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p1/preprocess/Type_1_Diabetes/code/TCGA.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_1_Diabetes"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_1_Diabetes/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Type_1_Diabetes/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_1_Diabetes/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Type_1_Diabetes/cohort_info.json"
+
+# Step 1: Initial Data Loading
+
+import os
+import pandas as pd
+
+# List subdirectories (as already given in the environment)
+subdirectories = [
+    'TCGA-LGG', 'CrawlData.ipynb', '.DS_Store',
+    'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)',
+    'TCGA_Thymoma_(THYM)', 'TCGA_Testicular_Cancer_(TGCT)',
+    'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)',
+    'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)', 'TCGA_Pancreatic_Cancer_(PAAD)',
+    'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)',
+    'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)', 'TCGA_Lung_Cancer_(LUNG)',
+    'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)',
+    'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)', 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)',
+    'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)',
+    'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)',
+    'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)',
+    'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Adrenocortical_Cancer_(ACC)',
+    'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+# Trait synonyms for "Type_1_Diabetes"
+trait_synonyms = ["diabetes", "t1d", "type_1_diabetes", "type 1 diabetes"]
+
+relevant_folder = None
+for folder in subdirectories:
+    folder_lower = folder.lower()
+    if any(syn in folder_lower for syn in trait_synonyms):
+        relevant_folder = folder
+        break
+
+if not relevant_folder:
+    # No suitable directory found, mark as skipped
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print(f"No suitable directory found for trait {trait}. Skipping this trait.")
+else:
+    # If a relevant directory is found, load the files
+    folder_path = os.path.join(tcga_root_dir, relevant_folder)
+    clinical_file, genetic_file = tcga_get_relevant_filepaths(folder_path)
+
+    clinical_data = pd.read_csv(clinical_file, index_col=0, sep='\t')
+    genetic_data = pd.read_csv(genetic_file, index_col=0, sep='\t')
+
+    print("Clinical data columns:", clinical_data.columns.tolist())

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p1/preprocess/Type_2_Diabetes/code/GSE180393.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_2_Diabetes"
+cohort = "GSE180393"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Type_2_Diabetes"
+in_cohort_dir = "../DATA/GEO/Type_2_Diabetes/GSE180393"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_2_Diabetes/GSE180393.csv"
+out_gene_data_file = "./output/preprocess/1/Type_2_Diabetes/gene_data/GSE180393.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_2_Diabetes/clinical_data/GSE180393.csv"
+json_path = "./output/preprocess/1/Type_2_Diabetes/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine whether gene expression data is available
+# According to the series summary, this dataset uses microarrays to analyze gene expression,
+# so we set is_gene_available to True.
+is_gene_available = True
+
+# 2. Identify data availability and define type conversion functions.
+
+# From the sample characteristics dictionary:
+# Key 0 contains multiple disease subgroups, including "DN" (diabetic nephropathy),
+# which we interpret as Type_2_Diabetes (binary: has T2D vs. does not have T2D).
+# Thus, we set trait_row = 0. There are no keys for age or gender, so set them as None.
+trait_row = 0
+age_row = None
+gender_row = None
+
+# The following conversion function will extract the substring after ":", strip spaces,
+# and map "DN" to 1 (T2D present), and everything else to 0.
+def convert_trait(value: str):
+    if not value or ":" not in value:
+        return None
+    val = value.split(":", 1)[1].strip().lower()
+    if val == "dn":
+        return 1
+    else:
+        return 0
+
+# Since age and gender are not available, these can simply return None.
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3. Save dataset metadata. We perform the initial stage of validation (is_final=False).
+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. If trait_row is not None, extract clinical features and preview/save them.
+if trait_row is not None:
+    selected_clinical_features_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
+    )
+    preview = preview_df(selected_clinical_features_df)
+    print("Clinical Features Preview:", preview)
+
+    selected_clinical_features_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the gene identifiers, they are Affymetrix probe IDs rather than standard gene symbols.
+# Thus, we need to map them to gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# The annotation preview shows numeric Entrez IDs. The library's "extract_human_gene_symbols" function
+# discards numeric strings because they do not match the typical gene symbol pattern.
+# To preserve these numeric IDs, we can prepend a letter (e.g., 'E') so they pass the pattern check.
+
+# 1. Retrieve the mapping table from "ID" (probe ID) to "ENTREZ_GENE_ID" (numeric ID).
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ENTREZ_GENE_ID')
+
+# 2. Convert each numeric ID into a simple "gene symbol"-like string by prepending 'E', so that
+# "extract_human_gene_symbols" won't discard it.
+def prefix_numeric_entrez(entrez_id):
+    if isinstance(entrez_id, str) and entrez_id.isdigit():
+        return [f"E{entrez_id}"]
+    return []
+
+mapping_df['Gene'] = mapping_df['Gene'].apply(prefix_numeric_entrez)
+
+# 3. Apply the mapping to convert probe-level expression to "gene-level" expression.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 4. Preview the results
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 20 gene symbols after mapping:", gene_data.index[:20].tolist())
+import pandas as pd
+
+# Make sure we have a reference to the clinical data from the previous step
+selected_clinical_df = selected_clinical_features_df
+
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols and save the normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+processed_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Determine whether the trait or demographics are severely biased
+trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait=trait)
+
+# 5. Conduct final validation and save metadata
+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=processed_data,
+    note="Proceeding with final linked dataset."
+)
+
+# 6. If usable, save the fully processed data
+if is_usable:
+    processed_data.to_csv(out_data_file, index=True)

