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p1/preprocess/Esophageal_Cancer/code/GSE100843.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE100843"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE100843"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Esophageal_Cancer/GSE100843.csv"
+out_gene_data_file = "./output/preprocess/1/Esophageal_Cancer/gene_data/GSE100843.csv"
+out_clinical_data_file = "./output/preprocess/1/Esophageal_Cancer/clinical_data/GSE100843.csv"
+json_path = "./output/preprocess/1/Esophageal_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  # The design mentions microarray for global gene expression.
+
+# 2.1) Identify data availability for 'trait', 'age', 'gender'
+#      From the sample characteristics dictionary, none of these variables are present.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Define data type conversion functions (they will return None here, as data is unavailable)
+def convert_trait(value):
+    return None
+
+def convert_age(value):
+    return None
+
+def convert_gender(value):
+    return None
+
+# 3) Initial filtering: trait data is considered unavailable because 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) 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 numeric pattern, these identifiers (e.g., 7892501, 7892502, etc.) are not recognized human gene symbols.
+# They appear to be probe IDs that should be mapped to gene symbols for downstream analysis.
+print("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the annotation dataframe that correspond to the probe ID and the gene symbol.
+#    From the preview above, "ID" stores the probe identifier and "gene_assignment" stores the gene information.
+
+# 2. Generate the mapping dataframe using the library function get_gene_mapping.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="gene_assignment"
+)
+
+# 3. Apply the mapping to convert probe-level measurements in 'gene_data' to gene-level expression.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# (Optional) Inspect the resulting gene_data shape or columns if desired
+print("Gene expression dataframe shape after mapping:", gene_data.shape)
+print("First few genes:", gene_data.index[:10].tolist())
+import os
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2
+if os.path.exists(out_clinical_data_file):
+    # 2) Link the clinical and gene expression data
+    # Load the single-row clinical CSV without forcing an index column
+    tmp_df = pd.read_csv(out_clinical_data_file, header=0)
+    # Rename the single row to the trait. Now columns = sample IDs, index = [trait].
+    tmp_df.index = [trait]
+    selected_clinical_df = tmp_df
+
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3) Handle missing values
+    final_data = handle_missing_values(linked_data, trait_col=trait)
+
+    # 4) Evaluate bias in the trait (and remove biased demographics if any)
+    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+    # 5) Final validation
+    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=final_data,
+        note="Trait data successfully extracted; row renamed to trait for linking."
+    )
+
+    # 6) If the dataset is usable, save
+    if is_usable:
+        final_data.to_csv(out_data_file)
+else:
+    # If the clinical file does not exist, the trait is unavailable
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,
+        df=empty_df,
+        note="No trait data was found; linking and final dataset output are skipped."
+    )

