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 p1/preprocess/Underweight/GSE57802.csv filter=lfs diff=lfs merge=lfs -text
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p1/preprocess/Acute_Myeloid_Leukemia/clinical_data/TCGA.csv

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p1/preprocess/Acute_Myeloid_Leukemia/code/GSE121291.py

@@ -0,0 +1,155 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE121291"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE121291"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/GSE121291.csv"
+out_gene_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/gene_data/GSE121291.csv"
+out_clinical_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/clinical_data/GSE121291.csv"
+json_path = "./output/preprocess/1/Acute_Myeloid_Leukemia/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
+# Based on the background information indicating "microarray mRNA", we consider gene data to be available. 
+is_gene_available = True
+
+# Step 2: Identify rows for each variable and define conversion functions
+
+# Observing the sample characteristics dictionary:
+#   0 => ['disease state: Acute Myeloid Leukemia']
+#   1 => ['cell line: AML cell line  THP-1']
+#   2 => ['agent: DMSO', 'agent: SY-1365', 'agent: JQ1', 'agent: NVP2', 'agent: FLAVO']
+#   3 => ['time: 2 hours', 'time: 6 hours']
+# All samples have the same disease state, so there is no variability for the trait.
+# No keys indicate age or gender.
+trait_row = None
+age_row = None
+gender_row = None
+
+def convert_trait(value: str) -> int:
+    # Not applicable here since trait data is not available
+    return None
+
+def convert_age(value: str) -> float:
+    # Not applicable here since age data is not available
+    return None
+
+def convert_gender(value: str) -> int:
+    # Not applicable here since gender data is not available
+    return None
+
+# Step 3: Save initial metadata using the library function
+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: 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])
+# Based on the observed Affymetrix probe set IDs (e.g., 11715100_at), these are not standard gene symbols.
+# Therefore, they need to be mapped to human gene symbols.
+print("\nrequires_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 annotation matches the probe identifiers (e.g., '11715100_at'),
+#    and "Gene Symbol" is the column containing the gene symbols (e.g., 'HIST1H3G').
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 2. Convert probe-level measurements to gene-level by applying the mapping.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# For inspection, let's check the shape and a few index entries of the resulting dataframe:
+print("Mapped gene expression data shape:", gene_data.shape)
+print("First 10 gene symbols in the mapped data:", gene_data.index[:10].tolist())
+# STEP 7
+
+# 1. Normalize gene symbols in the gene_data, then save to CSV
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Since trait data is not available, skip linking and other steps that depend on the trait.
+#    We only perform final validation indicating that the trait is not available.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    note="Gene expression data available, but no trait data. Skipping final linking step."
+)
+
+# 3. Because trait is unavailable, no final linked data is saved.
+# STEP 8
+
+# 1. Normalize gene symbols in the gene_data, then save to CSV.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Since we have no clinical data (trait_row=None) and no trait information,
+# we cannot link clinical data or perform trait-based missing-value handling or bias checks.
+
+# 2. Perform final validation indicating no trait is available.
+# Per the library's requirement, we must still provide a DataFrame and an is_biased flag.
+# Because is_trait_available=False, the dataset will not be marked usable regardless of is_biased value.
+df_for_validation = normalized_gene_data.copy()
+is_biased = False  # Arbitrary since there's no trait.
+
+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=df_for_validation,
+    note="Gene expression data present, but no trait data. Cannot perform linking or trait-based QC."
+)
+
+# 3. If the dataset is not usable (due to no trait), we skip saving any final linked data.
+if is_usable:
+    df_for_validation.to_csv(out_data_file)

p1/preprocess/Acute_Myeloid_Leukemia/code/GSE121431.py

@@ -0,0 +1,128 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE121431"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE121431"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/GSE121431.csv"
+out_gene_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/gene_data/GSE121431.csv"
+out_clinical_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/clinical_data/GSE121431.csv"
+json_path = "./output/preprocess/1/Acute_Myeloid_Leukemia/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 whether gene expression data is available
+is_gene_available = True  # Based on dataset background suggesting gene expression profiling
+
+# 2. Identify rows for trait, age, and gender and define conversion functions
+
+# The sample characteristics show only one disease state (AML) for all samples,
+# and there is no mention of age or gender. Thus, for association studies,
+# none of these variables are available (they are either constant or absent).
+trait_row = None
+age_row = None
+gender_row = None
+
+def convert_trait(value: str) -> Optional[int]:
+    # This dataset has no varying trait information. Returning None for all.
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    # No age data available. Returning None for all.
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # No gender data available. Returning None for all.
+    return None
+
+# 3. Save metadata (initial filtering)
+is_trait_available = (trait_row is not None)  # False in this case
+
+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. Since trait_row is None, we do not extract the clinical features
+# and thus skip the substep of saving clinical data.
+# 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 given identifiers (e.g., '11715100_at', '11715101_s_at', etc.), 
+# they appear to be Affymetrix probe IDs rather than human gene symbols.
+# They will require mapping to standard gene symbols.
+
+print("These IDs look like Affymetrix microarray probe IDs.")
+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. Identify the columns in the annotation dataframe:
+#    - 'ID' matches the probe IDs found in our gene_data index
+#    - 'Gene Symbol' contains the gene symbols needed for mapping
+
+# 2. Get the gene mapping dataframe using the library function
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Quick check of the shape of the resulting gene_data
+print(f"Mapped gene_data shape: {gene_data.shape}")
+# STEP 7
+
+# 1. Normalize gene symbols in the gene_data, then save to CSV.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Since trait data is unavailable (trait_row was None in an earlier step),
+# we cannot do trait-based linking, missing value handling, or bias checks.
+# Instead, we record this dataset with partial validation: is_final=False.
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,   # There is gene data
+    is_trait_available=False  # No trait data
+)
+
+# Because no trait data is available, we do not attempt to generate or save any final linked dataset.

p1/preprocess/Acute_Myeloid_Leukemia/code/GSE161532.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE161532"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE161532"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/GSE161532.csv"
+out_gene_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/gene_data/GSE161532.csv"
+out_clinical_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/clinical_data/GSE161532.csv"
+json_path = "./output/preprocess/1/Acute_Myeloid_Leukemia/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 if the dataset contains gene expression data
+is_gene_available = True  # Affymetrix Human Transcriptome Array 2.0 suggests gene expression data
+
+# 2) Identify data availability for trait, age, gender
+#    The dataset only has AML samples (no variation), so trait is effectively not available.
+#    Age data is in row 1 and gender data is in row 2, each having more than one unique value.
+trait_row = None
+age_row = 1
+gender_row = 2
+
+# 2.2) Define conversion functions for trait, age, and gender
+
+def convert_trait(value: str):
+    """
+    Convert trait (AML) data to a binary indicator if variation existed.
+    But effectively not used since trait_row is None.
+    """
+    try:
+        val = value.split(":", 1)[1].strip().lower()
+        # If it contained 'aml', would return 1, else 0
+        return 1 if "aml" in val else 0
+    except:
+        return None
+
+def convert_age(value: str):
+    """
+    Convert the 'age: X' format to an integer if possible, else None.
+    """
+    try:
+        val = value.split(":", 1)[1].strip().lower()
+        return int(val) if val.isdigit() else None
+    except:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert 'gender: Female' or 'gender: Male' to binary (0 for Female, 1 for Male).
+    """
+    try:
+        val = value.split(":", 1)[1].strip().lower()
+        if val == "female":
+            return 0
+        elif val == "male":
+            return 1
+        else:
+            return None
+    except:
+        return None
+
+# 3) Initial filtering on dataset usability based on gene/trait availability
+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) Skip clinical feature extraction since trait_row is None (trait not available)
+# 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 pattern ("xxxxx_st"), these are likely Affymetrix probe set IDs, 
+# which are not standard human gene symbols and 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))
+# STEP6: Gene Identifier Mapping
+
+# 1. Decide which columns in the annotation correspond to the expression IDs and gene symbols
+#    From the preview, 'ID' appears to hold probe identifiers like "TC01000001.hg.1" 
+#    and 'gene_assignment' has the string containing possible gene symbols.
+prob_col = "ID"
+gene_col = "gene_assignment"
+
+# 2. Get the gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For confirmation, let's check some aspects of the mapped gene data
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 20 gene symbols in mapped data:", list(gene_data.index[:20]))
+# STEP 7
+
+# Since in a previous step we determined trait_row = None, this means the trait is not available for this dataset.
+# Hence, we cannot link clinical and genetic data or proceed with trait-based analyses. We only normalize the gene data,
+# then finalize the metadata to record that the dataset is not trait-usable.
+
+# 1. Normalize gene symbols in the gene expression data, then save to CSV
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Gene data normalized and saved to: {out_gene_data_file}")
+
+# 2. Prepare to finalize the metadata for a dataset lacking trait data
+#    Provide an empty DataFrame and set is_biased to False to satisfy the function signature
+empty_df = pd.DataFrame()
+is_biased = False
+
+# 3. Perform final validation indicating trait is unavailable
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,       # Gene data is present
+    is_trait_available=False,     # Trait is unavailable as determined
+    is_biased=is_biased,          # Required by function signature
+    df=empty_df,                  # We have no trait-based linked data
+    note="No trait data. Clinical linking skipped."
+)
+
+# 4. Since the dataset is not trait-usable, we do not save any linked data file.