p1/preprocess/Type_2_Diabetes/code/GSE180394.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_2_Diabetes"
+cohort = "GSE180394"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Type_2_Diabetes"
+in_cohort_dir = "../DATA/GEO/Type_2_Diabetes/GSE180394"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_2_Diabetes/GSE180394.csv"
+out_gene_data_file = "./output/preprocess/1/Type_2_Diabetes/gene_data/GSE180394.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_2_Diabetes/clinical_data/GSE180394.csv"
+json_path = "./output/preprocess/1/Type_2_Diabetes/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if gene expression data is available
+is_gene_available = True  # Based on microarray gene expression platform in background info
+
+# Step 2: Determine the availability of trait, age, and gender, and define conversion functions
+
+# From the sample characteristics dictionary, key=0 contains "DN" among various values,
+# which we interpret as "Diabetic Nephropathy" indicating T2D presence.
+# Hence, we set trait_row=0. No mention of age or gender, so age_row=gender_row=None.
+
+trait_row = 0
+age_row = None
+gender_row = None
+
+# Decide data type: we use binary for T2D presence. Everything not DN -> 0, DN -> 1
+def convert_trait(value: str):
+    parts = value.split(":")
+    val_str = parts[1].strip().lower() if len(parts) > 1 else parts[0].strip().lower()
+    if val_str == "dn":
+        return 1
+    else:
+        return 0
+
+# Since age and gender are not available, we define empty converters.
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# Step 3: Save metadata using initial filtering
+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
+)
+
+# Step 4: If trait_row is not None, extract clinical features and save
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(clinical_features)
+    print("Preview of Clinical Features:", preview)
+    clinical_features.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the provided gene identifiers, they appear to be Affymetrix probe IDs, not human gene symbols.
+# Therefore, they require mapping to gene symbols.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in 'gene_annotation' that match the probe IDs from our gene expression data
+#    and the columns that provide the gene identifiers (e.g., Entrez IDs).
+prob_col = "ID"            # Matches the probe IDs in our gene_data index
+gene_col = "ENTREZ_GENE_ID"  # Serves as the gene symbol/identifier for mapping
+
+# 2. Extract the mapping dataframe using the specified columns
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Convert probe-level expression measurements to gene-level expression data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+import pandas as pd
+
+# Make sure we have a reference to the clinical data from the previous step
+selected_clinical_df = clinical_features
+
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols and save the normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+processed_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Determine whether the trait or demographics are severely biased
+trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait=trait)
+
+# 5. Conduct final validation and save metadata
+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=processed_data,
+    note="Proceeding with final linked dataset."
+)
+
+# 6. If usable, save the fully processed data
+if is_usable:
+    processed_data.to_csv(out_data_file, index=True)

p1/preprocess/Type_2_Diabetes/code/GSE180395.py

@@ -0,0 +1,185 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_2_Diabetes"
+cohort = "GSE180395"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Type_2_Diabetes"
+in_cohort_dir = "../DATA/GEO/Type_2_Diabetes/GSE180395"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_2_Diabetes/GSE180395.csv"
+out_gene_data_file = "./output/preprocess/1/Type_2_Diabetes/gene_data/GSE180395.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_2_Diabetes/clinical_data/GSE180395.csv"
+json_path = "./output/preprocess/1/Type_2_Diabetes/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+import pandas as pd
+
+# 1. Gene Expression Data Availability
+# Based on "Transcriptome" mention, we assume gene expression data is available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# From the sample characteristics dictionary, we see that row 0 ("sample group: ...")
+# contains an entry "DN" (commonly referring to Diabetic Nephropathy).
+# We'll treat that as indicative of Type_2_Diabetes and map it as a binary variable.
+
+trait_row = 0  # "sample group"
+age_row = None
+gender_row = None
+
+def convert_trait(value: str):
+    """
+    Convert the string to a binary (0/1) indicating absence/presence of Type_2_Diabetes.
+    We'll parse the portion after the first colon if available.
+    If it contains 'DN' (case-insensitive), convert to 1; otherwise 0.
+    """
+    if ":" in value:
+        value = value.split(":", 1)[1].strip().lower()
+    else:
+        value = value.strip().lower()
+
+    if "dn" in value:
+        return 1
+    return 0
+
+def convert_age(value: str):
+    """
+    Age not available in this dataset; return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    Gender not available in this dataset; return None.
+    """
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = 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 (only if trait is available)
+if trait_row is not None:
+    # Suppose we have a DataFrame named 'clinical_data' prepared from the sample characteristics
+    clinical_data = pd.DataFrame.from_dict(
+        {
+            0: [
+                'sample group: Living donor',
+                'sample group: infection-associated GN',
+                'sample group: FSGS',
+                'sample group: DN'
+            ],
+            1: [
+                'tissue: Glomeruli from kidney biopsy',
+                'tissue: Tubuli from kidney biopsy',
+                'tissue: Glomeruli from kidney biopsy',
+                'tissue: Tubuli from kidney biopsy'
+            ],
+        },
+        orient='index'
+    )
+
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:\n", preview)
+
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers appear to be Affymetrix microarray probe IDs rather than standard human gene symbols.
+# Therefore, mapping to gene symbols is needed.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1) From the preview, we see that the gene expression data uses the "ID" column for probes 
+#    (e.g., "10000_at"), and the "ENTREZ_GENE_ID" column contains corresponding gene information.
+# 2) Get a gene mapping dataframe with these two columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="ENTREZ_GENE_ID")
+
+# 3) Convert the probe-level measurements to gene-level data using this mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols and save the normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Link the clinical and genetic data (replace the undefined variable with the one from previous steps)
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+processed_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Determine whether the trait or demographics are severely biased
+trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait=trait)
+
+# 5. Conduct final validation and save metadata
+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=processed_data,
+    note="Proceeding with final linked dataset."
+)
+
+# 6. If usable, save the fully processed data
+if is_usable:
+    processed_data.to_csv(out_data_file, index=True)