p1/preprocess/Esophageal_Cancer/code/GSE104958.py

@@ -0,0 +1,206 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE104958"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE104958"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Esophageal_Cancer/GSE104958.csv"
+out_gene_data_file = "./output/preprocess/1/Esophageal_Cancer/gene_data/GSE104958.csv"
+out_clinical_data_file = "./output/preprocess/1/Esophageal_Cancer/clinical_data/GSE104958.csv"
+json_path = "./output/preprocess/1/Esophageal_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 for this dataset
+is_gene_available = True  # Based on the background info indicating DNA microarray data
+
+# 2) Identify availability of trait, age, and gender, and define conversion functions
+
+# From the sample characteristics dictionary:
+#   0 => ['organ: esophagus']
+#   1 => ['tissue: cancer tissue', 'tissue: normal tissue']
+#
+# Row 0 has only one unique value, so it's not useful for associative studies.
+# Row 1 has two unique values ("cancer tissue" and "normal tissue"), which we can treat as a binary trait.
+
+trait_row = 1
+age_row = None
+gender_row = None
+
+def convert_trait(value: str) -> int:
+    # Extract the substring after colon
+    parts = value.split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else ''
+    if val == 'cancer tissue':
+        return 1
+    elif val == 'normal tissue':
+        return 0
+    else:
+        return None
+
+# Since age_row and gender_row are None, we define the following but won't use them:
+def convert_age(value: str) -> float:
+    return None
+
+def convert_gender(value: str) -> int:
+    return None
+
+# 3) Save metadata with initial filtering
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) If trait_row is not None, extract clinical features
+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 the resulting clinical DataFrame
+    preview = preview_df(selected_clinical_df)
+    print("Selected clinical feature preview:", preview)
+
+    # Save clinical data 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 observation, these IDs look like custom transcript/probe IDs rather than standard human gene symbols.
+# Therefore, gene mapping is needed.
+print("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Decide which annotation columns correspond to the gene expression identifiers and gene symbols
+#    From the preview, the "ID" column in the annotation corresponds to the row IDs in the expression data,
+#    and the "GENE_SYMBOL" column contains the gene symbols.
+
+# 2) Create the gene mapping dataframe using these columns
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="GENE_SYMBOL"
+)
+
+# 3) Convert the probe-level expression data to gene-level expression data
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,
+    mapping_df=mapping_df
+)
+
+# Print a summary of the resulting gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data preview:", preview_df(gene_data))
+import os
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2
+if os.path.exists(out_clinical_data_file):
+    # 2) Link the clinical and gene expression data
+    # Load the single-row clinical CSV without forcing an index column
+    tmp_df = pd.read_csv(out_clinical_data_file, header=0)
+    # Rename the single row to the trait. Now columns = sample IDs, index = [trait].
+    tmp_df.index = [trait]
+    selected_clinical_df = tmp_df
+
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3) Handle missing values
+    final_data = handle_missing_values(linked_data, trait_col=trait)
+
+    # 4) Evaluate bias in the trait (and remove biased demographics if any)
+    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+    # 5) Final validation
+    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=final_data,
+        note="Trait data successfully extracted; row renamed to trait for linking."
+    )
+
+    # 6) If the dataset is usable, save
+    if is_usable:
+        final_data.to_csv(out_data_file)
+else:
+    # If the clinical file does not exist, the trait is unavailable
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,
+        df=empty_df,
+        note="No trait data was found; linking and final dataset output are skipped."
+    )

p1/preprocess/Esophageal_Cancer/code/GSE107754.py

@@ -0,0 +1,223 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE107754"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE107754"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Esophageal_Cancer/GSE107754.csv"
+out_gene_data_file = "./output/preprocess/1/Esophageal_Cancer/gene_data/GSE107754.csv"
+out_clinical_data_file = "./output/preprocess/1/Esophageal_Cancer/clinical_data/GSE107754.csv"
+json_path = "./output/preprocess/1/Esophageal_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 the dataset is likely to contain gene expression data.
+#    From the background information, this series uses "Whole human genome gene expression microarrays",
+#    so we set is_gene_available to True.
+is_gene_available = True
+
+# 2. Identify variable availability and define conversion functions.
+
+# 2.1 Availability
+#    We see multiple tissue types including "Esophagus cancer" in row 2, so we'll treat that as the trait indicator.
+#    Gender is in row 0. Age is not found anywhere.
+trait_row = 2
+age_row = None
+gender_row = 0
+
+# 2.2 Data Type Conversions
+def convert_trait(x: str):
+    """
+    Convert a sample characteristic string into a binary value for Esophageal_Cancer.
+    Return 1 if 'Esophagus cancer' is mentioned, otherwise 0.
+    Return None if the string format is unexpected.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Mark as 1 if it specifically contains 'esophagus cancer', else 0
+    return 1 if 'esophagus cancer' in val else 0
+
+def convert_gender(x: str):
+    """
+    Convert a gender string into binary value: 0 for female, 1 for male.
+    Return None if unknown.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    else:
+        return None
+
+# Since age is not available, we won't define a convert_age function (or just pass None).
+convert_age = None
+
+# 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. If trait data is available, extract clinical features, preview, and save.
+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 = preview_df(selected_clinical_df, n=5, max_items=200)
+    print("Preview of selected clinical features:\n", preview)
+
+    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 probe IDs (e.g., A_23_P100xxx) appear to be array-specific identifiers rather than standard human gene symbols.
+# Therefore, mapping is needed to convert these identifiers to canonical gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Determine which columns map the probe IDs in the gene expression data ("ID") to the gene symbols ("GENE_SYMBOL").
+# 2. Create a mapping dataframe.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col='ID',
+    gene_col='GENE_SYMBOL'
+)
+
+# 3. Apply the mapping to convert the probe-level data into gene-level data.
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,
+    mapping_df=mapping_df
+)
+
+# Optional: Preview the newly mapped gene expression data.
+print("Preview of gene_data after applying gene symbol mapping:")
+print(preview_df(gene_data, n=5))
+import os
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols in the gene expression data,
+#    remove unrecognized symbols, and aggregate duplicates.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Check whether we have a clinical CSV file from previous steps.
+if os.path.exists(out_clinical_data_file):
+    # Read the CSV so that sample IDs become columns,
+    # and rename rows to [trait, 'Gender'] accordingly.
+    tmp_df = pd.read_csv(out_clinical_data_file, header=0)
+
+    # If there are 2 rows (trait and gender), rename them.
+    # If only 1 row (trait only), rename just that row.
+    if tmp_df.shape[0] == 2:
+        tmp_df.index = [trait, 'Gender']
+    else:
+        tmp_df.index = [trait]
+
+    selected_clinical_df = tmp_df
+
+    # Link clinical data (now with rows as trait/covariates) and gene data.
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3) Handle missing values (drop incomplete samples/features, then impute).
+    final_data = handle_missing_values(linked_data, trait_col=trait)
+
+    # 4) Check trait bias, and remove biased demographics if needed.
+    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+    # 5) Final validation and record dataset metadata.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=final_data,
+        note="Trait and gender identified in two-row clinical file; indices renamed before linking."
+    )
+
+    # 6) If the dataset is usable, save the final linked data.
+    if is_usable:
+        final_data.to_csv(out_data_file)
+else:
+    # If we have no clinical file, we cannot use the dataset.
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,
+        df=empty_df,
+        note="No trait data was found; linking and final dataset output are skipped."
+    )