p1/preprocess/Acute_Myeloid_Leukemia/code/GSE222124.py

@@ -0,0 +1,153 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE222124"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222124"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/GSE222124.csv"
+out_gene_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/gene_data/GSE222124.csv"
+out_clinical_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/clinical_data/GSE222124.csv"
+json_path = "./output/preprocess/1/Acute_Myeloid_Leukemia/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 series title mentioning "Gene expression alterations"
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics dictionary:
+#  0: ['cell type: T cell leukemia', 'cell type: Acute monocytic leukemia monocyte', 'cell type: Natural killer cell leukemia']
+# There are three distinct values; one of them (“Acute monocytic leukemia monocyte”) is closely related to AML.
+# We will thus use row 0 as the binary trait indicator (AML vs. not AML).
+trait_row = 0  # Because it includes "Acute monocytic leukemia monocyte" (subtype of AML)
+age_row = None  # No explicit or inferable age mention
+gender_row = None  # No explicit or inferable gender mention
+
+def convert_trait(value: str):
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Map 'acute monocytic leukemia monocyte' to 1 for AML, others to 0
+    if val == 'acute monocytic leukemia monocyte':
+        return 1
+    elif val in ['t cell leukemia', 'natural killer cell leukemia']:
+        return 0
+    else:
+        return None
+
+# No age or gender data, so we define stub converters returning None
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3. Initial filtering and saving metadata
+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:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview
+    print("Preview of selected clinical data:", preview_df(selected_clinical_df, n=5))
+    # Save
+    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 given gene identifiers (Affymetrix probe set IDs), they are not human gene symbols,
+# so 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))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify the columns in 'gene_annotation' that match the gene expression data identifiers (probe IDs)
+#    and the columns that contain actual gene symbols. From the preview, we see 'ID' matches the probe IDs
+#    and 'Gene Symbol' corresponds to the gene symbols.
+probe_col = 'ID'
+symbol_col = 'Gene Symbol'
+
+# 2. Create a mapping dataframe for probe-to-gene
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3. Convert the probe-level measurements in 'gene_data' to gene-level data using the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7
+
+# 1. Normalize gene symbols in the gene_data, then save to CSV.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and gene expression data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Determine whether the trait/demographic features are severely biased
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)
+
+# 5. Final quality 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_trait_biased,
+    df=linked_data,
+    note="AML vs healthy controls; microarray-based expression data."
+)
+
+# 6. If usable, save the final linked data
+if is_usable:
+    linked_data.to_csv(out_data_file)

p1/preprocess/Acute_Myeloid_Leukemia/code/GSE222169.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE222169"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222169"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/GSE222169.csv"
+out_gene_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/gene_data/GSE222169.csv"
+out_clinical_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/clinical_data/GSE222169.csv"
+json_path = "./output/preprocess/1/Acute_Myeloid_Leukemia/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 ("Mitochondrial fusion ... AML") and sample annotations, we assume
+#    it is likely gene expression data rather than purely miRNA or methylation.
+is_gene_available = True
+
+# 2. Variable Availability and Data Conversion
+#    Inspecting the sample characteristics dictionary, the data does not provide variability
+#    for the trait "Acute Myeloid Leukemia" (all samples appear AML), and there is no mention
+#    of age or gender. Therefore, set all rows to None.
+trait_row = None
+age_row = None
+gender_row = None
+
+#    Define conversion functions (though they won't be used with None rows).
+def convert_trait(value: str) -> int:
+    # Not used here, but implement a placeholder
+    return 1 if value else None
+
+def convert_age(value: str) -> float:
+    # Not used here, but implement a placeholder
+    return float(value) if value else None
+
+def convert_gender(value: str) -> int:
+    # Not used here, but implement a placeholder
+    val_lower = value.lower() if value else ""
+    if "female" in val_lower:
+        return 0
+    elif "male" in val_lower:
+        return 1
+    return None
+
+# 3. Save Metadata (Initial Filtering)
+#    Trait data is not available because trait_row is None, so is_trait_available=False.
+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
+#    Skip because trait_row is None (no clinical data to extract).
+# 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, these are not standard gene symbols; they likely refer to probe identifiers.
+# Therefore, they require gene symbol mapping.
+
+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. Identify the annotation columns that correspond to the same identifiers in gene_data and the column with gene symbols.
+#    From the preview, 'ID' matches the identifiers in the gene_data, and 'SPOT_ID.1' appears to contain the gene symbol information.
+gene_id_col = "ID"
+gene_symbol_col = "SPOT_ID.1"
+
+# 2. Get a gene mapping dataframe using get_gene_mapping.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=gene_id_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level measurements in gene_data to gene expression by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (At this point, 'gene_data' now contains gene expression values indexed by gene symbols.)
+# STEP 7
+
+import pandas as pd
+
+# We rely on the variable "is_trait_available" from previous steps to decide if trait data is available.
+# If there is no trait data, we skip final validation (is_final=False) because the library function
+# requires a valid 'df' and 'is_biased' if is_final=True.
+
+if not is_trait_available:
+    # Perform a non-final validation, indicating that trait data is unavailable
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,    # Because from Step 2 we said we do have gene data
+        is_trait_available=False   # Confirm we do NOT have trait data
+    )
+else:
+    # 1. Normalize gene symbols in the gene_data, then save to CSV.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # We assume selected_clinical_df was created in a previous step if trait data was present
+    # 2. Link the clinical and gene expression data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait_col=trait)
+
+    # 4. Determine whether the trait/demographic features are severely biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)
+
+    # 5. Final quality 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_trait_biased,
+        df=linked_data,
+        note="AML dataset; microarray-based gene expression."
+    )
+
+    # 6. If usable, save the final linked data
+    if is_usable:
+        linked_data.to_csv(out_data_file)

p1/preprocess/Acute_Myeloid_Leukemia/code/GSE222616.py

@@ -0,0 +1,69 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE222616"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222616"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/GSE222616.csv"
+out_gene_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/gene_data/GSE222616.csv"
+out_clinical_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/clinical_data/GSE222616.csv"
+json_path = "./output/preprocess/1/Acute_Myeloid_Leukemia/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
+#    From the background, the platform is an Affymetrix gene expression array.
+is_gene_available = True
+
+# 2. Identify data availability for trait, age, and gender
+#    Based on the sample characteristics dictionary, the 'trait' is constant ("Acute myeloid leukaemia"),
+#    and there is no indication of age or gender. Hence they are not useful/available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Since they are all None, we do not need converter functions for actual data processing.
+# But we still define stubs per requirement (they won't be used).
+def convert_trait(value: str):
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3. Save metadata via initial filtering
+#    If 'trait_row' is None, 'is_trait_available' is False and the dataset fails 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. Since trait_row is None (trait not available), we skip clinical feature extraction.

