "EPHX2",
+            "CALHM2",
+            "IRX2",
+            "CD99",
+            "MON2",
+            "IL3RA",
+            "AP3M1",
+            "ADGRE1",
+            "ELOVL7",
+            "WDCP",
+            "ETFRF1",
+            "SATB1",
+            "PATL1",
+            "APOL3",
+            "TCN2",
+            "NUDCD3",
+            "AKR1E2",
+            "RAF1",
+            "NIPA2",
+            "CALM3",
+            "MRLN",
+            "ZNF717",
+            "STAMBP",
+            "ABTB3",
+            "GTF2E1",
+            "CD36",
+            "FGFBP2",
+            "ANXA2P2",
+            "TNKS2",
+            "CARMIL1",
+            "MGRN1",
+            "ZNF770",
+            "RPAIN",
+            "STAG3L4",
+            "CDV3",
+            "NIPSNAP3A",
+            "ARL11",
+            "STRN",
+            "ZNF234",
+            "CUL5",
+            "HSD17B10",
+            "GPR89A",
+            "TOMM22",
+            "IER2",
+            "B3GNTL1",
+            "LRRFIP2",
+            "SMIM24",
+            "KIAA0825",
+            "GALNS",
+            "AP2B1",
+            "SLC45A2",
+            "NCAPD2",
+            "PPM1F",
+            "NR1H2",
+            "TARS1",
+            "ZNF124",
+            "IGFBP7",
+            "RBM23",
+            "LRRC37A",
+            "TGM2",
+            "NLRP6",
+            "COPS4",
+            "PGP",
+            "CSAD",
+            "ARHGAP12",
+            "FAM135A",
+            "ARL15",
+            "ZNF154",
+            "R3HDM2",
+            "MAPK8IP1",
+            "NADSYN1",
+            "ZNF493",
+            "SNORD62A",
+            "AIM2",
+            "TNFRSF17",
+            "NMD3",
+            "SFRP2",
+            "CAMP",
+            "GRAPL",
+            "GSDMB",
+            "PRDX3",
+            "NEDD8",
+            "HRK",
+            "SDHD",
+            "JKAMP",
+            "BTF3",
+            "CMTR2",
+            "ZNF101",
+            "CHSY1",
+            "ZNF79",
+            "CXCR4",
+            "LRRC25",
+            "LNPK",
+            "MARCKSL1",
+            "CPNE1",
+            "SLC38A6",
+            "BORCS5",
+            "MFSD14B",
+            "ABI3",
+            "SNORD38B",
+            "INPP5B",
+            "SIX2",
+            "NODAL",
+            "RBPMS2",
+            "POLR1B",
+            "BSCL2",
+            "SETDB1",
+            "LILRA5",
+            "GPR180",
+            "POLR1HASP",
+            "RNASEK",
+            "MKNK1",
+            "PRMT2",
+            "BHLHA9",
+            "DCP1B",
+            "PLOD1",
+            "MSH3",
+            "CNN2",
+            "CARD8",
+            "UBC",
+            "GCNT2",
+            "RIC1",
+            "PDS5B",
+            "CEP170",
+            "RTKN2",
+            "H2AC6",
+            "ADGRG3",
+            "LAMTOR4",
+            "MIR632",
+            "TOMM34",
+            "CRHR1",
+            "ALCAM",
+            "FKBP6",
+            "ECE1",
+            "DAPK2",
+            "C4BPA",
+            "GLYATL2",
+            "ID3",
+            "HLA-DQA1",
+            "BHLHE23",
+            "ST3GAL1",
+            "SNX20",
+            "GRK6",
+            "LCLAT1",
+            "CEACAM1",
+            "ITM2C",
+            "VPREB3",
+            "APH1B",
+            "HEATR6",
+            "HLTF",
+            "STN1",
+            "NTNG2",
+            "IL18RAP",
+            "ZNF841",
+            "MOSPD1",
+            "APOOL",
+            "ANPEP",
+            "CCDC146",
+            "MRPL45",
+            "SNORA62",
+            "GSKIP",
+            "NAA38",
+            "OR7E125P",
+            "MYO15B",
+            "BCAS3",
+            "DCK",
+            "DTWD1",
+            "TFAP2A",
+            "TMX1",
+            "CARHSP1",
+            "RPS23",
+            "ELMO1",
+            "IGSF6",
+            "DHX35",
+            "GCC2",
+            "ZXDC",
+            "FZD9",
+            "SNORD30",
+            "ZNF134",
+            "ZNF852",
+            "SLC18B1",
+            "EBF1",
+            "TESK2",
+            "ANKRD28",
+            "ZCCHC7",
+            "GOLPH3L",
+            "ERI1",
+            "CAPG",
+            "FBXO25",
