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p1/preprocess/Multiple_Endocrine_Neoplasia_Type_2/code/GSE19987.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_Endocrine_Neoplasia_Type_2"
+cohort = "GSE19987"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_Endocrine_Neoplasia_Type_2"
+in_cohort_dir = "../DATA/GEO/Multiple_Endocrine_Neoplasia_Type_2/GSE19987"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_Endocrine_Neoplasia_Type_2/GSE19987.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_Endocrine_Neoplasia_Type_2/gene_data/GSE19987.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_Endocrine_Neoplasia_Type_2/clinical_data/GSE19987.csv"
+json_path = "./output/preprocess/1/Multiple_Endocrine_Neoplasia_Type_2/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  # This dataset likely contains gene expression data based on the background info.
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Identify the row keys for trait, age, and gender
+#   - We found "MEN2A" in the 'genetic class:' row, which is indexed as 1.
+#   - Age and gender data are not available in the sample characteristics dictionary.
+trait_row = 1
+age_row = None
+gender_row = None
+
+# 2.2 Define data type conversions
+
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert the genetic class field to 1 for MEN2A and 0 for any other genetic class.
+    Unknown or malformed values become None.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().upper()
+    return 1 if val == 'MEN2A' else 0
+
+convert_age = None     # Not applicable because age data is not available
+convert_gender = None  # Not applicable because gender data is not available
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait data is available)
+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
+    )
+
+    # Preview and save the clinical features
+    preview_info = preview_df(clinical_features_df, n=5, max_items=200)
+    print(preview_info)
+
+    clinical_features_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the provided identifiers (e.g., '1007_s_at', '1053_at', etc.), these are Affymetrix probe IDs.
+# They are not direct gene symbols. Therefore, 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))
+# STEP6: Gene Identifier Mapping
+# 1. Identify the corresponding columns in the annotation DataFrame for the probes and gene symbols.
+#    From the preview, 'ID' matches the gene expression data index, and 'Gene Symbol' appears to contain the gene symbols.
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 2. Map probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For verification, print out some info about the resulting gene_data
+print("Gene mapping complete. Gene expression data now has shape:", gene_data.shape)
+print("First 20 gene symbols after mapping:", list(gene_data.index[:20]))
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    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_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Multiple_Endocrine_Neoplasia_Type_2/code/TCGA.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_Endocrine_Neoplasia_Type_2"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_Endocrine_Neoplasia_Type_2/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_Endocrine_Neoplasia_Type_2/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_Endocrine_Neoplasia_Type_2/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Multiple_Endocrine_Neoplasia_Type_2/cohort_info.json"

p1/preprocess/Multiple_sclerosis/GSE131281.csv

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p1/preprocess/Multiple_sclerosis/clinical_data/GSE131281.csv

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p1/preprocess/Multiple_sclerosis/clinical_data/GSE131282.csv