p1/preprocess/Acute_Myeloid_Leukemia/code/GSE235070.py

@@ -0,0 +1,68 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE235070"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE235070"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/GSE235070.csv"
+out_gene_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/gene_data/GSE235070.csv"
+out_clinical_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/clinical_data/GSE235070.csv"
+json_path = "./output/preprocess/1/Acute_Myeloid_Leukemia/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 dataset description (non-miRNA and non-methylation)
+
+# Step 2: Identify variable availability and define conversion functions
+trait_row = None  # Only one unique value ("patient with AML"), so no variation
+age_row = None    # No age information found
+gender_row = None # No gender information found
+
+def convert_trait(value: str):
+    # Not used; trait is not available (no variation)
+    return None
+
+def convert_age(value: str):
+    # Not used; no age information
+    return None
+
+def convert_gender(value: str):
+    # Not used; no gender information
+    return None
+
+# Step 3: Save metadata with initial filtering
+is_trait_available = False  # No meaningful trait variation
+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
+)
+
+print("is_usable:", is_usable)
+
+# Step 4: Since trait_row is None, skip clinical feature extraction

p1/preprocess/Acute_Myeloid_Leukemia/code/GSE249638.py

@@ -0,0 +1,198 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE249638"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE249638"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/GSE249638.csv"
+out_gene_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/gene_data/GSE249638.csv"
+out_clinical_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/clinical_data/GSE249638.csv"
+json_path = "./output/preprocess/1/Acute_Myeloid_Leukemia/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 gene expression data availability
+is_gene_available = True  # Based on "comprehensive transcriptomic profiling"
+
+# 2) Determine data availability for trait, age, and gender
+#    By examining the sample characteristics dictionary, we see:
+#    - Row 1 has "disease: acute myeloid leukemia" and "disease: healthy control"
+#      which are relevant to our 'trait'. So we set trait_row = 1.
+#    - No rows indicate age or gender. Hence, age_row = None and gender_row = None.
+
+trait_row = 1
+age_row = None
+gender_row = None
+
+# 2) Data type conversion functions
+def convert_trait(value: str):
+    """
+    Convert disease information into a binary variable.
+    'acute myeloid leukemia' -> 1
+    'healthy control' -> 0
+    Unknown values -> None
+    """
+    # Split by colon and take the latter part
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if "acute myeloid leukemia" in val:
+        return 1
+    elif "healthy control" in val:
+        return 0
+    else:
+        return None
+
+def convert_age(value: str):
+    """
+    No age information is available for this dataset, so this function will not be used.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    No gender information is available for this dataset, so this function will not be used.
+    """
+    return None
+
+# 3) Conduct initial filtering on dataset usability
+#    Trait data availability is determined by whether trait_row is None or not.
+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) If trait_row is not None, extract clinical features
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,       # assumes 'clinical_data' is already available in the environment
+        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 = preview_df(selected_clinical_df)
+    print("Preview of selected clinical data:", preview)
+
+    # Save the extracted clinical data
+    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"), these are likely Affymetrix microarray probe set IDs,
+# not standard human gene symbols. Therefore, gene symbol mapping is required.
+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
+
+# In this dataset, the probe IDs in gene_data (e.g., "2824546_st")
+# do not match any column in gene_annotation (which has values like "TC01000001.hg.1").
+# We will check if there's any column overlap; if not, we cannot proceed with proper mapping.
+
+gene_annotation_cols = list(gene_annotation.columns)
+gene_data_index_set = set(gene_data.index)
+
+matched_column = None
+for col in gene_annotation_cols:
+    # Convert to string in case of mixed types
+    col_values_set = set(gene_annotation[col].astype(str))
+    overlap = gene_data_index_set & col_values_set
+    if len(overlap) > 0:
+        matched_column = col
+        print(f"Found {len(overlap)} matching IDs in annotation column '{col}'.")
+        break
+
+if not matched_column:
+    # No column in gene_annotation matches the probe IDs in gene_data
+    print("No matching column found in gene_annotation for gene_data index. "
+          "Skipping mapping; gene_data remains at probe-level.")
+else:
+    # If we found a column for probe mapping, pick a column for gene symbols.
+    # By inspecting the annotation preview, 'gene_assignment' often contains a recognizable symbol.
+    # We'll assume that's our gene information column.
+    print(f"Using '{matched_column}' as probe identifier column and 'gene_assignment' for gene symbols.")
+
+    mapping_df = get_gene_mapping(
+        annotation=gene_annotation,
+        prob_col=matched_column,
+        gene_col='gene_assignment'
+    )
+
+    # Now apply the mapping to convert probe-level data to gene-level data
+    gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+    print("Gene-level expression data shape:", gene_data.shape)
+    print("Gene-level expression data (head):")
+    print(gene_data.head())
+# STEP 7
+
+# 1. Normalize gene symbols in the gene_data, then save to CSV.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and gene expression data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Determine whether the trait/demographic features are severely biased
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)
+
+# 5. Final quality 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_trait_biased,
+    df=linked_data,
+    note="AML vs healthy controls; microarray-based expression data."
+)
+
+# 6. If usable, save the final linked data
+if is_usable:
+    linked_data.to_csv(out_data_file)

p1/preprocess/Acute_Myeloid_Leukemia/code/GSE98578.py

@@ -0,0 +1,131 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE98578"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE98578"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/GSE98578.csv"
+out_gene_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/gene_data/GSE98578.csv"
+out_clinical_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/clinical_data/GSE98578.csv"
+json_path = "./output/preprocess/1/Acute_Myeloid_Leukemia/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 description, this dataset is about gene expression in AML cell lines
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics, we see that the entire dataset consists of AML cell lines (all are AML),
+# so there is no variation for the main trait "Acute_Myeloid_Leukemia." Hence, it's effectively constant
+# and considered not available for our association studies.
+trait_row = None
+
+# Similarly, there is no age or gender information in the sample characteristics.
+age_row = None
+gender_row = None
+
+# Define data conversion functions (stubs that return None here, since the rows are not available).
+def convert_trait(value: str):
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3. Save Metadata (initial filtering)
+# trait data availability can be determined by whether trait_row is None
+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
+# Since trait_row is None, we skip the clinical feature extraction substep.
+# 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 indices, these are Affymetrix microarray probe identifiers rather than standard 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
+
+# 1. Identify the columns for probe ID (matching the gene expression identifiers) and gene symbol
+probe_col = "ID"
+gene_col = "Gene Symbol"
+
+# 2. Build the gene mapping dataframe from the gene annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3. Convert the probe-level gene expression data to gene-level by applying the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Quickly inspect the resulting gene-level dataframe
+print("Gene-level data preview:")
+print(gene_data.head())
+# STEP 7
+
+import pandas as pd
+
+# 1. Normalize gene symbols in the gene_data, then save to CSV.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Since in previous steps we concluded trait_row=None (i.e., we have no trait data),
+# there's no clinical dataframe to link and no trait to analyze. Hence, the dataset
+# is not usable for trait association. We still finalize the metadata accordingly.
+
+# 2. Because trait data is unavailable, skip linking and bias steps. 
+#    Mark the dataset as not usable for the specified trait, and record in cohort info.
+
+dummy_df = pd.DataFrame()  # we need a dataframe to pass to validation
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,     # We do have gene expression
+    is_trait_available=False,   # No trait data
+    is_biased=True,            # Force "biased" so that it's deemed unusable
+    df=dummy_df,
+    note="No trait data was found in this cohort."
+)
+
+# 3. Since there is no trait data, we do not save any linked dataframe.