+            "PAPOLG",
+            "SEC31B",
+            "IFT80",
+            "NSMCE2",
+            "RPL13AP5",
+            "CANT1",
+            "HOXB2",
+            "NAT10",
+            "KLHL28",
+            "ZBTB44",
+            "TAMM41",
+            "FAM120A",
+            "KRTAP10-4",
+            "LDLR",
+            "METAP1",
+            "HLA-DRB5",
+            "THOC1",
+            "TGFBI",
+            "TCF24",
+            "UQCRFS1",
+            "GNG7",
+            "SCIMP",
+            "BANF1",
+            "PLD1",
+            "SIDT1",
+            "ELAPOR1",
+            "GNPDA2",
+            "PADI4",
+            "CYBB",
+            "BLK",
+            "PRR18",
+            "RALBP1",
+            "PRORP",
+            "PLA2G4A",
+            "TRGC2",
+            "CLEC4D",
+            "TPD52",
+            "MVP",
+            "SH3KBP1",
+            "TRIM43",
+            "PRKCSH",
+            "LGALS3BP",
+            "DCTN5",
+            "UTF1",
+            "TMEM43",
+            "KLRD1",
+            "TRIM27",
+            "MAN1A2",
+            "ADI1",
+            "ZNF208",
+            "ZNF532",
+            "CXXC5",
+            "GDI2",
+            "SIDT2",
+            "STX8",
+            "FLJ42627",
+            "SNORD96A",
+            "SOCS1",
+            "PORCN",
+            "NCAM1",
+            "DGCR6L",
+            "SPTLC2",
+            "RFX7",
+            "RGS10",
+            "MLXIP",
+            "EIF3J",
+            "PRPF18",
+            "PSPH",
+            "PIK3R6",
+            "APOD",
+            "TIMM23",
+            "ACP3",
+            "TMEM87A",
+            "RUNX2",
+            "SFT2D1",
+            "RTCA",
+            "KCNAB2",
+            "SLC6A19",
+            "GPR84",
+            "S100P",
+            "USP37",
+            "TVP23B",
+            "ZNFX1",
+            "PEPD",
+            "CYBRD1",
+            "EDRF1",
+            "GRK5",
+            "MAP2K1",
+            "VPS53",
+            "ERAP1",
+            "ARPP19",
+            "ATMIN",
+            "SKIL",
+            "ANKS1A",
+            "PLAGL2",
+            "CHORDC1",
+            "NANS",
+            "ZNF813",
+            "MIR212",
+            "TP53INP1",
+            "RAVER2",
+            "SDHAP1",
+            "ARHGAP9",
+            "OMG",
+            "ZCCHC9",
+            "CXCR3",
+            "IVD",
+            "C11orf54",
+            "PIK3C2A",
+            "MARCHF1",
+            "WIPI1",
+            "ARPIN-AP3S2",
+            "PMS2P1",
+            "PLXDC1",
+            "TANGO2",
+            "EI24",
+            "RAB3GAP2",
+            "MTCH1",
+            "ANKRD36B",
+            "TUBA1B",
+            "ABCA7",
+            "MORF4L2",
+            "SRXN1",
+            "SNORA41",
+            "KRTAP21-1",
+            "AK5",
+            "EEFSEC",
+            "SNORA75",
+            "TTLL3",
+            "FSHMD1A",
+            "ACO1",
+            "SMURF1",
+            "PARP6",
+            "RFC2",
+            "SNORA40",
+            "AMN",
+            "APOBEC3A",
+            "GOLGA8H",
+            "MRE11",
+            "DHX34",
+            "NBR2",
+            "RPL36AL",
+            "RNU4-1",
+            "DPH6",
+            "VOPP1",
+            "RHCE",
+            "KRTAP10-2",
+            "IL2RA",
+            "LDB1",
+            "GNB4",
+            "ERMP1",
+            "EIF4EBP2",
+            "HMGA2",
+            "RPS9",
+            "GNL3L",
+            "MCEMP1",
+            "FLVCR1",
+            "HBG1",
+            "DNAAF11",
+            "ZFAS1",
+            "SPRYD3",
+            "HSD17B7",
+            "GAB3",
+            "AFDN",
+            "ZNF354A",
+            "RPPH1",
+            "SND1",
+            "NUP210L",
+            "ISCA1",
+            "PRICKLE1",
+            "SNORA27",
+            "BST1",
+            "CD177",
+            "ANKRD11",
+            "TMEM176A",
+            "EEF2",
+            "PRKRA",
+            "BTN3A3",
+            "NDUFB6",
+            "F2RL1",
+            "PMS2P5",
+            "ALG8",
+            "HDC",
+            "AKTIP",
+            "SLMAP",
+            "AKAP6",
+            "KLHL5",
+            "TTC12",
+            "OCLN",