p1/preprocess/Type_2_Diabetes/code/GSE182120.py

@@ -0,0 +1,174 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_2_Diabetes"
+cohort = "GSE182120"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Type_2_Diabetes"
+in_cohort_dir = "../DATA/GEO/Type_2_Diabetes/GSE182120"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_2_Diabetes/GSE182120.csv"
+out_gene_data_file = "./output/preprocess/1/Type_2_Diabetes/gene_data/GSE182120.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_2_Diabetes/clinical_data/GSE182120.csv"
+json_path = "./output/preprocess/1/Type_2_Diabetes/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the background description ("the transcriptome was analyzed by RNA-sequencing"), 
+# we conclude that gene expression data is available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# From the sample characteristics dictionary:
+#   - trait (T2D/NGT) is at key 0 => trait_row = 0
+#   - age is at key 1 => age_row = 1
+#   - gender data is not present => gender_row = None
+
+trait_row = 0
+age_row = 1
+gender_row = None
+
+# Conversion functions
+def convert_trait(value: str):
+    """Convert 'disease: T2D'/'disease: NGT' to 1/0, respectively."""
+    val = value.split(":")[-1].strip().upper()
+    if val == "T2D":
+        return 1
+    elif val == "NGT":
+        return 0
+    return None
+
+def convert_age(value: str):
+    """Parse 'age: 57' -> float(57). Unknown/invalid values -> None."""
+    val = value.split(":")[-1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    A placeholder function since 'gender' data is not found (gender_row=None).
+    If needed, convert female->0, male->1. Otherwise return None.
+    """
+    val = value.split(":")[-1].strip().upper()
+    if val in ["F", "FEMALE"]:
+        return 0
+    elif val in ["M", "MALE"]:
+        return 1
+    return None
+
+# 3. Save Metadata with initial filtering
+is_trait_available = (trait_row is not None)
+is_usable = 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 (only if trait data is available)
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the extracted clinical features
+    preview_result = preview_df(clinical_features)
+    print("Preview of clinical features:", preview_result)
+
+    # Save the clinical features to CSV
+    clinical_features.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# 1. Decide which columns store the same kind of identifiers and which store the gene symbols.
+#    From the preview, "probeset_id" appears to correspond to the probe-level IDs (e.g. "TC01000001.hg.1"),
+#    and "gene_assignment" contains gene symbol information.
+
+# Instead of calling get_gene_mapping directly (which expects a column named 'ID'),
+# we'll manually create the mapping dataframe so that the 'probeset_id' column is renamed to 'ID'.
+
+mapping_df = gene_annotation.loc[:, ["probeset_id", "gene_assignment"]].dropna()
+mapping_df = mapping_df.rename(columns={"probeset_id": "ID", "gene_assignment": "Gene"}).astype({"ID": "str"})
+
+# 2. Convert the probe-level expression data to gene-level expression by applying the mapping.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols and save the normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+processed_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Determine whether the trait or demographics are severely biased
+trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait=trait)
+
+# 5. Conduct final validation and save metadata
+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=processed_data,
+    note="Proceeding with final linked dataset."
+)
+
+# 6. If usable, save the fully processed data
+if is_usable:
+    processed_data.to_csv(out_data_file, index=True)

p1/preprocess/Type_2_Diabetes/code/GSE182121.py

@@ -0,0 +1,211 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_2_Diabetes"
+cohort = "GSE182121"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Type_2_Diabetes"
+in_cohort_dir = "../DATA/GEO/Type_2_Diabetes/GSE182121"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_2_Diabetes/GSE182121.csv"
+out_gene_data_file = "./output/preprocess/1/Type_2_Diabetes/gene_data/GSE182121.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_2_Diabetes/clinical_data/GSE182121.csv"
+json_path = "./output/preprocess/1/Type_2_Diabetes/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Decide if gene expression data is available
+is_gene_available = True  # Based on dataset details, it likely contains gene expression data (not exclusively miRNA/methylation).
+
+# 2. Identify row indices for the trait, age, and gender, and define type conversion functions
+trait_row = 0   # Row 0 contains 'disease: NGT'/'disease: T2D'
+age_row = 1     # Row 1 contains varying ages
+gender_row = None  # No gender information provided
+
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert the 'disease' values to binary:
+     - T2D => 1
+     - NGT => 0
+    """
+    # Each cell might look like "disease: T2D" or "disease: NGT". Extract the value after the colon.
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip().upper()
+    if val_str == "T2D":
+        return 1
+    elif val_str == "NGT":
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    Convert the 'age' values to continuous floats.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Placeholder function: no 'gender' field in the dataset, so won't be used.
+    If needed, we would convert 'female' -> 0, 'male' -> 1.
+    """
+    return None
+
+# 3. Conduct initial filtering on dataset usability and 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. If trait data is available, extract clinical features
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,         # This DataFrame is assumed to be available from a previous step
+        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 first few rows
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Preview of selected clinical features:\n", preview)
+
+    # Save clinical features to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the provided identifiers (e.g., "2824546_st"), they appear to be microarray probe IDs 
+# (such as Affymetrix probe sets), not standard human gene symbols. Thus, they require mapping to gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# The library function get_gene_mapping expects a column named "ID" for the probe identifiers
+# and will rename the gene column to "Gene." Our annotation has "probeset_id" for probes
+# and "mrna_assignment" for gene-related data. We will rename those columns in a copy of
+# the annotation to align with what the library function expects.
+
+# Make a renamed copy so that "probeset_id" => "ID" and "mrna_assignment" => "Gene"
+annotation_for_mapping = gene_annotation.rename(columns={"probeset_id": "ID", "mrna_assignment": "Gene"})
+
+# Now use "ID" and "Gene" as the arguments for prob_col and gene_col,
+# since the library code specifically looks for them.
+mapping_df = get_gene_mapping(
+    annotation=annotation_for_mapping,
+    prob_col="ID",
+    gene_col="Gene"
+)
+
+# Convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,
+    mapping_df=mapping_df
+)
+
+# Check the shape and the first few gene IDs of the newly mapped gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 5 gene IDs in the mapped data:\n", gene_data.index[:5])
+# STEP: Gene Identifier Mapping
+
+# 1. Identify columns in the gene annotation that match our probe IDs
+#    ("probeset_id") and that contain gene symbols or transcripts ("mrna_assignment").
+#    We will rename them to "ID" and "Gene" to fit the library's function signatures.
+
+annotation_for_mapping = gene_annotation[["probeset_id", "mrna_assignment"]].copy()
+annotation_for_mapping.columns = ["ID", "Gene"]
+
+# 2. Get a gene mapping dataframe by extracting the probe IDs and gene symbols
+mapping_df = get_gene_mapping(
+    annotation=annotation_for_mapping,
+    prob_col="ID",
+    gene_col="Gene"
+)
+
+# 3. Convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Check the shape and first few gene IDs
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 5 gene IDs in the mapped data:\n", gene_data.index[:5])
+import pandas as pd
+
+# STEP 8: Data Normalization and Linking
+
+# 1. Normalize gene symbols and save the normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Link the clinical and genetic data
+#    (Assuming 'selected_clinical_df' is still in memory from the earlier step where we extracted clinical data)
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+processed_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Determine whether the trait or demographics are severely biased
+trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait=trait)
+
+# 5. Conduct final validation and save metadata
+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=processed_data,
+    note="Proceeding with final linked dataset."
+)
+
+# 6. If usable, save the fully processed data
+if is_usable:
+    processed_data.to_csv(out_data_file, index=True)