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+,GSM3768507,GSM3768508,GSM3768509,GSM3768510,GSM3768511,GSM3768512,GSM3768513,GSM3768514,GSM3768515,GSM3768516,GSM3768517,GSM3768518,GSM3768519,GSM3768520,GSM3768521,GSM3768522,GSM3768523,GSM3768524,GSM3768525,GSM3768526,GSM3768527,GSM3768528,GSM3768529,GSM3768530,GSM3768531,GSM3768532,GSM3768533,GSM3768534,GSM3768535,GSM3768536,GSM3768537,GSM3768538,GSM3768539,GSM3768540,GSM3768541,GSM3768542,GSM3768543,GSM3768544,GSM3768545,GSM3768546,GSM3768547,GSM3768548,GSM3768549,GSM3768550,GSM3768551,GSM3768552,GSM3768553,GSM3768554,GSM3768555,GSM3768556,GSM3768557,GSM3768558,GSM3768559,GSM3768560,GSM3768561,GSM3768562,GSM3768563,GSM3768564,GSM3768565,GSM3768566,GSM3768567,GSM3768568,GSM3768569,GSM3768570,GSM3768571,GSM3768572,GSM3768573,GSM3768574,GSM3768575,GSM3768576,GSM3768577,GSM3768578,GSM3768579,GSM3768580,GSM3768581,GSM3768582,GSM3768583,GSM3768584,GSM3768613,GSM3768614,GSM3768616,GSM3768617,GSM3768619,GSM3768620,GSM3768621,GSM3768623,GSM3768624,GSM3768625,GSM3768626,GSM3768627,GSM3768628,GSM3768629,GSM3768630,GSM3768631,GSM3768632,GSM3768633,GSM3768634,GSM3768635,GSM3768636,GSM3768637,GSM3768638,GSM3768639,GSM3768640,GSM3768641,GSM3768642,GSM3768643,GSM3768644,GSM3768645,GSM3768646,GSM3768647,GSM3768648,GSM3768649,GSM3768650,GSM3768651,GSM3768652,GSM3768653,GSM3768654,GSM3768655,GSM3768656,GSM3768657,GSM3768658,GSM3768659,GSM3768660,GSM3768661,GSM3768662,GSM3768663,GSM3768664,GSM3768665,GSM3768666,GSM3768667,GSM3768668,GSM3768669,GSM3768670,GSM3768671,GSM3768672,GSM3768673,GSM3768674,GSM3768675,GSM3768676,GSM3768677,GSM3768678,GSM3768679,GSM3768680,GSM3768681,GSM3768682,GSM3768683,GSM3768684,GSM3768685,GSM3768686,GSM3768687,GSM3768688,GSM3768689,GSM3768690,GSM3768691,GSM3768692,GSM3768693,GSM3768694,GSM3768695,GSM3768696,GSM3768697,GSM3768698,GSM3768699,GSM3768700,GSM3768701,GSM3768702,GSM3768703,GSM3768704,GSM3768705,GSM3768706,GSM3768707,GSM3768708,GSM3768709,GSM3768710,GSM3768711,GSM3768712,GSM3768713,GSM3768714,GSM3768715,GSM3768716,GSM3768717,GSM3768718,GSM3768719,GSM3768720,GSM3768721
+Multiple_sclerosis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,58.0,59.0,80.0,63.0,47.0,78.0,59.0,88.0,45.0,45.0,61.0,50.0,54.0,78.0,80.0,61.0,45.0,69.0,39.0,58.0,78.0,56.0,44.0,58.0,78.0,58.0,58.0,80.0,58.0,56.0,78.0,42.0,58.0,58.0,50.0,78.0,92.0,54.0,71.0,58.0,39.0,78.0,78.0,56.0,58.0,54.0,45.0,59.0,45.0,77.0,78.0,56.0,44.0,58.0,78.0,34.0,58.0,63.0,78.0,78.0,58.0,92.0,58.0,69.0,58.0,49.0,47.0,78.0,45.0,58.0,70.0,56.0,58.0,71.0,45.0,78.0,78.0,49.0,58.0,92.0,56.0,35.0,80.0,56.0,84.0,75.0,38.0,59.0,77.0,58.0,78.0,64.0,56.0,95.0,60.0,78.0,75.0,58.0,78.0,75.0,51.0,56.0,64.0,77.0,58.0,78.0,60.0,39.0,47.0,87.0,75.0,88.0,64.0,75.0,35.0,58.0,39.0,56.0,61.0,78.0,84.0,73.0,59.0,75.0,47.0,78.0,77.0,39.0,60.0,77.0,49.0,89.0,75.0,58.0,58.0,84.0,70.0,47.0,77.0,58.0,56.0,60.0,75.0,58.0,88.0,92.0,45.0,59.0,84.0,78.0,84.0,60.0,75.0,58.0,58.0,49.0,51.0,58.0,78.0,77.0,35.0,84.0,49.0,75.0,75.0,61.0,75.0,78.0,47.0,58.0,39.0,78.0,77.0,87.0,35.0,45.0,84.0,70.0,58.0,73.0,45.0,78.0,64.0,58.0
+Gender,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0

p1/preprocess/Multiple_sclerosis/clinical_data/GSE135511.csv

@@ -0,0 +1,2 @@
+GSM4013300,GSM4013301,GSM4013302,GSM4013303,GSM4013304,GSM4013305,GSM4013306,GSM4013307,GSM4013308,GSM4013309,GSM4013310,GSM4013311,GSM4013312,GSM4013313,GSM4013314,GSM4013315,GSM4013316,GSM4013317,GSM4013318,GSM4013319,GSM4013320,GSM4013321,GSM4013322,GSM4013323,GSM4013324,GSM4013325,GSM4013326,GSM4013327,GSM4013328,GSM4013329,GSM4013330,GSM4013331,GSM4013332,GSM4013333,GSM4013334,GSM4013335,GSM4013336,GSM4013337,GSM4013338,GSM4013339,GSM4013340,GSM4013341,GSM4013342,GSM4013343,GSM4013344,GSM4013345,GSM4013346,GSM4013347,GSM4013348,GSM4013349
+0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p1/preprocess/Multiple_sclerosis/code/GSE131279.py