+            "NSD1",
+            "MMP25",
+            "CD101",
+            "DSE",
+            "MMP8",
+            "FAM27B",
+            "CEP76",
+            "LPCAT1",
+            "ELP2",
+            "PEAK1",
+            "SGSH",
+            "RPL23AP82",
+            "DNASE2",
+            "SRPK2",
+            "ALPK1",
+            "HAUS2",
+            "LRRC28",
+            "CD300LF",
+            "SNHG29",
+            "CALU",
+            "PGM5",
+            "TMTC1",
+            "MRPL47",
+            "SEPTIN14P20",
+            "ARL17A",
+            "MACO1",
+            "PLB1",
+            "FNDC3A",
+            "NCAPG2",
+            "SLC17A7",
+            "MDGA1",
+            "NXPE3",
+            "CD300C",
+            "ZNF486",
+            "KRT73",
+            "RNPC3",
+            "MMRN1",
+            "ZNF814",
+            "CDC42EP5",
+            "ZC2HC1A",
+            "RRM1",
+            "HNRNPUL2",
+            "HIPK3",
+            "C1QTNF4",
+            "SHTN1",
+            "BRD7",
+            "UHMK1",
+            "PGM5P2",
+            "PPDPF",
+            "SLC9A6",
+            "CXCL8",
+            "PWARSN",
+            "CUBN",
+            "NSUN2",
+            "TPST1",
+            "RNASE6",
+            "CD244",
+            "GTF2H1",
+            "FCER1A",
+            "DESI1",
+            "CKAP4",
+            "NIPAL2",
+            "TMTC4",
+            "ITPK1",
+            "NOD2",
+            "TMEM156",
+            "MGAT5",
+            "SNORD8",
+            "CLCN7",
+            "CENPC",
+            "MAFF",
+            "SSBP3",
+            "ID2",
+            "SAXO2",
+            "EIF3C",
+            "USP39",
+            "NDUFAF1",
+            "TBC1D7",
+            "RABGGTB",
+            "PVALB",
+            "LPIN2",
+            "HLA-J",
+            "CXCL16",
+            "RSPRY1",
+            "ZNF429",
+            "SHISA4",
+            "WDR70",
+            "RAP1A",
+            "NT5DC1",
+            "TBC1D9",
+            "PTPN1",
+            "GRHPR",
+            "EIF2AK3",
+            "CHD1L",
+            "POLR2E",
+            "VNN3P",
+            "ELP5",
+            "PDE8A",
+            "INTS3",
+            "MSL1",
+            "PIK3R1",
+            "MCTS1",
+            "RPS26P11",
+            "KCNJ4",
+            "OR52N4",
+            "SNORA15",
+            "WSB2",
+            "SHLD2",
+            "ZNF26",
+            "PABPC4",
+            "P2RY12",
+            "TIAM1",
+            "TMEM63C",
+            "MRRF",
+            "PRLH",
+            "YWHAH",
+            "ID1",
+            "TLR3",
+            "SLC38A11",
+            "MDM1",
+            "IER3",
+            "DNAJB1",
+            "ADGRG7",
+            "LTA4H",
+            "GASK1B",
+            "SEMA4B",
+            "TSHZ2",
+            "IGKV2D-26",
+            "IRAG2",
+            "ZBTB8OS",
+            "MZT2B",
+            "TAS2R31",
+            "ASPH",
+            "MROH6",
+            "YTHDC1",
+            "GSTO1",
+            "CAB39",
+            "EMB",
+            "THEM5",
+            "ARG1",
+            "TRANK1",
+            "MS4A2",
+            "PCBP1",
+            "THEM4",
+            "TRMT1",
+            "DTX2",
+            "ZNF641",
+            "CTU1",
+            "LY75",
+            "MBOAT1",
+            "CD40",
+            "ADPRM",
+            "ITPR3",
+            "IKBKB",
+            "LINC00268",
+            "PFN3",
+            "WARS1",
+            "ADGB",
+            "GGTA1",
+            "CAMK1D",
+            "ZNF706",
+            "SAC3D1",
+            "HADHA",
+            "DAPK1",
+            "NOP10",
+            "TACR3",
+            "LSMEM1",
+            "FAM185A",
+            "NFE2L1",
+            "ETHE1",
+            "MILR1",
+            "DUSP18",
+            "LAP3",
+            "MOSPD2",
+            "IMPA2",
+            "DTHD1",
+            "ARHGEF3",
+            "SLFN12",
+            "THUMPD3",
+            "PAPSS1",
+            "HEXIM1",
+            "PLP2",
+            "PTPN11",
+            "SMAD4",
+            "EIF4H",
+            "CTLA4",
+            "LINC00852",
+            "NTN1",
+            "GDE1",