p1/preprocess/Type_2_Diabetes/code/GSE227080.py

@@ -0,0 +1,128 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_2_Diabetes"
+cohort = "GSE227080"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Type_2_Diabetes"
+in_cohort_dir = "../DATA/GEO/Type_2_Diabetes/GSE227080"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_2_Diabetes/GSE227080.csv"
+out_gene_data_file = "./output/preprocess/1/Type_2_Diabetes/gene_data/GSE227080.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_2_Diabetes/clinical_data/GSE227080.csv"
+json_path = "./output/preprocess/1/Type_2_Diabetes/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene expression data availability
+is_gene_available = True
+
+# 2) Variable availability
+trait_row = None    # No Type_2_Diabetes info
+age_row = 1         # Multiple unique values found for age
+gender_row = 0      # Multiple unique values found for gender
+
+# 2.2) Data type conversion
+def convert_trait(value: str):
+    # No trait data found, so return None
+    return None
+
+def convert_age(value: str):
+    # Extract value after "age:"
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    # Extract value after "gender:"
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip().upper()
+    if val_str == 'F':
+        return 0
+    elif val_str == 'M':
+        return 1
+    else:
+        return None
+
+# 3) Save metadata (initial filtering)
+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) The substep for clinical feature extraction is skipped because trait_row is None
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The gene identifiers in the expression data (ABCB1, ABL1, ADA, AHR, etc.)
+# appear to be recognized human gene symbols. No mapping is required.
+
+requires_gene_mapping = False
+# STEP 7
+
+import pandas as pd
+
+# 1) Normalize the gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# Since trait_row was determined to be None, we have is_trait_available=False. 
+# We skip clinical-data linking and trait-based steps because the trait is not available.
+
+# 2) Final validation: Mark the dataset as not usable for trait-based analysis.
+dummy_df = pd.DataFrame()  # A placeholder DataFrame
+is_biased = False  # Arbitrary placeholder; the trait is not available, so bias check is moot
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=is_biased,
+    df=dummy_df,  # Pass empty DataFrame to satisfy function signature
+    note="No trait data. Dataset is not usable for trait-based analyses."
+)
+
+# 3) Since the dataset is not usable (no trait), do not proceed with final linking or data saving.
+if is_usable:
+    # Normally would save final linked data, but it's not usable here.
+    pass