@@ -0,0 +1,134 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE131279"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE131279"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_sclerosis/GSE131279.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_sclerosis/gene_data/GSE131279.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_sclerosis/clinical_data/GSE131279.csv"
+json_path = "./output/preprocess/1/Multiple_sclerosis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Decide if the dataset likely contains gene expression data
+is_gene_available = True  # Based on the series summary, gene expression was analyzed
+
+# 2. Determine data availability for trait, age, gender
+#    For this dataset, all samples are MS (subtypes only), so there's no variability in "Multiple_sclerosis" status.
+trait_row = None  # No separate control vs MS column; "ms type" subtypes do not provide control vs MS info
+
+# We see that age is given under key "2" as "age at death: <value>"
+age_row = 2
+
+# We see that gender is given under key "1" as "Sex: F" or "Sex: M"
+gender_row = 1
+
+# 2.2 Define conversion functions
+def convert_trait(value: str):
+    # Trait data is not available in a variable sense, so always None
+    return None
+
+def convert_age(value: str):
+    # Example: "age at death: 58" -> 58.0
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    raw_value = parts[1].strip()
+    try:
+        return float(raw_value)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    # Example: "Sex: F" -> 0, "Sex: M" -> 1
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    gender_str = parts[1].strip().upper()
+    if gender_str == 'F':
+        return 0
+    elif gender_str == 'M':
+        return 1
+    return 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. Since trait_row is None, we skip the clinical feature extraction step
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The given identifiers (e.g., 'ILMN_1343048') are Illumina probe IDs, not standard human gene symbols.
+# Therefore, they need to be mapped to gene symbols.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1. Identify the columns in 'gene_annotation' that match the gene expression data index (ID) and the gene symbols (Symbol).
+# 2. Extract these columns and create a gene mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Convert probe-level data to gene-level data using the mapping, distributing multiple-gene mappings and summing up.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7
+
+# 1. Normalize the gene expression data to standard gene symbols and save the result.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Since trait_row is None from a previous step, we know trait data is not available for this cohort.
+# We therefore skip linking with clinical data and final data handling, because there's no trait column.
+
+is_gene_available = True  # As determined earlier
+is_trait_available = False  # No trait data found or it's all the same subtype with no control
+
+# According to the library, for cohorts that fail trait requirements, we do not finalize.
+# 2. Save metadata with initial filtering (is_final=False). This logs that trait data is unavailable.
+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("No trait data available, dataset deemed not usable for association studies. Skipping final data save.")