p1/preprocess/Esophageal_Cancer/code/GSE131027.py

@@ -0,0 +1,203 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE131027"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE131027"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Esophageal_Cancer/GSE131027.csv"
+out_gene_data_file = "./output/preprocess/1/Esophageal_Cancer/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/preprocess/1/Esophageal_Cancer/clinical_data/GSE131027.csv"
+json_path = "./output/preprocess/1/Esophageal_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 summary indicating "investigation of expression features"
+
+# 2. Determine data availability for trait, age, and gender
+#    From the sample characteristics dictionary, trait appears at key=1 with multiple cancer types,
+#    including "Oesophageal cancer". Age and gender information are not present.
+
+trait_row = 1
+age_row = None
+gender_row = None
+
+# 2.2 Define data type conversion functions
+def convert_trait(x: str):
+    """
+    Convert string to binary indicator of trait ("Esophageal_Cancer").
+    1 = Oesophageal cancer; 0 = other cancer types; None = unknown
+    """
+    parts = x.split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else parts[0].strip().lower()
+    if 'oesophageal' in val:
+        return 1
+    elif 'cancer' in val:
+        return 0
+    else:
+        return None
+
+def convert_age(x: str):
+    """
+    Not used in this dataset (age_row = None). Dummy placeholder.
+    """
+    return None
+
+def convert_gender(x: str):
+    """
+    Not used in this dataset (gender_row = None). Dummy placeholder.
+    """
+    return None
+
+# 3. Conduct initial filtering and save metadata
+#    Trait is available if trait_row is not None
+is_trait_available = (trait_row is not None)
+
+metadata_result = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. If trait data is available, extract clinical features
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_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 selected clinical features
+    preview_dict = preview_df(selected_clinical_df, n=5)
+    print("Preview of selected clinical features:", preview_dict)
+
+    # Save clinical dataframe
+    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., '1007_s_at', '1053_at') indicate Affymetrix probe set IDs rather than official gene symbols.
+# Therefore, mapping to human gene symbols is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns for probe identifiers ("ID") and gene symbols ("Gene Symbol")
+# 2. Create a mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print a quick check of the transformed gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data preview:\n", gene_data.head(5))
+import os
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2
+if os.path.exists(out_clinical_data_file):
+    # 2) Link the clinical and gene expression data
+    # Load the single-row clinical CSV without forcing an index column
+    tmp_df = pd.read_csv(out_clinical_data_file, header=0)
+    # Rename the single row to the trait. Now columns = sample IDs, index = [trait].
+    tmp_df.index = [trait]
+    selected_clinical_df = tmp_df
+
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3) Handle missing values
+    final_data = handle_missing_values(linked_data, trait_col=trait)
+
+    # 4) Evaluate bias in the trait (and remove biased demographics if any)
+    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+    # 5) Final validation
+    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=final_data,
+        note="Trait data successfully extracted; row renamed to trait for linking."
+    )
+
+    # 6) If the dataset is usable, save
+    if is_usable:
+        final_data.to_csv(out_data_file)
+else:
+    # If the clinical file does not exist, the trait is unavailable
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,
+        df=empty_df,
+        note="No trait data was found; linking and final dataset output are skipped."
+    )