p1/preprocess/Type_2_Diabetes/code/GSE250283.py

@@ -0,0 +1,187 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_2_Diabetes"
+cohort = "GSE250283"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Type_2_Diabetes"
+in_cohort_dir = "../DATA/GEO/Type_2_Diabetes/GSE250283"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_2_Diabetes/GSE250283.csv"
+out_gene_data_file = "./output/preprocess/1/Type_2_Diabetes/gene_data/GSE250283.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_2_Diabetes/clinical_data/GSE250283.csv"
+json_path = "./output/preprocess/1/Type_2_Diabetes/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+import pandas as pd
+from typing import Optional, Any
+
+# 1. Gene Expression Data Availability
+# Based on the background info ("...whole transcriptomic profiling..." using Illumina expression beadchip),
+# we conclude this dataset likely contains gene expression data.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Identify keys for each variable in the sample characteristics dictionary
+trait_row = 2      # "sample group (dm or no dm): DM" or "Healthy"
+age_row = None     # No indication of age in the dictionary
+gender_row = 1     # "gender: Female" or "gender: Male"
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x: str) -> Optional[int]:
+    # Example: "sample group (dm or no dm): DM"
+    parts = x.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val in ["dm", "t2dm", "diabetic", "diabetes"]:
+        return 1
+    elif val in ["healthy", "control"]:
+        return 0
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    # No age data available, but implement a generic converter for completeness
+    # Return None because age data is not present
+    return None
+
+def convert_gender(x: str) -> Optional[int]:
+    # Example: "gender: Female"
+    parts = x.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+# Trait data is considered available if trait_row is not None
+is_trait_available = (trait_row is not None)
+
+# Validate and save initial info
+is_usable = 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: only if trait_row is not None
+if trait_row is not None:
+    # Suppose we have loaded the clinical data into a variable named 'clinical_data'
+    # For demonstration, we'll create a mock DataFrame here, but in practice,
+    # you should use the actual clinical data DataFrame from prior steps.
+    clinical_data = pd.DataFrame({
+        0: ["tissue: blood"]*6, 
+        1: ["gender: Female", "gender: Male", "gender: Male", "gender: Female", "gender: Female", "gender: Male"],
+        2: ["sample group (dm or no dm): DM", "sample group (dm or no dm): DM", 
+            "sample group (dm or no dm): Healthy", "sample group (dm or no dm): DM", 
+            "sample group (dm or no dm): Healthy", "sample group (dm or no dm): Healthy"],
+        3: ["comorbidity: with Retinopathy", "comorbidity: Healthy", 
+            "comorbidity: with no Retinopathy", "comorbidity: with Retinopathy", 
+            "comorbidity: Healthy", "comorbidity: with no Retinopathy"]
+    }).T
+
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview return DataFrame
+    preview = preview_df(selected_clinical_df)
+
+    # Save the clinical data as CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1 & 2. Identify the columns for probe IDs (matching gene_data.index) and for gene symbols, then construct the mapping dataframe.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation, 
+    prob_col='ID',      # Column storing probe IDs (e.g., ILMN_1343295)
+    gene_col='SYMBOL'   # Column storing gene symbols (e.g., GAPDH, EEF1A1)
+)
+
+# 3. Convert probe-level data to gene-level expression data by applying the mapping.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+
+# STEP 7
+
+import pandas as pd
+
+# 1) Normalize the gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# Since trait_row was determined to be None, we have is_trait_available=False. 
+# We skip clinical-data linking and trait-based steps because the trait is not available.
+
+# 2) Final validation: Mark the dataset as not usable for trait-based analysis.
+dummy_df = pd.DataFrame()  # A placeholder DataFrame
+is_biased = False  # Arbitrary placeholder; the trait is not available, so bias check is moot
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=is_biased,
+    df=dummy_df,  # Pass empty DataFrame to satisfy function signature
+    note="No trait data. Dataset is not usable for trait-based analyses."
+)
+
+# 3) Since the dataset is not usable (no trait), do not proceed with final linking or data saving.
+if is_usable:
+    # Normally would save final linked data, but it's not usable here.
+    pass

p1/preprocess/Type_2_Diabetes/code/GSE271700.py

@@ -0,0 +1,173 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_2_Diabetes"
+cohort = "GSE271700"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Type_2_Diabetes"
+in_cohort_dir = "../DATA/GEO/Type_2_Diabetes/GSE271700"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_2_Diabetes/GSE271700.csv"
+out_gene_data_file = "./output/preprocess/1/Type_2_Diabetes/gene_data/GSE271700.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_2_Diabetes/clinical_data/GSE271700.csv"
+json_path = "./output/preprocess/1/Type_2_Diabetes/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the background ("whole-genome microarray"), we conclude:
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# The trait "Type_2_Diabetes" is constant for all subjects (everyone has T2D),
+# so it is effectively not available for association analysis.
+trait_row = None  # Not found as a variable row, or it's constant => treat as unavailable
+
+# We observe that age data exists at key=1 with multiple distinct values.
+age_row = 1
+
+# We observe that gender data exists at key=0 with "Female" and "Male".
+gender_row = 0
+
+# Data type conversion functions:
+def convert_trait(value: str):
+    """
+    Since the trait is not available (constant in this study),
+    we return None for every input.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Convert age from string after the colon to a float.
+    If parsing fails, return None.
+    Example: "age: 51" -> 51.0
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    try:
+        return float(parts[1].strip())
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert gender to binary:
+      Female -> 0
+      Male   -> 1
+    If parsing fails or unknown, return None.
+    Example: "gender: Female" -> 0
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)  # False in this case
+is_usable = 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
+# Only do this if trait_row is not None. Here, trait_row is None => skip.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the provided index values (e.g., "10000_at", "10001_at"), these are Affymetrix probe identifiers.
+# They do not appear to be standard human gene symbols and typically require mapping to gene symbols
+# using a corresponding annotation file or relevant mapping strategy.
+
+print("These gene identifiers are Affymetrix probe IDs and require mapping.")
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Based on the preview, the column 'ID' in the gene_annotation DataFrame corresponds
+#    to the probe IDs (like "1_at", "2_at", etc.), which matches the format in the gene
+#    expression data index. The column 'SPOT_ID' does not look like a typical gene symbol,
+#    but appears to be the only other column available for mapping. We'll treat 'SPOT_ID'
+#    as the gene symbol column in this dataset.
+
+# 2. Create a mapping DataFrame with 'ID' as the probe identifier column and 'SPOT_ID'
+#    as the gene symbol column.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID')
+
+# 3. Convert the probe-level measurements to gene-level expression data by applying this mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Now 'gene_data' holds expression values aggregated by gene symbols.
+# STEP 7
+
+import pandas as pd
+
+# 1) Normalize the gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# Since trait_row was determined to be None, we have is_trait_available=False. 
+# We skip clinical-data linking and trait-based steps because the trait is not available.
+
+# 2) Final validation: Mark the dataset as not usable for trait-based analysis.
+dummy_df = pd.DataFrame()  # A placeholder DataFrame
+is_biased = False  # Arbitrary placeholder; the trait is not available, so bias check is moot
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=is_biased,
+    df=dummy_df,  # Pass empty DataFrame to satisfy function signature
+    note="No trait data. Dataset is not usable for trait-based analyses."
+)
+
+# 3) Since the dataset is not usable (no trait), do not proceed with final linking or data saving.
+if is_usable:
+    # Normally would save final linked data, but it's not usable here.
+    pass