p1/preprocess/Multiple_sclerosis/code/GSE131281.py

@@ -0,0 +1,173 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE131281"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE131281"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_sclerosis/GSE131281.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_sclerosis/gene_data/GSE131281.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_sclerosis/clinical_data/GSE131281.csv"
+json_path = "./output/preprocess/1/Multiple_sclerosis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Decide if the dataset likely contains gene expression data
+#    Based on the background info about differential gene expression in cortical grey matter,
+#    it appears to be a gene expression dataset (not miRNA-only or methylation).
+is_gene_available = True
+
+# 2) Identify data availability for trait, age, and gender
+#    From inspection:
+#    - The "patient id: M##" vs "patient id: C##" in row 0 can be used to determine MS vs control.
+#    - Row 2, "age at death: ##", indicates the age.
+#    - Row 1, "Sex: F" or "Sex: M", indicates gender.
+trait_row = 0
+age_row = 2
+gender_row = 1
+
+# 2.2) Define data type conversion functions
+def convert_trait(x: str):
+    # Example input: "patient id: M06"
+    val = x.split(":", 1)[-1].strip()  # e.g. "M06"
+    if val.startswith("M"):
+        return 1
+    elif val.startswith("C"):
+        return 0
+    else:
+        return None
+
+def convert_age(x: str):
+    # Example input: "age at death: 58"
+    val = x.split(":", 1)[-1].strip()  # e.g. "58"
+    if val.isdigit():
+        return float(val)
+    else:
+        return None
+
+def convert_gender(x: str):
+    # Example input: "Sex: F"
+    val = x.split(":", 1)[-1].strip()  # e.g. "F"
+    if val.upper().startswith("F"):
+        return 0
+    elif val.upper().startswith("M"):
+        return 1
+    else:
+        return None
+
+# 3) Conduct initial filtering and 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) If trait info is available, extract clinical features and save
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview clinical data
+    preview = preview_df(selected_clinical)
+    print("Clinical data preview:", preview)
+
+    # Save the extracted clinical data
+    selected_clinical.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 observation of ILMN probe identifiers, they are not standard human gene symbols.
+# Therefore, they require mapping to 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) From our examination of the gene annotation preview and the gene expression data,
+#    we see that the "ID" column in gene_annotation matches the probe IDs in gene_data,
+#    and the "Symbol" column in gene_annotation stores gene symbols.
+
+# 2) Get the gene mapping dataframe using "ID" for the probe column and "Symbol" for the gene symbol column.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")
+
+# 3) Map probe-level measurements to gene-level data using the apply_gene_mapping function.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) For quick observation, let's print a small preview of the mapped gene_data
+print("Mapped gene_data preview:")
+print(gene_data.head())
+# STEP 7
+
+# 1. Normalize the gene expression data to standard gene symbols and save the result.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# 2. Link the clinical and genetic data on sample IDs.
+# Replace "selected_clinical_df" with the correct variable name "selected_clinical"
+linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)
+
+# 3. Handle missing values in the linked data.
+linked_data = handle_missing_values(df=linked_data, trait_col=trait)
+
+# 4. Determine whether the trait and demographics (if any) are severely biased.
+is_biased, linked_data = judge_and_remove_biased_features(df=linked_data, trait=trait)
+
+# 5. Perform final quality validation and save metadata.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Dataset includes multiple sclerosis trait from post-mortem motor cortex samples."
+)
+
+# 6. If the dataset is usable, save the final linked data.
+if is_usable:
+    linked_data.to_csv(out_data_file)
+    print("Final linked data saved to:", out_data_file)
+else:
+    print("Dataset is deemed not usable. No final data saved.")

p1/preprocess/Multiple_sclerosis/code/GSE131282.py

@@ -0,0 +1,175 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE131282"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE131282"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_sclerosis/GSE131282.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_sclerosis/gene_data/GSE131282.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_sclerosis/clinical_data/GSE131282.csv"
+json_path = "./output/preprocess/1/Multiple_sclerosis/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 description, this dataset likely contains gene expression data.
+
+# 2. Identify the availability of trait, age, and gender
+#    and define their corresponding row indices. Use None if not available.
+trait_row = 4   # "ms type: ..." or "pm simple: ..." can be used to infer MS vs. control
+age_row = 2     # "age at death: ..."
+gender_row = 1  # "Sex: F" or "Sex: M"
+
+# 2.2 Define the conversion functions
+def convert_trait(x: str):
+    parts = x.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # If the string contains "ms type:", interpret as MS=1; if "pm simple:", interpret as control=0.
+    if "ms type:" in x.lower():
+        # Even if it contains '?', it's still MS, just unknown subtype.
+        return 1
+    elif "pm simple:" in x.lower():
+        return 0
+    else:
+        return None
+
+def convert_age(x: str):
+    parts = x.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    if val == "?":
+        return None
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(x: str):
+    parts = x.split(":", 1)
+    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. 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 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 = preview_df(selected_clinical_df)
+    print(preview)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These are Illumina microarray probe identifiers (e.g., ILMN_1343048).
+# They are not official 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))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns for probe identifiers and gene symbols in the annotation dataframe
+#    From our inspection, the probe identifier column is "ID" and the gene symbol column is "Symbol".
+
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",       # Probe/array identifier column
+    gene_col="Symbol"    # Gene symbol column
+)
+
+# 2. Map probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# (Optional) Quick inspection to confirm the gene_data structure
+print("Mapped gene expression DataFrame shape:", gene_data.shape)
+print("First 5 mapped gene names:", gene_data.index[:5].tolist())
+# STEP7
+
+# 1. Normalize the gene expression data to standard gene symbols and save the result.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# 2. Link the clinical and genetic data on sample IDs.
+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(df=linked_data, trait_col=trait)
+
+# 4. Determine whether the trait and demographics (if any) are severely biased.
+is_biased, linked_data = judge_and_remove_biased_features(df=linked_data, trait=trait)
+
+# 5. Perform final quality validation and save metadata.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Dataset includes multiple sclerosis trait from post-mortem motor cortex samples."
+)
+
+# 6. If the dataset is usable, save the final linked data.
+if is_usable:
+    linked_data.to_csv(out_data_file)
+    print("Final linked data saved to:", out_data_file)
+else:
+    print("Dataset is deemed not usable. No final data saved.")