p1/preprocess/Esophageal_Cancer/code/GSE156915.py

@@ -0,0 +1,71 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE156915"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE156915"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Esophageal_Cancer/GSE156915.csv"
+out_gene_data_file = "./output/preprocess/1/Esophageal_Cancer/gene_data/GSE156915.csv"
+out_clinical_data_file = "./output/preprocess/1/Esophageal_Cancer/clinical_data/GSE156915.csv"
+json_path = "./output/preprocess/1/Esophageal_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 background info stating "Whole transcriptome" data were used, we set is_gene_available to True.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# The sample characteristics do not provide information for our trait ("Esophageal_Cancer"), nor age or gender.
+# Hence, all rows are None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define minimal conversion functions (they will not be used since all rows are None).
+def convert_trait(value: str) -> float:
+    return None
+
+def convert_age(value: str) -> float:
+    return None
+
+def convert_gender(value: str) -> int:
+    return None
+
+# 3. Save Metadata (initial filtering)
+# Trait data availability is determined by 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
+)
+print("is_usable:", is_usable)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip the clinical feature extraction step.

p1/preprocess/Esophageal_Cancer/code/GSE218109.py

@@ -0,0 +1,211 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE218109"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE218109"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Esophageal_Cancer/GSE218109.csv"
+out_gene_data_file = "./output/preprocess/1/Esophageal_Cancer/gene_data/GSE218109.csv"
+out_clinical_data_file = "./output/preprocess/1/Esophageal_Cancer/clinical_data/GSE218109.csv"
+json_path = "./output/preprocess/1/Esophageal_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 this dataset is likely to contain gene expression data
+is_gene_available = True  # Based on "Transcriptional profiling" info
+
+# 2.1 Identify availability of trait, age, and gender
+#    The dictionary shows "tissue" is constant (everyone has ESCC), so no variation => no valid trait data
+trait_row = None
+
+#    Key 1 contains multiple ages
+age_row = 1
+
+#    Key 0 contains sex categories (M, F)
+gender_row = 0
+
+# 2.2 Define data type conversion functions
+
+def convert_trait(x: str):
+    # Trait data not available; always return None
+    return None
+
+def convert_age(x: str):
+    # Example: "age: 45" -> 45 (int). Convert errors to None
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    try:
+        return float(parts[1].strip())
+    except ValueError:
+        return None
+
+def convert_gender(x: str):
+    # Example: "Sex: M" -> 1, "Sex: F" -> 0
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'm':
+        return 1
+    elif val == 'f':
+        return 0
+    else:
+        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. Clinical Feature Extraction (skip if trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,  # assuming the DataFrame is available in variable 'clinical_data'
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical_df))
+    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 index, these are numeric identifiers rather than typical human gene symbols.
+
+# Conclude by indicating if gene mapping is required:
+requires_gene_mapping = True
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1. Decide which columns represent the probe IDs and the gene symbols
+#    From the dictionary preview, it seems "ID" is the probe identifier and "GENE_SYMBOL" holds the gene symbol field.
+
+# 2. Create a gene mapping dataframe
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",          # Probe identifier column
+    gene_col="GENE_SYMBOL"  # Gene symbol column
+)
+
+# 3. Convert the probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,  # The probe-level DataFrame previously read
+    mapping_df=mapping_df
+)
+
+# For verification, let's inspect the updated gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+print("First few gene symbols after mapping:", gene_data.index[:10].tolist())
+import os
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2
+if os.path.exists(out_clinical_data_file):
+    # 2) Link the clinical and gene expression data
+    # Load the single-row clinical CSV without forcing an index column
+    tmp_df = pd.read_csv(out_clinical_data_file, header=0)
+    # Rename the single row to the trait. Now columns = sample IDs, index = [trait].
+    tmp_df.index = [trait]
+    selected_clinical_df = tmp_df
+
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3) Handle missing values
+    final_data = handle_missing_values(linked_data, trait_col=trait)
+
+    # 4) Evaluate bias in the trait (and remove biased demographics if any)
+    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+    # 5) Final validation
+    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=final_data,
+        note="Trait data successfully extracted; row renamed to trait for linking."
+    )
+
+    # 6) If the dataset is usable, save
+    if is_usable:
+        final_data.to_csv(out_data_file)
+else:
+    # If the clinical file does not exist, the trait is unavailable
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,
+        df=empty_df,
+        note="No trait data was found; linking and final dataset output are skipped."
+    )