p1/preprocess/Acute_Myeloid_Leukemia/code/GSE99612.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE99612"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE99612"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/GSE99612.csv"
+out_gene_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/gene_data/GSE99612.csv"
+out_clinical_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/clinical_data/GSE99612.csv"
+json_path = "./output/preprocess/1/Acute_Myeloid_Leukemia/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 re
+import pandas as pd
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the series title indicating "gene expression profiles"
+
+# 2. Variable Availability and Data Type Conversion
+# After inspecting the sample characteristics, we decide:
+trait_row = 0    # We will interpret "cell line: THP-1" as AML, and "cell line: Caco-2" as not AML.
+age_row = 3      # We can parse "patient age: 1 year infant" from this row; "tumor origin" rows will be None.
+gender_row = None  # Available data appear to be solely male or inconsistent entries. Treat as not available.
+
+# Define conversion functions
+def convert_trait(value: str):
+    if not isinstance(value, str):
+        return None
+    # Extract the string portion after the colon if present
+    parts = value.split(':', 1)
+    val = parts[-1].strip().lower()
+    # Heuristic: THP-1 => 1 (AML), otherwise 0
+    if "thp-1" in val:
+        return 1
+    elif "caco-2" in val:
+        return 0
+    return None
+
+def convert_age(value: str):
+    if not isinstance(value, str):
+        return None
+    # Extract the string portion after the colon if present
+    parts = value.split(':', 1)
+    val = parts[-1].strip().lower()
+    # Heuristic: find a numeric age if "year" is mentioned
+    match = re.search(r'(\d+)', val)
+    if match:
+        return float(match.group(1))
+    return None
+
+# Not needed since gender data is not available
+convert_gender = None
+
+# 3. Save Metadata (initial filtering)
+# Trait availability is determined by trait_row not being None
+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
+# Only proceed if trait data is available
+if trait_row is not None:
+    # Suppose 'clinical_data' is a DataFrame loaded in a previous step
+    # (It is assumed to be accessible in the environment)
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the extracted features
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical data:", preview)
+
+    # Save 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 numeric format of these identifiers, they do not appear to be human gene symbols.
+# Therefore, a mapping step is required.
+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))
+# Gene Identifier Mapping
+
+# 1 & 2. Decide which columns in the annotation match the gene-data IDs and gene symbols.
+# In this dataset, the 'ID' column corresponds to the probe identifiers (matching gene_data.index),
+# and 'gene_assignment' contains references from which we can extract human gene symbols.
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="gene_assignment")
+
+# 3. Convert probe-level measurements to gene expression data by applying the gene mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+print("Mapped gene_data shape:", gene_data.shape)
+# STEP7
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct quality check and save the cohort information using the final dataset (after bias removal).
+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=unbiased_linked_data
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Acute_Myeloid_Leukemia/code/TCGA.py

@@ -0,0 +1,116 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Acute_Myeloid_Leukemia/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Acute_Myeloid_Leukemia/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify the relevant subdirectory
+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)'
+]
+
+target_subdir = None
+for sd in subdirectories:
+    if 'Acute_Myeloid_Leukemia' in sd or 'LAML' in sd:
+        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)
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
+candidate_gender_cols = ["gender"]
+
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+age_data = clinical_df[candidate_age_cols] if candidate_age_cols else pd.DataFrame()
+gender_data = clinical_df[candidate_gender_cols] if candidate_gender_cols else pd.DataFrame()
+
+print(preview_df(age_data))
+print(preview_df(gender_data))
+# Step: Select Demographic Features
+
+# 1. Decide the best columns for age and gender based on the candidate dictionaries
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+# 2. Print out the chosen columns
+print("Chosen column for age_col:", age_col)
+print("Chosen column for gender_col:", gender_col)
+# 1. Extract and standardize the clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# (Optional) Save the selected clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 2. Normalize gene symbols in the genetic data
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3. Link the clinical and genetic data on sample IDs
+linked_data = selected_clinical_df.join(normalized_gene_df.T, how="inner")
+
+# 4. Handle missing values
+cleaned_df = handle_missing_values(linked_data, trait)
+
+# 5. Determine if the trait or demographic features are biased
+is_biased, final_df = judge_and_remove_biased_features(cleaned_df, trait)
+
+# 6. Final quality validation
+is_gene_available = not normalized_gene_df.empty
+is_trait_available = trait in final_df.columns
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_biased,
+    df=final_df,
+    note=""
+)
+
+# 7. If the dataset is usable, save the final dataframe
+if is_usable:
+    final_df.to_csv(out_data_file)

p1/preprocess/Acute_Myeloid_Leukemia/cohort_info.json

@@ -0,0 +1 @@
+{"GSE99612": {"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": 48, "note": ""}, "GSE98578": {"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 was found in this cohort."}, "GSE249638": {"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": 37, "note": "AML vs healthy controls; microarray-based expression data."}, "GSE235070": {"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}, "GSE222616": {"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}, "GSE222169": {"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}, "GSE222124": {"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": 70, "note": "AML vs healthy controls; microarray-based expression data."}, "GSE161532": {"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. Clinical linking skipped."}, "GSE121431": {"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}, "GSE121291": {"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": "Gene expression data present, but no trait data. Cannot perform linking or trait-based QC."}, "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": ""}}

p1/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE161532.csv

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p1/preprocess/Adrenocortical_Cancer/GSE90713.csv

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p1/preprocess/Adrenocortical_Cancer/clinical_data/GSE68950.csv

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p1/preprocess/Adrenocortical_Cancer/clinical_data/GSE75415.csv

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+GSM1954726,GSM1954727,GSM1954728,GSM1954729,GSM1954730,GSM1954731,GSM1954732,GSM1954733,GSM1954734,GSM1954735,GSM1954736,GSM1954737,GSM1954738,GSM1954739,GSM1954740,GSM1954741,GSM1954742,GSM1954743,GSM1954744,GSM1954745,GSM1954746,GSM1954747,GSM1954748,GSM1954749,GSM1954750,GSM1954751,GSM1954752,GSM1954753,GSM1954754,GSM1954755,GSM1954756
+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,,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,,,,,,,,

p1/preprocess/Adrenocortical_Cancer/clinical_data/GSE90713.csv

@@ -0,0 +1,2 @@
+GSM2411058,GSM2411059,GSM2411060,GSM2411061,GSM2411062,GSM2411063,GSM2411064,GSM2411065,GSM2411066,GSM2411067,GSM2411068,GSM2411069,GSM2411070,GSM2411071,GSM2411072,GSM2411073,GSM2411074,GSM2411075,GSM2411076,GSM2411077,GSM2411078,GSM2411079,GSM2411080,GSM2411081,GSM2411082,GSM2411083,GSM2411084,GSM2411085,GSM2411086,GSM2411087,GSM2411088,GSM2411089,GSM2411090,GSM2411091,GSM2411092,GSM2411093,GSM2411094,GSM2411095,GSM2411096,GSM2411097,GSM2411098,GSM2411099,GSM2411100,GSM2411101,GSM2411102,GSM2411103,GSM2411104,GSM2411105,GSM2411106,GSM2411107,GSM2411108,GSM2411109,GSM2411110,GSM2411111,GSM2411112,GSM2411113,GSM2411114,GSM2411115,GSM2411116,GSM2411117,GSM2411118,GSM2411119,GSM2411120
+1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p1/preprocess/Adrenocortical_Cancer/clinical_data/TCGA.csv