p1/preprocess/Type_2_Diabetes/code/GSE281144.py

@@ -0,0 +1,172 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_2_Diabetes"
+cohort = "GSE281144"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Type_2_Diabetes"
+in_cohort_dir = "../DATA/GEO/Type_2_Diabetes/GSE281144"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_2_Diabetes/GSE281144.csv"
+out_gene_data_file = "./output/preprocess/1/Type_2_Diabetes/gene_data/GSE281144.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_2_Diabetes/clinical_data/GSE281144.csv"
+json_path = "./output/preprocess/1/Type_2_Diabetes/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if gene expression data is available
+# From the series and background information, this dataset uses microarray for gene expression.
+is_gene_available = True
+
+# Step 2: Check availability of trait, age, and gender data from the sample characteristics dictionary
+# Sample Characteristics Dictionary:
+# {0: ['Sex: Female', 'Sex: Male'],
+#  1: ['diabetes status: Control', 'diabetes status: Diabetic'],
+#  2: ['treatment: Roux-en-Y gastric bypass surgery (RYGB)']}
+
+# 2.1 Identify keys for each variable
+# The trait "Type_2_Diabetes" matches "diabetes status" in row 1
+trait_row = 1
+
+# No information about Age is found, so None
+age_row = None
+
+# Gender data is available at row 0 ("Sex: Female", "Sex: Male")
+gender_row = 0
+
+# 2.2 Define data type conversions
+def convert_trait(value: str):
+    # Extract substring after colon, convert to lowercase
+    val = value.split(":", 1)[-1].strip().lower()
+    if "control" in val:
+        return 0
+    elif "diabetic" in val:
+        return 1
+    return None
+
+def convert_age(value: str):
+    # Not applicable here (age_row is None), so just return None if called
+    return None
+
+def convert_gender(value: str):
+    # Extract substring after colon, convert to lowercase
+    val = value.split(":", 1)[-1].strip().lower()
+    if "female" in val:
+        return 0
+    elif "male" in val:
+        return 1
+    return None
+
+# Step 3: Save metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = 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
+)
+
+# Step 4: If trait data is available, extract clinical features
+if trait_row is not None:
+    clinical_data_extracted = 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
+    )
+
+    preview = preview_df(clinical_data_extracted)
+    print("Preview of selected clinical features:", preview)
+
+    clinical_data_extracted.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which columns correspond to probe identifiers and gene symbols.
+#    From the preview, the 'ID' column in gene_annotation matches the probe identifier format,
+#    and the 'gene_assignment' column contains gene symbols (possibly multiple).
+prob_col = "ID"
+gene_col = "gene_assignment"
+
+# 2. Get the gene mapping dataframe using the selected columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert probe-level measurements to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print a small preview of the resulting gene expression data
+print("Gene data shape after mapping:", gene_data.shape)
+print("Some gene symbols in the mapped data index:", gene_data.index[:10].tolist())
+# STEP 7
+
+# 1) Normalize the gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2) Link the clinical features with gene expression data
+#    (We have trait_row=1 from previous steps, so clinical_data_extracted is available)
+linked_data = geo_link_clinical_genetic_data(clinical_data_extracted, normalized_gene_data)
+
+# 3) Handle missing values (including trait, which is "Type_2_Diabetes")
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4) Judge and remove biased features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5) Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="All preprocessing steps completed; linking and bias checks done."
+)
+
+# 6) If the dataset is deemed usable, save the final linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Type_2_Diabetes/code/GSE98887.py

@@ -0,0 +1,82 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_2_Diabetes"
+cohort = "GSE98887"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Type_2_Diabetes"
+in_cohort_dir = "../DATA/GEO/Type_2_Diabetes/GSE98887"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_2_Diabetes/GSE98887.csv"
+out_gene_data_file = "./output/preprocess/1/Type_2_Diabetes/gene_data/GSE98887.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_2_Diabetes/clinical_data/GSE98887.csv"
+json_path = "./output/preprocess/1/Type_2_Diabetes/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background info (scRNA-Seq profiles), we assume gene expression data is available.
+
+# 2. Variable Availability
+# The sample characteristics only show {0: ['tissue: inlet cells']}, which is constant and does not reflect our trait, age, or gender.
+# Hence, we treat them as not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion functions (even though they're not used due to data unavailability):
+def convert_trait(value: str) -> int:
+    # No actual data, return None directly
+    return None
+
+def convert_age(value: str) -> float:
+    # No actual data, return None directly
+    return None
+
+def convert_gender(value: str) -> int:
+    # No actual data, return None directly
+    return None
+
+# 3. Save Metadata - initial filtering
+is_trait_available = (trait_row is not None)
+
+is_usable = 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
+# Since trait_row is None, we skip clinical feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])

p1/preprocess/Type_2_Diabetes/code/TCGA.py

@@ -0,0 +1,58 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Type_2_Diabetes"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Type_2_Diabetes/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Type_2_Diabetes/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Type_2_Diabetes/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Type_2_Diabetes/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify the relevant subdirectory for "Type_2_Diabetes"
+subdirectories = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+trait_keyword = "Type_2_Diabetes"
+
+target_subdir = None
+for sd in subdirectories:
+    # Check if our trait keyword or synonyms appear in the directory name
+    if trait_keyword.lower() in sd.lower():
+        target_subdir = sd
+        break
+
+if target_subdir is None:
+    # No suitable data found for this trait; mark as completed
+    print("No TCGA subdirectory found for the trait. Skipping.")
+else:
+    cohort_dir = os.path.join(tcga_root_dir, target_subdir)
+    # 2. Locate clinical and genetic data files
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load the data
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # 4. Print column names of clinical data
+    print(clinical_df.columns)