p1/preprocess/Multiple_sclerosis/code/GSE135511.py

@@ -0,0 +1,161 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE135511"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE135511"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_sclerosis/GSE135511.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_sclerosis/gene_data/GSE135511.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_sclerosis/clinical_data/GSE135511.csv"
+json_path = "./output/preprocess/1/Multiple_sclerosis/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 indicating "Gene expression profiling..."
+
+# 2) Variable availability and data type conversion
+# From the sample characteristics dictionary, trait is found at index 0. Age and gender are not present.
+trait_row = 0
+age_row = None
+gender_row = None
+
+# Define the conversion functions.
+
+def convert_trait(value: str):
+    """
+    Convert the 'disease state' string to binary:
+    'Healthy Control' -> 0, 'Multiple Sclerosis' -> 1, otherwise None.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'healthy control':
+        return 0
+    elif val == 'multiple sclerosis':
+        return 1
+    return None
+
+def convert_age(value: str):
+    """No age data is available, so always return None."""
+    return None
+
+def convert_gender(value: str):
+    """No gender data is available, so always return None."""
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction 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 extracted clinical features
+    preview_result = preview_df(selected_clinical_df)
+    print("Preview of selected clinical data:", preview_result)
+
+    # Save the 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])
+# The observed identifiers (ILMN_xxxxxxx) are Illumina probe IDs and are not human gene symbols.
+# Hence, they require mapping to official 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 the annotation that match the expression data IDs (e.g., "ID")
+#    and the columns that contain gene symbols (e.g., "Symbol").
+# 2) Get the gene mapping dataframe from the annotation dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3) Convert probe-level expression data to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print some information about the resulting gene-level dataframe
+print("Mapped Gene Data Shape:", gene_data.shape)
+print("Sample of mapped Gene Data index:", gene_data.index[:20])
+# STEP7
+
+# 1. Normalize the gene expression data to standard gene symbols and save the result.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# 2. Link the clinical and genetic data on sample IDs.
+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(df=linked_data, trait_col=trait)
+
+# 4. Determine whether the trait and demographics (if any) are severely biased.
+is_biased, linked_data = judge_and_remove_biased_features(df=linked_data, trait=trait)
+
+# 5. Perform final quality validation and save metadata.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Dataset includes multiple sclerosis trait from post-mortem motor cortex samples."
+)
+
+# 6. If the dataset is usable, save the final linked data.
+if is_usable:
+    linked_data.to_csv(out_data_file)
+    print("Final linked data saved to:", out_data_file)
+else:
+    print("Dataset is deemed not usable. No final data saved.")