p1/preprocess/Esophageal_Cancer/code/GSE55857.py

@@ -0,0 +1,89 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE55857"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE55857"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Esophageal_Cancer/GSE55857.csv"
+out_gene_data_file = "./output/preprocess/1/Esophageal_Cancer/gene_data/GSE55857.csv"
+out_clinical_data_file = "./output/preprocess/1/Esophageal_Cancer/clinical_data/GSE55857.csv"
+json_path = "./output/preprocess/1/Esophageal_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 = False  # This dataset is about small non-coding RNAs (miRNAs), not mRNA gene expression
+
+# 2) Identify rows for trait, age, and gender
+trait_row = 1    # "tissue: ESCC normal"/"tissue: ESCC tumor"
+age_row = None
+gender_row = None
+
+# 2.2) Define conversion functions
+def convert_trait(value: str):
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    label = parts[1].strip().lower()
+    if 'tumor' in label:
+        return 1
+    elif 'normal' in label:
+        return 0
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3) Perform 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) If trait data is available, extract and save clinical features
+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 the extracted clinical data
+    previewed_clinical = preview_df(selected_clinical_df)
+    print(previewed_clinical)
+
+    # Save the clinical data to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)

p1/preprocess/Esophageal_Cancer/code/GSE66258.py

@@ -0,0 +1,74 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE66258"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE66258"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Esophageal_Cancer/GSE66258.csv"
+out_gene_data_file = "./output/preprocess/1/Esophageal_Cancer/gene_data/GSE66258.csv"
+out_clinical_data_file = "./output/preprocess/1/Esophageal_Cancer/clinical_data/GSE66258.csv"
+json_path = "./output/preprocess/1/Esophageal_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 = False  # The dataset focuses on small non-coding RNAs (sncRNAs), which is not standard mRNA expression data.
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on sample characteristics, row 0 has a single unique value: 'tissue: esophageal squamous cell carcinoma (ESCC) tumor'
+# and row 1 has only IDs. Neither age nor gender is provided. Hence, all are effectively unavailable or constant.
+
+trait_row = None   # No varying trait information found
+age_row = None     # No age information
+gender_row = None  # No gender information
+
+# Since none of them are available, we set is_trait_available to False
+is_trait_available = (trait_row is not None)
+
+# We won't define conversion functions because there's no available data to convert.
+# But for completeness, we'll define stubs:
+
+def convert_trait(x: str) -> float:
+    return None
+
+def convert_age(x: str) -> float:
+    return None
+
+def convert_gender(x: str) -> int:
+    return None
+
+# 3. Save Metadata: initial filtering
+# Trait data is not available, so the dataset fails initial filtering on trait availability.
+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
+# Because trait_row is None, we skip this sub-step.