@@ -0,0 +1,93 @@
+sampleID,Adrenocortical_Cancer,Age
+TCGA-OR-A5J1-01,1,58
+TCGA-OR-A5J2-01,1,44
+TCGA-OR-A5J3-01,1,23
+TCGA-OR-A5J4-01,1,23
+TCGA-OR-A5J5-01,1,30
+TCGA-OR-A5J6-01,1,29
+TCGA-OR-A5J7-01,1,30
+TCGA-OR-A5J8-01,1,66
+TCGA-OR-A5J9-01,1,22
+TCGA-OR-A5JA-01,1,53
+TCGA-OR-A5JB-01,1,52
+TCGA-OR-A5JC-01,1,37
+TCGA-OR-A5JD-01,1,57
+TCGA-OR-A5JE-01,1,17
+TCGA-OR-A5JF-01,1,69
+TCGA-OR-A5JG-01,1,61
+TCGA-OR-A5JH-01,1,32
+TCGA-OR-A5JI-01,1,22
+TCGA-OR-A5JJ-01,1,65
+TCGA-OR-A5JK-01,1,49
+TCGA-OR-A5JL-01,1,36
+TCGA-OR-A5JM-01,1,25
+TCGA-OR-A5JO-01,1,26
+TCGA-OR-A5JP-01,1,40
+TCGA-OR-A5JQ-01,1,26
+TCGA-OR-A5JR-01,1,45
+TCGA-OR-A5JS-01,1,65
+TCGA-OR-A5JT-01,1,65
+TCGA-OR-A5JU-01,1,58
+TCGA-OR-A5JV-01,1,55
+TCGA-OR-A5JW-01,1,47
+TCGA-OR-A5JX-01,1,50
+TCGA-OR-A5JY-01,1,68
+TCGA-OR-A5JZ-01,1,60
+TCGA-OR-A5K0-01,1,69
+TCGA-OR-A5K1-01,1,48
+TCGA-OR-A5K2-01,1,32
+TCGA-OR-A5K3-01,1,53
+TCGA-OR-A5K4-01,1,64
+TCGA-OR-A5K5-01,1,59
+TCGA-OR-A5K6-01,1,56
+TCGA-OR-A5K8-01,1,39
+TCGA-OR-A5K9-01,1,61
+TCGA-OR-A5KB-01,1,61
+TCGA-OR-A5KO-01,1,39
+TCGA-OR-A5KP-01,1,45
+TCGA-OR-A5KQ-01,1,20
+TCGA-OR-A5KS-01,1,72
+TCGA-OR-A5KT-01,1,44
+TCGA-OR-A5KU-01,1,37
+TCGA-OR-A5KV-01,1,17
+TCGA-OR-A5KW-01,1,55
+TCGA-OR-A5KX-01,1,25
+TCGA-OR-A5KY-01,1,23
+TCGA-OR-A5KZ-01,1,42
+TCGA-OR-A5L1-01,1,37
+TCGA-OR-A5L2-01,1,83
+TCGA-OR-A5L3-01,1,67
+TCGA-OR-A5L4-01,1,48
+TCGA-OR-A5L5-01,1,77
+TCGA-OR-A5L6-01,1,60
+TCGA-OR-A5L8-01,1,36
+TCGA-OR-A5L9-01,1,53
+TCGA-OR-A5LA-01,1,52
+TCGA-OR-A5LB-01,1,59
+TCGA-OR-A5LC-01,1,71
+TCGA-OR-A5LD-01,1,52
+TCGA-OR-A5LE-01,1,14
+TCGA-OR-A5LF-01,1,74
+TCGA-OR-A5LG-01,1,46
+TCGA-OR-A5LH-01,1,36
+TCGA-OR-A5LI-01,1,42
+TCGA-OR-A5LJ-01,1,54
+TCGA-OR-A5LK-01,1,62
+TCGA-OR-A5LL-01,1,75
+TCGA-OR-A5LM-01,1,23
+TCGA-OR-A5LN-01,1,31
+TCGA-OR-A5LO-01,1,61
+TCGA-OR-A5LP-01,1,37
+TCGA-OR-A5LR-01,1,30
+TCGA-OR-A5LS-01,1,34
+TCGA-OR-A5LT-01,1,57
+TCGA-OU-A5PI-01,1,53
+TCGA-P6-A5OF-01,1,55
+TCGA-P6-A5OG-01,1,45
+TCGA-P6-A5OH-01,1,59
+TCGA-PA-A5YG-01,1,51
+TCGA-PK-A5H8-01,1,42
+TCGA-PK-A5H9-01,1,27
+TCGA-PK-A5HA-01,1,63
+TCGA-PK-A5HB-01,1,63
+TCGA-PK-A5HC-01,1,44

p1/preprocess/Adrenocortical_Cancer/code/GSE108088.py

@@ -0,0 +1,149 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE108088"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE108088"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE108088.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE108088.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE108088.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/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
+import numpy as np
+
+# 1. Determine gene expression availability
+# Based on the background info "comprehensive molecular profiling," we assume it includes gene expression data.
+is_gene_available = True
+
+# 2. Identify the keys for trait, age, and gender
+# After examining the sample characteristics dictionary, there's no direct or inferred "Adrenocortical_Cancer," 
+# no age info, and no gender info. Hence, we set them all to None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.1 and 2.2: Data type conversion functions
+def convert_trait(raw_value: str):
+    # This function would parse the raw_value and return 0 or 1 if the trait is binary,
+    # or a float if continuous. Here, we have no trait data, so it's a placeholder.
+    # If used, ensure to handle unknown or malformed entries by returning None.
+    # We split by 'colon' if needed, but since trait_row is None, we won't use it.
+    return None
+
+def convert_age(raw_value: str):
+    # Sample placeholder function. No age data is found, so it returns None.
+    return None
+
+def convert_gender(raw_value: str):
+    # Sample placeholder function. No gender data is found, so it returns None.
+    return None
+
+# 3. Conduct initial filtering on dataset usability, saving relevant metadata
+# Trait data availability is determined by whether trait_row is None.
+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
+# We only proceed if trait_row is not None.
+# Since trait_row is None, we skip this substep.
+# 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 given identifiers (e.g., "1007_s_at", "1053_at", etc.), these are Affymetrix probe IDs,
+# not human gene symbols. Therefore, further mapping is required to convert them into 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))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify which columns in the annotation dataframe correspond to the probe IDs and gene symbols.
+prob_col = "ID"
+gene_col = "Gene Symbol"
+
+# 2. Obtain the gene mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert probe-level expression data to gene-level expression data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Let's inspect the shape and a quick preview of the mapped gene expression data.
+print("Gene data shape after mapping:", gene_data.shape)
+print("Gene data preview:")
+print(preview_df(gene_data, n=5))
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained 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. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+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=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Adrenocortical_Cancer/code/GSE143383.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE143383"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE143383"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE143383.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE143383.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE143383.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/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 "gene expression analysis" and Affymetrix platform info.
+
+# 2. Variable Availability and Data Type Conversion
+#    2.1 Identify rows for trait, age, and gender
+trait_row = None  # No variable in the dictionary indicates a differing trait (likely constant or not listed).
+age_row = None    # No row found for age in the sample characteristics.
+gender_row = 0    # Row 0 contains 'gender: X'.
+
+#    2.2 Define the conversion functions
+def convert_trait(x: str) -> Optional[float]:
+    """Not applicable here because trait_row is None. This is a placeholder."""
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    """Not applicable here because age_row is None. This is a placeholder."""
+    return None
+
+def convert_gender(x: str) -> Optional[int]:
+    """
+    Convert 'gender: X' to binary.
+    'F' -> 0, 'M' -> 1, anything else -> None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'f':
+        return 0
+    elif val == 'm':
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata - initial filtering
+#    Trait data availability depends on whether trait_row is None.
+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
+#    Skip if trait_row is None.
+if trait_row is not None:
+    # Assuming `clinical_data` is the dataframe for sample characteristics
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,  # 'Adrenocortical_Cancer'
+        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 and save
+    print("Clinical features preview:", preview_df(selected_clinical_df, n=5))
+    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])
+# Based on the listed identifiers (e.g., "11715100_at"), they appear to be Affymetrix probe set IDs, not human gene symbols.
+# Hence, gene mapping is required.
+
+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. Identify the columns in the gene_annotation dataframe that correspond to the probe IDs and gene symbols.
+#    From the preview, "ID" matches the probe identifiers in gene_data, and "Gene Symbol" contains the gene symbols.
+
+# 2. Create a gene mapping dataframe using the relevant columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Convert probe-level measurements in gene_data to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print a quick preview of the resulting gene_data
+print("Mapped gene_data preview:")
+print(gene_data.head(5))
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained 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. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+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=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Adrenocortical_Cancer/code/GSE19776.py