p1/preprocess/Multiple_sclerosis/code/GSE141381.py

@@ -0,0 +1,159 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE141381"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE141381"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_sclerosis/GSE141381.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_sclerosis/gene_data/GSE141381.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_sclerosis/clinical_data/GSE141381.csv"
+json_path = "./output/preprocess/1/Multiple_sclerosis/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 dataset description, it's likely gene expression data.
+
+# 2) Variable Availability and Data Type Conversion
+#    From the sample characteristics, we see no variation for "Multiple_sclerosis" (all samples have SPMS),
+#    so 'trait_row' is None. For 'age', row 1 holds multiple numeric entries. For 'gender', row 0 has two distinct values.
+
+trait_row = None  # No varying disease status in the sample characteristics
+age_row = 1
+gender_row = 0
+
+# Define data conversion functions:
+def convert_trait(x: str) -> Optional[int]:
+    """
+    Placeholder for trait conversion. No real usage here since trait_row is None.
+    """
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    """
+    Convert an 'age: 52' style string to a float. 
+    If the value is 'unknown' or invalid, return None.
+    """
+    # Typically split by ':', take the right side, strip and parse
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    if val.lower() == "unknown" or val == "":
+        return None
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(x: str) -> Optional[int]:
+    """
+    Convert 'gender: male' or 'gender: female' to 1 or 0, respectively.
+    Unknown or invalid entries are None.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    return None
+
+# 3) 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) Clinical Feature Extraction
+#    Since trait_row is None, we skip extraction of clinical features.
+# 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 identifiers, they appear to be probe IDs and not standard human gene symbols.
+# Hence, we conclude that they require mapping to gene symbols.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Identify the columns in 'gene_annotation' that match the probe IDs from the expression data
+#    and the gene symbol data, respectively. From inspection, 'ID' matches the expression probe IDs,
+#    and 'gene_assignment' stores the strings containing gene symbols.
+mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')
+
+# 2) Convert probe-level measurements to gene-level sums using the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 3) Optionally, check the size and a few gene IDs after mapping
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 20 gene symbols after mapping:")
+print(gene_data.index[:20])
+# STEP7
+# Since the trait was determined to be unavailable (trait_row is None),
+# we cannot fully link clinical data or perform trait-based quality checks.
+# However, we still normalize the gene expression data and then perform a final
+# validation call to record the dataset's status (unusable for trait-based analysis).
+
+# 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)
+
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# 2. We have no trait data for linking, so skip steps requiring trait information (linking, missing trait handling, etc.).
+
+# 3. Perform final validation to record that the dataset is not usable due to missing trait data.
+#    We must pass a dummy dataframe (the normalized gene data transposed) so the function can run in final mode.
+final_df = normalized_gene_data.T
+
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,       # We do have gene expression data
+    is_trait_available=False,     # No trait data
+    is_biased=False,             # Not applicable, but required by the function
+    df=final_df,
+    note="Trait data is not available, so this dataset is not usable for trait-based analysis."
+)
+
+# 4. Because the dataset is not usable (trait is unavailable), we do NOT save any linked data.
+print("Trait data is not available. The dataset is marked unusable for trait-based analysis. No linked data saved.")

p1/preprocess/Multiple_sclerosis/code/GSE141804.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE141804"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE141804"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_sclerosis/GSE141804.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_sclerosis/gene_data/GSE141804.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_sclerosis/clinical_data/GSE141804.csv"
+json_path = "./output/preprocess/1/Multiple_sclerosis/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
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # based on the series title mentioning 'Gene Expression'
+
+# 2. Variable Availability and Data Type Conversion
+# -------------------------------------------------
+# From the sample characteristics dict, the available keys are 0 for gender, 1 for age.
+# No row appears to contain the trait "Multiple_sclerosis", so trait is not available.
+
+trait_row = None
+age_row = 1
+gender_row = 0
+
+# Define functions to parse the value after the colon:
+def convert_trait(value: str):
+    """
+    This dataset does not have explicit or inferable trait information.
+    Return None directly.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Extract the numeric part after the colon and convert to float.
+    Unknowns are mapped to None.
+    """
+    # Typically something like: "age (years): 42.50"
+    match = re.split(r':\s*', value)
+    if len(match) < 2:
+        return None
+    try:
+        return float(match[1])
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert female => 0, male => 1; unknown => None.
+    """
+    # Typically something like: "gender: Male"
+    match = re.split(r':\s*', value)
+    if len(match) < 2:
+        return None
+    gender_str = match[1].strip().lower()
+    if gender_str == 'female':
+        return 0
+    elif gender_str == 'male':
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata (Initial Filtering)
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip this step (no 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])
+# Observing the gene identifiers, they appear to be microarray probe set IDs from an Affymetrix platform.
+# Therefore, they are not standard gene symbols and 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. Identify the correct columns in the annotation for probe ID and gene symbol.
+#    From the preview, "ID" matches the gene_data index, and "Gene Symbol" contains the gene symbols.
+
+# 2. Create a gene mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Apply the mapping to convert probe-level measurements to gene-level expression.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optionally, confirm the shape or head of the resulting gene_data
+print("Mapped gene_data dimension:", gene_data.shape)
+print("First few gene symbols in the mapped gene_data:")
+print(gene_data.index[:10])
+# STEP7
+# Since the trait was determined to be unavailable (trait_row is None),
+# we cannot link with clinical data or perform final quality checks based on the trait.
+# However, we can still normalize the gene expression data and save it separately.
+
+# 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. Trait data is not available. We therefore skip linking with clinical data,
+#    handling missing trait, bias checking, and final validation.
+print("Trait data is not available. Steps requiring trait information are skipped.")