p1/preprocess/Esophageal_Cancer/code/GSE75241.py

@@ -0,0 +1,204 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE75241"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE75241"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Esophageal_Cancer/GSE75241.csv"
+out_gene_data_file = "./output/preprocess/1/Esophageal_Cancer/gene_data/GSE75241.csv"
+out_clinical_data_file = "./output/preprocess/1/Esophageal_Cancer/clinical_data/GSE75241.csv"
+json_path = "./output/preprocess/1/Esophageal_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 gene expression data availability
+is_gene_available = True  # Based on the series title indicating gene expression data
+
+# 2. Identify data availability and define row indices for trait, age, gender
+trait_row = 1      # "tissue: nonmalignant surrounding mucosa" or "tissue: esophageal tumor"
+age_row = None     # Not found
+gender_row = None  # Not found
+
+# 2.2 Define data conversion functions
+
+def convert_trait(value: str):
+    """
+    Convert trait-related strings to binary:
+      - 'nonmalignant' -> 0
+      - 'tumor' -> 1
+    Unknown/empty -> None
+    """
+    if not value or pd.isna(value):
+        return None
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if 'nonmalignant' in val:
+        return 0
+    elif 'tumor' in val:
+        return 1
+    else:
+        return None
+
+def convert_age(value: str):
+    """
+    No age data available for this dataset, so return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    No gender data available for this dataset, so return None.
+    """
+    return None
+
+# 3. Conduct initial filtering to 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 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 the result
+    previewed = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", previewed)
+
+    # Save clinical features to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the provided gene IDs, they appear to be platform-specific probe identifiers rather than standard human gene symbols.
+# Therefore, they require mapping to gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify columns containing matching probe identifiers and gene symbols
+id_col = "ID"  # Matches the probe IDs of the gene expression data
+symbol_col = "gene_assignment"  # Contains gene symbols embedded in the text
+
+# 2. Get the gene-probe mapping DataFrame
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=symbol_col)
+
+# 3. Convert probe-level measurements into gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2
+if os.path.exists(out_clinical_data_file):
+    # 2) Link the clinical and gene expression data
+    # Load the single-row clinical CSV without forcing an index column
+    tmp_df = pd.read_csv(out_clinical_data_file, header=0)
+    # Rename the single row to the trait. Now columns = sample IDs, index = [trait].
+    tmp_df.index = [trait]
+    selected_clinical_df = tmp_df
+
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3) Handle missing values
+    final_data = handle_missing_values(linked_data, trait_col=trait)
+
+    # 4) Evaluate bias in the trait (and remove biased demographics if any)
+    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+    # 5) Final validation
+    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=final_data,
+        note="Trait data successfully extracted; row renamed to trait for linking."
+    )
+
+    # 6) If the dataset is usable, save
+    if is_usable:
+        final_data.to_csv(out_data_file)
+else:
+    # If the clinical file does not exist, the trait is unavailable
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,
+        df=empty_df,
+        note="No trait data was found; linking and final dataset output are skipped."
+    )