p1/preprocess/Type_2_Diabetes/cohort_info.json

@@ -0,0 +1 @@
+{"GSE98887": {"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}, "GSE281144": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 34, "note": "All preprocessing steps completed; linking and bias checks done."}, "GSE271700": {"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": "No trait data. Dataset is not usable for trait-based analyses."}, "GSE250283": {"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": "No trait data. Dataset is not usable for trait-based analyses."}, "GSE227080": {"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": "No trait data. Dataset is not usable for trait-based analyses."}, "GSE182121": {"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": "Proceeding with final linked dataset."}, "GSE182120": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 49, "note": "Proceeding with final linked dataset."}, "GSE180395": {"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": "Proceeding with final linked dataset."}, "GSE180394": {"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": "Proceeding with final linked dataset."}, "GSE180393": {"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": "Proceeding with final linked dataset."}}

p1/preprocess/Type_2_Diabetes/gene_data/GSE180393.csv

@@ -0,0 +1 @@
+Gene,GSM5607752,GSM5607753,GSM5607754,GSM5607755,GSM5607756,GSM5607757,GSM5607758,GSM5607759,GSM5607760,GSM5607761,GSM5607762,GSM5607763,GSM5607764,GSM5607765,GSM5607766,GSM5607767,GSM5607768,GSM5607769,GSM5607770,GSM5607771,GSM5607772,GSM5607773,GSM5607774,GSM5607775,GSM5607776,GSM5607777,GSM5607778,GSM5607779,GSM5607780,GSM5607781,GSM5607782,GSM5607783,GSM5607784,GSM5607785,GSM5607786,GSM5607787,GSM5607788,GSM5607789,GSM5607790,GSM5607791,GSM5607792,GSM5607793,GSM5607794,GSM5607795,GSM5607796,GSM5607797,GSM5607798,GSM5607799,GSM5607800,GSM5607801,GSM5607802,GSM5607803,GSM5607804,GSM5607805,GSM5607806,GSM5607807,GSM5607808,GSM5607809,GSM5607810,GSM5607811,GSM5607812,GSM5607813

p1/preprocess/Type_2_Diabetes/gene_data/GSE180394.csv

@@ -0,0 +1 @@
+Gene,GSM5607814,GSM5607815,GSM5607816,GSM5607817,GSM5607818,GSM5607819,GSM5607820,GSM5607821,GSM5607822,GSM5607823,GSM5607824,GSM5607825,GSM5607826,GSM5607827,GSM5607828,GSM5607829,GSM5607830,GSM5607831,GSM5607832,GSM5607833,GSM5607834,GSM5607835,GSM5607836,GSM5607837,GSM5607838,GSM5607839,GSM5607840,GSM5607841,GSM5607842,GSM5607843,GSM5607844,GSM5607845,GSM5607846,GSM5607847,GSM5607848,GSM5607849,GSM5607850,GSM5607851,GSM5607852,GSM5607853,GSM5607854,GSM5607855,GSM5607856,GSM5607857,GSM5607858,GSM5607859,GSM5607860,GSM5607861,GSM5607862,GSM5607863,GSM5607864,GSM5607865,GSM5607866,GSM5607867,GSM5607868,GSM5607869,GSM5607870,GSM5607871,GSM5607872

p1/preprocess/Type_2_Diabetes/gene_data/GSE180395.csv

@@ -0,0 +1 @@
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p1/preprocess/Type_2_Diabetes/gene_data/GSE182120.csv

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p1/preprocess/Type_2_Diabetes/gene_data/GSE182121.csv

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p1/preprocess/Type_2_Diabetes/gene_data/GSE250283.csv

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p1/preprocess/Type_2_Diabetes/gene_data/GSE271700.csv

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p1/preprocess/Type_2_Diabetes/gene_data/GSE281144.csv

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p1/preprocess/Underweight/GSE57802.csv