+            "FBXW7",
+            "SRCAP",
+            "ZNF518B",
+            "ASGR2",
+            "ERP27",
+            "GUSBP15",
+            "LILRA4",
+            "H4C8",
+            "SCAMP1",
+            "RASAL3",
+            "IL12RB2",
+            "COA5",
+            "MRI1",
+            "NGDN",
+            "CYP1B1",
+            "SLC35E2B",
+            "CLDN7",
+            "MED7",
+            "GLMP",
+            "MT1F",
+            "SNAPC5",
+            "IWS1",
+            "ZFTRAF1",
+            "ACP5",
+            "TUBD1",
+            "ERICH1",
+            "SLC48A1",
+            "ZNF431",
+            "CD151",
+            "COMMD1",
+            "FRG1BP",
+            "BET1",
+            "CDK2AP2",
+            "RTN1",
+            "GPR15",
+            "MLKL",
+            "CKAP2",
+            "BNC2",
+            "DND1",
+            "PLG",
+            "CCDC59",
+            "RCOR1",
+            "AP4E1",
+            "GTF2H2",
+            "LPAR2",
+            "BCAP31",
+            "SLC35A4",
+            "ZNF275",
+            "POGZ",
+            "WDR5B",
+            "RBM12",
+            "CASP9",
+            "ZKSCAN1",
+            "STX2",
+            "COX2",
+            "CARD11",
+            "TMEM170A",
+            "PRICKLE3",
+            "SNORD35A",
+            "DDX11L2",
+            "RFPL4A",
+            "GPR132",
+            "ETV7",
+            "RPL23AP7",
+            "GSC2",
+            "C15orf39",
+            "DDX11",
+            "POLI",
+            "CA1",
+            "NKX3-1",
+            "BNIP3L",
+            "SNORD107",
+            "HIVEP2",
+            "CMBL",
+            "SH3GL1",
+            "JARID2",
+            "DDT",
+            "ZNF177",
+            "METTL6",
+            "CDC16",
+            "LYZ",
+            "CASP4",
+            "GYPE",
+            "SPRYD4",
+            "SNORD45B",
+            "PDE4DIP",
+            "OR52K3P",
+            "NEK6",
+            "PSMC5",
+            "TOR1AIP1",
+            "SULT1A2",
+            "ADNP",
+            "ABCD3",
+            "CD37",
+            "IDH1",
+            "RALGDS",
+            "FBP1",
+            "BRCA1",
+            "IGHV1OR15-9",
+            "H2BC20P",
+            "AOC3",
+            "VCPKMT",
+            "PPP6R1",
+            "MOB1B",
+            "PRR35",
+            "TPP2",
+            "RLIG1",
+            "ZFP90",
+            "OSBPL3",
+            "AVIL",
+            "LHFPL2",
+            "VSTM1",
+            "MIR223",
+            "SNORA16B",
+            "CLCN3",
+            "BID",
+            "ZNF445",
+            "IGLV6-57",
+            "TNFRSF1A",
+            "SNORD103A",
+            "MAP7",
+            "HSPA1L",
+            "AK2",
+            "PRPS1",
+            "RSU1",
+            "CDS2",
+            "FCRL6",
+            "ADHFE1",
+            "GPR137B",
+            "COIL",
+            "NECAP2",
+            "PRKCB",
+            "SCAPER",
+            "ASL",
+            "NUP153",
+            "PDXDC1",
+            "VTRNA1-1",
+            "VNN1",
+            "MRPS15",
+            "TLR7",
+            "PDZD4",
+            "SGMS1",
+            "PSD4",
+            "SNURF",
+            "TBC1D10C",
+            "PLVAP",
+            "SNORA20",
+            "GBGT1",
+            "U2AF1",
+            "LRRC37A4P",
+            "ZNF749",
+            "ANKRD20A11P",
+            "SEC61A1",
+            "SNORA38",
+            "FAM25A",
+            "MYLIP",
+            "TMED8",
+            "KATNBL1",
+            "MXRA7",
+            "ZNF816",
+            "SLC35E4",
+            "GYPB",
+            "MX2",
+            "MIR210",
+            "PPP1R12B",
+            "CLEC1B",
+            "UBE2K",
+            "ZNF683",
+            "PDK4",
+            "SLC35A5",
+            "DDX12P",
+            "HCFC1",
+            "SORD",
+            "YKT6",
+            "CYTB",
+            "EIF4E2",
+            "SUPT16H",
+            "ZNF559",
+            "CD80",
+            "NOMO1",
+            "GUSBP3",
+            "ALPL",
+            "ANKRD20A1",
+            "NUDT7",
+            "MAB21L3",
+            "EIF4E",
+            "GPR89B",
+            "SNORD62B",