p1/preprocess/Esophageal_Cancer/code/GSE77790.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE77790"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE77790"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Esophageal_Cancer/GSE77790.csv"
+out_gene_data_file = "./output/preprocess/1/Esophageal_Cancer/gene_data/GSE77790.csv"
+out_clinical_data_file = "./output/preprocess/1/Esophageal_Cancer/clinical_data/GSE77790.csv"
+json_path = "./output/preprocess/1/Esophageal_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 "Agilent whole genome microarrays" mention
+
+# 2) Identify availability of variables and define conversion functions
+
+# From the sample characteristics dictionary, row 1 contains labels like
+# 'cell type: lung squamous cell carcinoma', 'cell type: esophageal cancer', etc.
+# We can use row 1 to infer the presence or absence of "esophageal cancer".
+trait_row = 1  # We have multiple distinct values including "esophageal cancer", so it's valid
+
+# No row indicates age or gender information. Hence, set them to None.
+age_row = None
+gender_row = None
+
+# Conversion function for the trait. We map "esophageal cancer" to 1, all others to 0.
+def convert_trait(value: str) -> int:
+    """
+    Convert cell type strings to a binary indicator for 'esophageal cancer'.
+    Unknown or unrecognized strings return 0 by default (non-esophageal).
+    """
+    # Typically the format is "cell type: something"
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return 0
+    label = parts[1].strip().lower()
+    if 'esophageal' in label:
+        return 1
+    return 0
+
+# Since age_row = None and gender_row = None, we define placeholder converters
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    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) If trait_row is available, extract and preview clinical features, then save
+if trait_row is not None:
+    clinical_features_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Preview of selected clinical features:")
+    print(preview_df(clinical_features_df, n=5))
+    clinical_features_df.to_csv(out_clinical_data_file, index=True)
+# 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 IDs observed, these are not standard human gene symbols.
+# Hence, they would require mapping to human gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1) Identify columns for gene ID and gene symbol
+#    From the annotation preview, 'ID' matches the probe ID used in gene_data,
+#    and 'GENE_SYMBOL' stores the gene symbols.
+
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="GENE_SYMBOL"
+)
+
+# 2) Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,
+    mapping_df=mapping_df
+)
+
+# (Optional) Preview the mapped gene expression data
+print("Preview of mapped gene expression data:")
+print(preview_df(gene_data, n=5))
+import os
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2
+if os.path.exists(out_clinical_data_file):
+    # 2) Link the clinical and gene expression data
+    # Load the clinical data with its row index (features) from the CSV
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0, index_col=0)
+
+    # Link
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3) Handle missing values
+    final_data = handle_missing_values(linked_data, trait_col=trait)
+
+    # 4) Evaluate bias in the trait (and remove biased demographics if any)
+    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+    # 5) Final validation
+    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=final_data,
+        note="Trait data successfully extracted; index set from CSV directly."
+    )
+
+    # 6) If the dataset is usable, save
+    if is_usable:
+        final_data.to_csv(out_data_file)
+else:
+    # If the clinical file does not exist, the trait is unavailable
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,
+        df=empty_df,
+        note="No trait data was found; linking and final dataset output are skipped."
+    )

p1/preprocess/Esophageal_Cancer/code/TCGA.py

@@ -0,0 +1,104 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Esophageal_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Esophageal_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Esophageal_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Esophageal_Cancer/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify subdirectories under tcga_root_dir
+subdirectories = os.listdir(tcga_root_dir)
+
+trait_subdir = None
+for d in subdirectories:
+    # Check if the directory name contains "esophageal" or "esca" (lowercase match)
+    if "esophageal" in d.lower() or "esca" in d.lower():
+        trait_subdir = d
+        break
+
+# 2. If none found, skip this trait
+if not trait_subdir:
+    print(f"No suitable subdirectory found for trait '{trait}'. Skipping...")
+    is_gene_available = False
+    is_trait_available = False
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available
+    )
+else:
+    # Identify the paths to the clinical and genetic data files
+    full_subdir_path = os.path.join(tcga_root_dir, trait_subdir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(full_subdir_path)
+
+    # 3. Load data into DataFrames
+    clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')
+
+    # 4. Print the column names of the clinical data for inspection
+    print("Clinical Data Columns:")
+    print(clinical_df.columns.tolist())
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "age_began_smoking_in_years"]
+candidate_gender_cols = ["gender"]
+
+extracted_cols = candidate_age_cols + candidate_gender_cols
+
+if extracted_cols:
+    subset_clinical = clinical_df[extracted_cols]
+    preview_data = subset_clinical.head(5).to_dict(orient='list')
+    print(preview_data)
+# Step 1: Choose the best columns for age and gender based on the data inspection
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+# Step 2: Print out the chosen columns
+print(f"age_col: {age_col}")
+print(f"gender_col: {gender_col}")
+# 1) Extract and standardize clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Normalize gene symbols
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3) Link clinical and genetic data
+linked_data = selected_clinical_df.join(normalized_gene_df.T, how='inner')
+
+# 4) Handle missing values
+linked_data_clean = handle_missing_values(linked_data, trait)
+
+# 5) Determine biased features
+trait_biased, linked_data_no_bias = judge_and_remove_biased_features(linked_data_clean, trait)
+
+# 6) Final quality validation
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data_no_bias,
+    note="Endometrioid Cancer TCGA cohort processed successfully."
+)
+
+# 7) Save usable data
+if is_usable:
+    linked_data_no_bias.to_csv(out_data_file)

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p1/preprocess/Essential_Thrombocythemia/clinical_data/GSE174060.csv

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