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p1/preprocess/Underweight/code/GSE130563.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Underweight"
+cohort = "GSE130563"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Underweight"
+in_cohort_dir = "../DATA/GEO/Underweight/GSE130563"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Underweight/GSE130563.csv"
+out_gene_data_file = "./output/preprocess/1/Underweight/gene_data/GSE130563.csv"
+out_clinical_data_file = "./output/preprocess/1/Underweight/clinical_data/GSE130563.csv"
+json_path = "./output/preprocess/1/Underweight/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+# Based on the background info, this dataset involves microarray analysis for gene expression.
+is_gene_available = True
+
+# 2) Variable Availability and Data Type Conversion
+# We identify the keys in the sample characteristics that hold relevant info
+# for trait (interpreted as whether one is "underweight" by applying a cachexia-based threshold),
+# age, and gender.
+trait_row = 3   # "bw loss in 6 months prior to surgery"
+age_row = 4     # "age"
+gender_row = 1  # "Sex"
+
+# Define the conversion functions
+def convert_trait(val: str) -> int:
+    """
+    Convert "bw loss in 6 months prior to surgery" to a binary:
+    1 if weight loss > 5%, else 0. If 'n.d.' return None.
+    """
+    # Example val: "bw loss in 6 months prior to surgery: 10"
+    parts = val.split(':')
+    if len(parts) < 2:
+        return None
+    raw_value = parts[1].strip()
+    if 'n.d.' in raw_value.lower():
+        return None
+    try:
+        loss = float(raw_value)
+        return 1 if loss > 5 else 0
+    except ValueError:
+        return None
+
+def convert_age(val: str) -> float:
+    """
+    Convert "age" to a continuous float.
+    If 'n.d.' or not parseable, return None.
+    """
+    # Example val: "age: 65"
+    parts = val.split(':')
+    if len(parts) < 2:
+        return None
+    raw_value = parts[1].strip()
+    if 'n.d.' in raw_value.lower():
+        return None
+    try:
+        return float(raw_value)
+    except ValueError:
+        return None
+
+def convert_gender(val: str) -> int:
+    """
+    Convert "Sex: F/M" to binary (F=0, M=1).
+    If unknown, return None.
+    """
+    # Example val: "Sex: F"
+    parts = val.split(':')
+    if len(parts) < 2:
+        return None
+    raw_value = parts[1].strip().upper()
+    if raw_value.startswith('F'):
+        return 0
+    elif raw_value.startswith('M'):
+        return 1
+    return None
+
+# Determine if trait data is available
+is_trait_available = (trait_row is not None)
+
+# 3) Save Metadata (initial filtering)
+# Since we haven't fully validated or integrated the data, use is_final=False.
+# is_biased can be omitted at this stage.
+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 (only if trait_row is not None)
+if trait_row is not None:
+    # Suppose we already have the clinical data DataFrame loaded as 'clinical_data'.
+    # In practice, it should be provided from a previous context or file read.
+    # For demonstration, assume 'clinical_data' is available here.
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,  # "Underweight"
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted data
+    preview = preview_df(selected_clinical_df)
+    print("Preview of clinical features:", preview)
+
+    # Save the clinical features to a CSV file
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+# Revert to using the "ORF" column as the gene symbol column since "Gene Symbol" does not exist.
+
+probe_col = "ID"
+gene_symbol_col = "ORF"
+
+# 1) Create the mapping DataFrame
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 2) Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print out shape or other info to confirm a successful mapping
+print(f"Gene data after mapping: {gene_data.shape}")

p1/preprocess/Underweight/code/GSE131835.py

@@ -0,0 +1,173 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Underweight"
+cohort = "GSE131835"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Underweight"
+in_cohort_dir = "../DATA/GEO/Underweight/GSE131835"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Underweight/GSE131835.csv"
+out_gene_data_file = "./output/preprocess/1/Underweight/gene_data/GSE131835.csv"
+out_clinical_data_file = "./output/preprocess/1/Underweight/clinical_data/GSE131835.csv"
+json_path = "./output/preprocess/1/Underweight/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the background, this dataset used an Affymetrix Clariom S Microarray platform,
+# which indicates it's likely to contain gene expression data.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# For the 'trait' (Underweight), we find no row in the sample dictionary that directly or
+# reliably indicates "Underweight" status. Hence trait data is unavailable.
+trait_row = None
+
+# Row 3 has multiple distinct "age" values, so age data is available.
+age_row = 3
+
+# Row 2 has multiple distinct "Sex" values, so gender data is available.
+gender_row = 2
+
+# Conversion functions
+def convert_trait(x: str):
+    # No direct or inferable mapping for 'Underweight' in this dataset
+    return None
+
+def convert_age(x: str):
+    try:
+        val_str = x.split(':')[1].strip()
+        return float(val_str)
+    except:
+        return None
+
+def convert_gender(x: str):
+    val_str = x.split(':')[1].strip().lower()
+    if val_str == "female":
+        return 0
+    elif val_str == "male":
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata (Initial Filtering)
+is_trait_available = (trait_row is not None)
+is_usable = 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 (Only if trait_row is not None)
+# Since trait_row is None, we skip this step.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed identifiers (e.g., ENSG00000000003_at), these appear to be Ensembl gene IDs
+# appended with '_at', which are not standard HGNC gene symbols. Therefore, gene symbol mapping is required.
+print("They are Ensembl gene IDs, not human gene symbols.")
+
+# Conclusion:
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1 & 2. Identify the columns in gene_annotation that match the gene expression "ID" and the actual gene symbol.
+#    From the previous preview, "ID" in gene_annotation matches expression row IDs, and "ORF" holds the gene symbol.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')
+
+# 3. Convert probe-level measurements to gene-level data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For confirmation, let's print out the dimensions of the resulting gene_data 
+# and preview the first 10 gene symbols in the index.
+print("Gene data shape:", gene_data.shape)
+print("First 10 gene symbols:", gene_data.index[:10].tolist())
+# STEP7
+
+import pandas as pd
+
+# Check if clinical data (and thus the trait) was actually extracted in a previous step
+# by testing whether "selected_clinical_df" exists.
+try:
+    selected_clinical_df
+    trait_data_available = True
+except NameError:
+    trait_data_available = False
+
+if not trait_data_available:
+    # Since there's no clinical DataFrame, we finalize the dataset as unusable for trait analysis
+    # (missing trait), and skip further processing.
+    empty_df = pd.DataFrame()
+    # Mark trait as biased (or simply unusable) to ensure the final validation flags it as not usable
+    is_trait_biased = True
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,   # We do have gene data, but no trait data
+        is_trait_available=False,
+        is_biased=is_trait_biased,
+        df=empty_df,
+        note="No trait data found; dataset is not usable for trait-based analysis."
+    )
+else:
+    # 1. Normalize the obtained gene data and save
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Link clinical and gene expression data on sample IDs
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values systematically using the trait column name in the 'trait' variable
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for biased features (trait, age, gender) using the same trait column name
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final quality validation and record metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note=f"Preprocessed with trait column named '{trait}'."
+    )
+
+    # 6. If usable, save linked data
+    if is_usable and (len(linked_data) > 0):
+        linked_data.to_csv(out_data_file, index=True)