+            "RHD",
+            "FAM25C",
+            "ARL17B",
+            "MAFIP",
+            "HBG2",
+            "DDX11L9",
+            "TRIM43B",
+            "GTF2H2B",
+            "NOMO2",
+            "SNORD103B",
+            "PLGLA",
+            "SDHAP2",
+            "DDTL"
+        ],
+        "Coefficient": [
+            3.1034389768669643,
+            -2.3598557591041147,
+            -2.327712667761737,
+            -2.254975249650579,
+            2.194848309803719,
+            -2.1060476181673455,
+            2.067221536558282,
+            2.008902706519641,
+            -1.9998497427007556,
+            1.9706539523626228,
+            -1.791561585703038,
+            -1.6878034594133207,
+            -1.6413560062900565,
+            -1.6009395723077144,
+            1.5776739632709007,
+            -1.5633294750371254,
+            1.5347786406674857,
+            1.5006732311300082,
+            -1.4794646611768758,
+            -1.4538806110481612,
+            -1.4489852855808496,
+            1.443879896344682,
+            -1.4252056314286057,
+            -1.4065298260159154,
+            -1.391738111278441,
+            -1.3813408487637346,
+            1.3629367175766376,
+            1.3627125739517967,
+            -1.361311096819218,
+            1.3463260891413793,
+            1.2933225111236426,
+            -1.2793775567419585,
+            1.2779369032263221,
+            -1.2712037795541578,
+            -1.2563008985695416,
+            -1.250764672500857,
+            1.2311469301923006,
+            -1.22382147194491,
+            1.2019026748665373,
+            -1.1776200571577031,
+            1.1715737304617158,
+            1.152552870780379,
+            1.1462391845267212,
+            -1.1422512250582288,
+            -1.1344507384000764,
+            -1.1260998515908638,
+            1.093438361619575,
+            -1.0880170235850213,
+            1.0877815143831222,
+            -1.0671086212681087,
+            1.0592720541332916,
+            -1.0552780618939765,
+            1.0549337264122842,
+            -1.053994902600405,
+            1.040877072623721,
+            1.0312605633465806,
+            -1.024799317234394,
+            1.018774246125695,
+            -1.014863221692147,
+            -1.0139400670362975,
+            0.9875969796696339,
+            0.9850512038075481,
+            -0.9846793497116905,
+            -0.9836791405005001,
+            0.9735884497679697,
+            0.9665684218526036,
+            0.9618846615200195,
+            -0.9547793581938456,
+            0.9502808961867261,
+            0.9483634145674684,
+            0.9460357129570093,
+            0.9389681039015373,
+            -0.9285540331347409,
+            -0.9267445373473978,
+            -0.9145791127098942,
+            0.9132725765614511,
+            0.9088394221808405,
+            -0.9083672000061536,
+            0.905919151379771,
+            0.9024580747772312,
+            0.8971285578227697,
+            -0.8970501913447501,
+            0.8879085768891034,
+            0.88272610395537,
+            0.8799123259081477,
+            0.8749309966181805,
+            -0.8684033258687338,
+            -0.8663745548676144,
+            -0.8651002790809488,
+            -0.8633396616497825,
+            0.8626193027827068,
+            0.8525784278792984,
+            -0.8521725600985308,
+            -0.848619420644615,
+            0.8458902555792125,
+            -0.8413165011458601,
+            0.8412439561779205,
+            0.8334204396051712,
+            -0.827689007356622,
+            -0.8224802297438668,
+            0.8216198987374713,
+            0.8206338745852078,
+            0.8197059274690566,
+            0.8106377253606932,
+            0.8069228021756805,
+            0.7951748148464208,
+            -0.7914196419696738,
+            -0.7857564580276472,
+            -0.7822800282930007,
+            0.777622722999135,
+            -0.7769617538316249,
+            -0.7724071802921386,
+            -0.771681532617407,
+            -0.7682280097261333,
+            0.7674152588659439,
+            0.7626231375129688,
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