@@ -0,0 +1,175 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE19776"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE19776"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE19776.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE19776.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE19776.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/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: Decide if the dataset contains gene expression data
+# Based on the series title "Adrenocortical Carcinoma Gene Expression Profiling",
+# we conclude that it is likely to contain gene expression data.
+is_gene_available = True
+
+# Step 2: Variable Availability and Data Type Conversion
+
+# 2.1 Identify Rows
+#   - trait: We see only "tissue: adrenocortical carcinoma" under key 0. This is a single unique value,
+#     which is uninformative for association. Hence treat it as not available for the trait.
+trait_row = None
+
+#   - age: Found under key 5 (multiple distinct values, some are "age: Unknown").
+age_row = 5
+
+#   - gender: Found under key 4 (M/F). Multiple values, not constant.
+gender_row = 4
+
+# 2.2 Define Conversion Functions
+def convert_trait(x: str) -> int:
+    """
+    Returns None because trait is not available (single unique value in dataset).
+    This function is a placeholder to adhere to the required interface.
+    """
+    return None
+
+def convert_age(x: str) -> float:
+    """
+    Convert the substring after 'age:' to float if possible.
+    If it's 'Unknown' or non-parsable, return None.
+    """
+    val = x.split(':')[-1].strip()
+    if val.lower() == "unknown":
+        return None
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(x: str) -> int:
+    """
+    Convert 'gender: F' -> 0, 'gender: M' -> 1.
+    If the value is unknown or doesn't match, return None.
+    """
+    val = x.split(':')[-1].strip().upper()
+    if val == 'F':
+        return 0
+    elif val == 'M':
+        return 1
+    return None
+
+# Step 3: Save initial filtering metadata
+# Trait data is not available if trait_row is None
+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: Extract clinical features only if trait_row is not None
+# Since trait_row = 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])
+# The provided gene identifiers are all numeric, which are not standard human gene symbols.
+# They likely refer to probe IDs or some other numeric format.
+# Therefore, gene mapping to human gene symbols is required.
+
+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
+
+# Reviewer feedback indicates a mismatch between the numeric row IDs in the gene expression dataframe
+# (e.g., "3", "4", "5") and the probe IDs in the annotation file (e.g., "1007_s_at", "1053_at").
+# Because there is no overlap, a direct mapping is not possible with the provided annotation.
+# We'll demonstrate a fallback approach: we attempt to match, but if no overlap is found, we skip mapping.
+
+# 1. Decide which columns in the annotation *would* store the probe IDs and gene symbols if they matched.
+probe_col = "ID"
+gene_col = "Gene Symbol"
+
+# 2. Extract the potential mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3. Check for any intersection in identifiers before applying the mapping.
+common_ids = set(gene_data.index).intersection(mapping_df['ID'])
+if len(common_ids) == 0:
+    print("No matching identifiers found between gene expression data and annotation. Skipping gene mapping.")
+else:
+    gene_data = apply_gene_mapping(gene_data, mapping_df)
+    print("Gene mapping applied successfully.")
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained 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. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+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=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Adrenocortical_Cancer/code/GSE49278.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE49278"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE49278"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE49278.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE49278.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE49278.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/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: "Expression profiling by array ..."
+
+# 2. Variable Availability and Data Type Conversion
+# Observing the sample characteristics, key=2 has only one unique value (Adrenocortical carcinoma),
+# so that is constant and not useful for association analyses, thus trait_row = None.
+trait_row = None
+
+# key=0 shows multiple age values => available
+age_row = 0
+
+# key=1 shows two gender values => available
+gender_row = 1
+
+# Define conversion functions
+def convert_trait(value: str):
+    # Since trait data is effectively not available (constant),
+    # this function returns None
+    return None
+
+def convert_age(value: str):
+    # Typical format: "age (years): 70"
+    # Convert the part after the colon to a numeric type
+    try:
+        val_str = value.split(':', 1)[1].strip()
+        return float(val_str)
+    except:
+        return None
+
+def convert_gender(value: str):
+    # Typical format: "gender: F" or "gender: M"
+    # Convert F -> 0, M -> 1
+    try:
+        val_str = value.split(':', 1)[1].strip().upper()
+        if val_str == 'F':
+            return 0
+        elif val_str == 'M':
+            return 1
+        else:
+            return None
+    except:
+        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. Clinical Feature Extraction
+# Skip this step because trait_row is None (no trait data available).
+# 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
+
+# After reviewing the annotation DataFrame columns:
+#   ['ID', 'RANGE_STRAND', 'RANGE_START', 'RANGE_END', 'total_probes', 'GB_ACC', 'SPOT_ID', 'RANGE_GB']
+# we see that 'GB_ACC' usually contains "NR_" transcripts and 'SPOT_ID' has genomic coordinates. Neither appear to provide
+# valid gene symbols recognizable by extract_human_gene_symbols (which filters out NR_, XR_, LOC, etc.). 
+# Therefore, mapping to standard gene symbols is not possible here. 
+# We'll retain the original probe-level data without attempting gene-level aggregation.
+
+print("No suitable gene symbol column found. Proceeding with probe-level data only.")
+# The 'gene_data' DataFrame remains as probe-level data.
+# No further action is required for mapping in this dataset.
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained 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. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+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=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Adrenocortical_Cancer/code/GSE67766.py

@@ -0,0 +1,132 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE67766"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE67766"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE67766.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE67766.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE67766.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/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 if gene expression data is available
+is_gene_available = True  # Based on background context, we assume gene expression data is present
+
+# 2. Determine availability for trait, age, and gender from the sample characteristics dictionary
+#    Given the dictionary: {0: ['cell line: SW-13']}, there is no variation or explicit mention
+#    of trait, age, or gender. Hence, they are all considered unavailable.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define data type conversion functions
+def convert_trait(x: str):
+    # No trait data available. Return None for any input.
+    return None
+
+def convert_age(x: str):
+    # No age data available. Return None for any input.
+    return None
+
+def convert_gender(x: str):
+    # No gender data available. Return None for any input.
+    return None
+
+# 3. Save Metadata (initial filtering)
+#    'is_trait_available' is False because 'trait_row' is None
+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 this step (no clinical data to extract).
+# 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 gene identifiers ('ILMN_...') are Illumina probe IDs rather than standard human gene symbols.
+# Hence, gene mapping to official symbols is required.
+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) Identify the columns for gene identifier and gene symbol based on the annotation preview.
+probe_col = "ID"
+symbol_col = "Symbol"
+
+# 2) Build the gene mapping dataframe from the annotation dataframe.
+mapping_df = get_gene_mapping(gene_annotation, probe_col, symbol_col)
+
+# 3) Apply the mapping to convert probe-level expression to gene-level expression.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols in the obtained 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 data is unavailable (trait_row = None), we cannot link or analyze trait/demographic features.
+# We must finalize this dataset as unusable for downstream analysis.
+
+# Provide a dummy dataframe and a boolean for is_biased to satisfy the library requirements.
+import pandas as pd
+empty_df = pd.DataFrame()
+
+# 5. Perform final quality validation and save cohort info. 
+#    We set is_biased=False to fulfill the function parameters; it will still result in is_usable=False 
+#    because is_trait_available=False.
+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=False,
+    df=empty_df,
+    note="No trait data available for this cohort."
+)
+
+# 6. Since no trait data is available, is_usable must be False, so we skip saving the final linked data.

p1/preprocess/Adrenocortical_Cancer/code/GSE68606.py

@@ -0,0 +1,149 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE68606"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68606"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE68606.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE68606.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE68606.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/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 "Assay Type: Gene Expression" and "Affymetrix Human Genome U133A arrays" in the metadata,
+# we conclude that this dataset likely contains gene expression data.
+is_gene_available = True
+
+# 2) Variable Availability and Data Type Conversion
+
+# 2.1 Identify availability of 'trait', 'age', and 'gender' by looking at the Sample Characteristics Dictionary
+# We did not find "Adrenocortical_Cancer" or an equivalent entry in any row,
+# so trait data is considered not available.
+trait_row = None
+
+# Age data is present in row 6 with multiple unique numeric values.
+age_row = 6
+
+# Gender data is present in row 5 (female/male).
+gender_row = 5
+
+# 2.2 Define conversion functions for each variable
+
+def convert_trait(x: str):
+    # Trait data is not available in this dataset, return None for all inputs.
+    return None
+
+def convert_age(x: str):
+    # Extract the substring after the colon and strip whitespace
+    val = x.split(":", 1)[-1].strip()
+    # Convert to integer if possible, otherwise None
+    return int(val) if val.isdigit() else None
+
+def convert_gender(x: str):
+    # Extract the substring after the colon and strip whitespace
+    val = x.split(":", 1)[-1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    else:
+        return None
+
+# 3) Save Metadata (Initial Filtering)
+
+is_trait_available = (trait_row is not None)  # False in this case
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+# Skip this step because trait_row is None (no trait data available).
+# 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 (e.g., '1007_s_at', '1053_at') are Affymetrix probe set IDs, not human gene symbols.
+# Therefore, 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: Gene Identifier Mapping
+
+# 1) The key for the probe identifiers in the gene annotation is "ID",
+#    and the key for the gene symbols is "Gene Symbol".
+
+# 2) Build a gene mapping dataframe using those two columns.
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3) Apply the mapping to convert probe-level measurements to gene expression data.
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+# STEP 7: Data Normalization and Linking
+
+# Even though we lack trait data, it's still valuable to finalize gene-level data.
+# 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)
+
+# Since trait_row = None, there's no trait data to link or analyze.
+# We cannot produce a linked dataset or evaluate trait bias in a meaningful way.
+# However, the task instructions request a "final" validation.
+
+import pandas as pd
+
+# Provide a dummy DataFrame and set is_biased to False
+# so that validate_and_save_cohort_info can finalize and mark this dataset as unusable for trait analysis.
+empty_df = pd.DataFrame()
+is_biased = False
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,      # We do have gene data
+    is_trait_available=False,    # But no trait data
+    is_biased=is_biased,         # Arbitrarily set to False since no trait is present
+    df=empty_df,                 # An empty DataFrame to satisfy the function's requirements
+    note="No trait data available, so no final linked dataset can be produced."
+)
+
+# 6. Because the dataset is not usable for trait-based analysis, we do not save a final linked dataset.

p1/preprocess/Adrenocortical_Cancer/code/GSE68950.py

@@ -0,0 +1,153 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE68950"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68950"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE68950.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE68950.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE68950.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/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  # "Assay Type: Gene Expression" indicates gene expression data.
+
+# 2.1 Variable Availability
+# The term "adrenal cortical carcinoma" is present in the "disease state" field (row 1),
+# matching our trait "Adrenocortical_Cancer." Hence, trait_row = 1.
+trait_row = 1
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversions
+def convert_trait(value: str):
+    """
+    Convert 'disease state' to a binary trait:
+    1 for 'adrenal cortical carcinoma',
+    0 for anything else.
+    """
+    label = value.split(":", 1)[-1].strip().lower()
+    if "adrenal cortical carcinoma" in label:
+        return 1
+    else:
+        return 0
+
+def convert_age(value: str):
+    return None  # Age data not available
+
+def convert_gender(value: str):
+    return None  # Gender data not available
+
+# 3. Save Metadata with 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. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,  # "Adrenocortical_Cancer"
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the selected clinical features
+    print(preview_df(selected_clinical_df))
+    # Save the extracted clinical data
+    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])
+# The gene identifiers shown (e.g., "1007_s_at", "1053_at") are Affymetrix probe set IDs
+# rather than standard human gene symbols, so they 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. Identify the columns for gene identifier and gene symbol in the annotation dataframe
+probe_col = "ID"
+symbol_col = "Gene Symbol"
+
+# 2. Get the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3. Map probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_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 clinical and genetic data on sample IDs
+#    "selected_clinical_df" was defined in a previous step, so we can use it directly.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically
+processed_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait or demographic features are severely biased
+trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+# 5. Final quality validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=processed_data,
+    note="Trait data present and mapped from step 2."
+)
+
+# 6. Save the final linked data only if usable
+if is_usable:
+    processed_data.to_csv(out_data_file, index=True)

p1/preprocess/Adrenocortical_Cancer/code/GSE75415.py

@@ -0,0 +1,175 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE75415"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE75415"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE75415.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE75415.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE75415.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/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 if gene expression data is available
+is_gene_available = True  # Based on the series title/summary indicating gene expression microarray data.
+
+# 2. Identify rows and define conversion functions for trait, age, and gender.
+trait_row = 1  # "histologic type: ..." key
+age_row = None  # No age info found
+gender_row = 0  # "gender: ..." key
+
+def convert_trait(value: str):
+    """
+    Convert histologic type to a binary variable:
+    1 => adrenocortical carcinoma
+    0 => adenoma or normal
+    None => unknown
+    """
+    parts = value.split(':', 1)
+    if len(parts) == 2:
+        val = parts[1].strip().lower()
+        if 'carcinoma' in val:
+            return 1
+        elif 'adenoma' in val or 'normal' in val:
+            return 0
+        elif 'unknown' in val:
+            return None
+    return None
+
+def convert_age(value: str):
+    """
+    Age data is not available, so return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    Convert gender to binary:
+    0 => female
+    1 => male
+    None => unknown
+    """
+    parts = value.split(':', 1)
+    if len(parts) == 2:
+        val = parts[1].strip().lower()
+        if val == 'female':
+            return 0
+        elif val == 'male':
+            return 1
+    return None
+
+# 3. Conduct initial filtering 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. Extract clinical features if trait data is available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,  # Assuming clinical_data is the DataFrame with sample characteristics
+        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 and save
+    previewed = preview_df(selected_clinical_df)
+    print("Selected Clinical Features Preview:", previewed)
+    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])
+# Observing the provided list of identifiers (e.g., "1007_s_at", "1053_at"), they are Affymetrix probe set IDs.
+# These are not standard human gene symbols; hence they do require mapping.
+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. Based on the annotation preview, the column "ID" in 'gene_annotation' matches the probe identifiers
+#    in the gene expression data (also labeled "ID"). The column "Gene Symbol" contains the actual gene symbols.
+# 2. Extract the two columns from the gene annotation dataframe, "ID" (probe ID) and "Gene Symbol" (gene symbol),
+#    to create the mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, "ID", "Gene Symbol")
+
+# 3. Convert probe-level measurements to gene-level expression data using the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print the resulting dataframe shape and some rows to verify.
+print("Gene expression data shape after mapping:", gene_data.shape)
+print(gene_data.head())
+# 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 clinical and genetic data on sample IDs
+#    "selected_clinical_df" was defined in a previous step, so we can use it directly.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically
+processed_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait or demographic features are severely biased
+trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+# 5. Final quality validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=processed_data,
+    note="Trait data present and mapped from step 2."
+)
+
+# 6. Save the final linked data only if usable
+if is_usable:
+    processed_data.to_csv(out_data_file, index=True)

p1/preprocess/Adrenocortical_Cancer/code/GSE76019.py

@@ -0,0 +1,127 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE76019"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE76019"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE76019.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE76019.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE76019.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/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 "gene expression profiling" statement
+# 2. Variable availability
+#    The trait is constant ("ACC") across all samples --> not useful for association study
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define data conversion functions (they won't be used because rows are None, but we must still define them)
+def convert_trait(value: str):
+    # No actual functionality here because trait_row is None
+    return None
+
+def convert_age(value: str):
+    # No actual functionality here because age_row is None
+    return None
+
+def convert_gender(value: str):
+    # No actual functionality here because gender_row is None
+    return None
+
+# 3. Initial Filtering (Save metadata)
+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 is skipped since 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 expression data uses probe-based identifiers (e.g., Affymetrix probe IDs) rather than standard human gene symbols.
+# Therefore, a mapping step 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))
+# STEP: Gene Identifier Mapping
+
+# 1) The key in 'gene_annotation' that matches the probe identifiers in 'gene_data' is "ID".
+#    The key that stores the gene symbols is "Gene Symbol".
+
+# 2) Extract a gene mapping dataframe from 'gene_annotation' with these columns.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="Gene Symbol"
+)
+
+# 3) Convert probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+# STEP 7: Data Normalization and Linking
+
+import pandas as pd
+
+# 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)
+
+# Since the trait was determined unavailable (trait_row is None), there is no clinical data to link.
+# We therefore skip linking, missing value handling, and bias checks.
+
+# 5. Final quality validation (the dataset is not suitable for trait-based association studies).
+#    We must provide a DataFrame and a boolean for is_biased to avoid errors, even though trait data is missing.
+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=False,  # Arbitrary value, as the dataset is already not usable due to missing trait
+    df=pd.DataFrame(),  # Empty DataFrame to satisfy validation requirements
+    note="No trait data, so dataset is not suitable for association studies."
+)
+
+# 6. Since the dataset is not usable for trait-based analyses, we do NOT save a final linked data file.

p1/preprocess/Adrenocortical_Cancer/code/GSE90713.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE90713"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE90713"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE90713.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE90713.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE90713.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/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 if gene expression data is available
+is_gene_available = True  # Based on the series description showing Affymetrix microarray gene expression
+
+# 2) Identify availability of trait, age, and gender data
+trait_row = 0   # "tissue: adrenocortical carcinoma" vs. "tissue: normal adrenal"
+age_row = None  # No age-related information found
+gender_row = None  # No gender-related information found
+
+# 2) Data type conversion functions
+def convert_trait(x: str) -> Optional[int]:
+    """
+    Convert the tissue annotation to binary values for adrenocortical carcinoma (1) or normal adrenal (0).
+    Unknown values return None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[-1].strip().lower()
+    if val in ["adrenocortical carcinoma", "acc", "tumor"]:
+        return 1
+    elif val in ["normal adrenal", "normal"]:
+        return 0
+    else:
+        return None
+
+def convert_age(x: str) -> Optional[float]:
+    """No age data available, so always return None."""
+    return None
+
+def convert_gender(x: str) -> Optional[int]:
+    """No gender data available, so always return None."""
+    return None
+
+# 3) Initial filtering and metadata saving
+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) Extract clinical features if trait_row is not None
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save the extracted clinical features
+    preview_result = preview_df(selected_clinical_df)
+    print("Preview of Clinical Data:", preview_result)
+    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])
+# These identifiers (e.g., "11715100_at", "11715101_s_at") appear to be Affymetrix probe set IDs, 
+# not standard human gene symbols. Hence, gene symbol mapping is required.
+
+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. Identify the columns in the annotation that match our probe IDs and gene symbols:
+#    - Probe ID column: 'ID'
+#    - Gene Symbol column: 'Gene Symbol'
+
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# 2. Generate a gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply the gene mapping to convert probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print a quick preview of the first few rows after mapping
+print("Mapped Gene Expression Data (first 5 rows):")
+print(gene_data.head(5))
+# 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 clinical and genetic data on sample IDs
+#    "selected_clinical_df" was defined in a previous step, so we can use it directly.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically
+processed_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait or demographic features are severely biased
+trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+# 5. Final quality validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=processed_data,
+    note="Trait data present and mapped from step 2."
+)
+
+# 6. Save the final linked data only if usable
+if is_usable:
+    processed_data.to_csv(out_data_file, index=True)

p1/preprocess/Adrenocortical_Cancer/code/TCGA.py

@@ -0,0 +1,112 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify the relevant subdirectory for the trait "Obesity"
+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 = trait
+target_subdir = None
+
+for sd in subdirectories:
+    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:
+    # 2. Locate clinical and genetic data files
+    cohort_dir = os.path.join(tcga_root_dir, target_subdir)
+    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)
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis"]
+candidate_gender_cols = []
+
+candidate_demo_cols = candidate_age_cols + candidate_gender_cols
+if candidate_demo_cols:
+    extracted_df = clinical_df[candidate_demo_cols]
+    preview_data = preview_df(extracted_df)
+    print(preview_data)
+# Based on the inspection of the provided dictionaries for age and gender:
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = None
+
+print("Chosen age_col:", age_col)
+print("Chosen gender_col:", gender_col)
+# 1. Extract and standardize the clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# (Optional) Save the selected clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 2. Normalize gene symbols in the genetic data
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3. Link the clinical and genetic data on sample IDs
+linked_data = selected_clinical_df.join(normalized_gene_df.T, how="inner")
+
+# 4. Handle missing values
+cleaned_df = handle_missing_values(linked_data, trait)
+
+# 5. Determine if the trait or demographic features are biased
+is_biased, final_df = judge_and_remove_biased_features(cleaned_df, trait)
+
+# 6. Final quality validation
+is_gene_available = not normalized_gene_df.empty
+is_trait_available = trait in final_df.columns
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_biased,
+    df=final_df,
+    note=""
+)
+
+# 7. If the dataset is usable, save the final dataframe
+if is_usable:
+    final_df.to_csv(out_data_file)

p1/preprocess/Adrenocortical_Cancer/cohort_info.json

@@ -0,0 +1 @@
+{"GSE90713": {"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": 63, "note": "Trait data present and mapped from step 2."}, "GSE76019": {"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, so dataset is not suitable for association studies."}, "GSE75415": {"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": 30, "note": "Trait data present and mapped from step 2."}, "GSE68950": {"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": 798, "note": "Trait data present and mapped from step 2."}, "GSE68606": {"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 available, so no final linked dataset can be produced."}, "GSE67766": {"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 available for this cohort."}, "GSE49278": {"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 found; skipped clinical-linking steps."}, "GSE19776": {"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 found; skipped clinical-linking steps."}, "GSE143383": {"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 found; skipped clinical-linking steps."}, "GSE108088": {"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 found; skipped clinical-linking steps."}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": false, "sample_size": 79, "note": ""}}

p1/preprocess/Adrenocortical_Cancer/gene_data/GSE108088.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cb50d3f64363391929b11b0b0c3b9b60220fdf282410c0fbee176df2e3905608
+size 11463867

p1/preprocess/Adrenocortical_Cancer/gene_data/GSE143383.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b84c1abcbbb0d634ec65f7af9b8266221d89f82714b2571c6600d3f9c49e5558
+size 12280887

p1/preprocess/Adrenocortical_Cancer/gene_data/GSE19776.csv

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+ID,GSM493903,GSM493904,GSM493905,GSM493906,GSM493907,GSM493908,GSM493909,GSM493910,GSM493911,GSM493912,GSM493913,GSM493914,GSM493915,GSM493916,GSM493917

p1/preprocess/Adrenocortical_Cancer/gene_data/GSE49278.csv

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