p1/preprocess/Multiple_sclerosis/code/GSE146383.py

@@ -0,0 +1,135 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE146383"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE146383"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_sclerosis/GSE146383.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_sclerosis/gene_data/GSE146383.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_sclerosis/clinical_data/GSE146383.csv"
+json_path = "./output/preprocess/1/Multiple_sclerosis/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  # From background info indicating a transcriptome study
+
+# 2.1 Identify data availability
+#    No row was found for the trait "Multiple_sclerosis" => treat as not available
+trait_row = None
+
+#    Row 1 clearly corresponds to age, with multiple distinct values
+age_row = 1
+
+#    Row 0 describes gender, with "Female" and "Male"
+gender_row = 0
+
+# 2.2 Define data type conversion functions
+def convert_trait(value: str):
+    """
+    Since trait_row is None, this function won't be used.
+    But we define it to comply with the specification.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Extract the portion after the colon and parse it as float.
+    If parsing fails, return None.
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Extract the portion after the colon, convert Female->0, Male->1, otherwise None.
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip().lower()
+    if val_str.startswith('f'):
+        return 0
+    elif val_str.startswith('m'):
+        return 1
+    else:
+        return None
+
+# 3. Save metadata - initial filtering
+is_trait_available = (trait_row is not None)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Since trait_row is None, we skip the clinical feature extraction step.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the Affymetrix probe set identifiers, these are not human gene symbols and therefore 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. From the preview, we see that "ID" in gene_annotation matches the probe identifiers in gene_data,
+#    and "Gene Symbol" holds the actual gene symbols.
+probe_col = "ID"
+symbol_col = "Gene Symbol"
+
+# 2. Generate a mapping DataFrame between probes and gene symbols
+gene_mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3. Convert from probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+# STEP7
+# Since the trait was determined to be unavailable (trait_row is None),
+# we cannot link with clinical data or perform final quality checks based on the trait.
+# However, we can still normalize the gene expression data and save it separately.
+
+# 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. Trait data is not available. We therefore skip linking with clinical data,
+#    handling missing trait, bias checking, and final validation.
+print("Trait data is not available. Steps requiring trait information are skipped.")

p1/preprocess/Multiple_sclerosis/code/GSE189788.py

@@ -0,0 +1,150 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE189788"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE189788"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_sclerosis/GSE189788.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_sclerosis/gene_data/GSE189788.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_sclerosis/clinical_data/GSE189788.csv"
+json_path = "./output/preprocess/1/Multiple_sclerosis/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  # Affymetrix HU-133A-2 is a standard gene expression array
+
+# 2. Identify data availability for trait, age, and gender
+#    and define the corresponding row keys
+#    Here, all samples have the same trait ("multiple sclerosis"), so it's not useful for associational studies.
+trait_row = None
+age_row = 2
+gender_row = 3
+
+# 2.2 Define conversion functions for trait, age, and gender
+def convert_trait(value: str):
+    """
+    Dummy conversion function for trait. 
+    Although trait_row is None for this dataset (meaning no variability), 
+    the function is provided to fulfill the requirement.
+    """
+    val_str = value.split(':')[-1].strip().lower()
+    # If the dataset had multiple values, we could convert them to 0/1 accordingly.
+    if val_str == "multiple sclerosis":
+        return 1
+    return None
+
+def convert_age(value: str):
+    """
+    Convert the age string (e.g., 'age(years): 43') into a float. 
+    Unknown or invalid values become None.
+    """
+    val_str = value.split(':')[-1].strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert the gender string (e.g., 'gender: female') to binary (female=0, male=1).
+    Unknown values become None.
+    """
+    val_str = value.split(':')[-1].strip().lower()
+    if val_str == "female":
+        return 0
+    elif val_str == "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. Clinical feature extraction is skipped because trait_row is None
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the identifiers ('1007_s_at', '1053_at', etc.), they appear to be Affymetrix probe set IDs, not gene symbols.
+# Therefore, they will 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. Identify the appropriate columns in 'gene_annotation' that match the probe identifiers in 'gene_data.index' 
+#    and provide gene symbols.
+probe_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Construct a mapping DataFrame between the probe identifier and the gene symbol.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level data to gene-level expression by distributing expression values among mapped genes.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+# Since trait_row is None, we have no usable trait data for linking or further clinical analysis.
+# Nevertheless, we still normalize the gene symbols. Then we finalize the metadata to indicate
+# that the dataset is not usable for trait-based analysis. We do not proceed with linking or
+# saving any "linked" data as there is no trait to link.
+
+import pandas as pd
+
+# 1. Normalize gene symbols in the obtained gene data with the 'normalize_gene_symbols_in_index' function.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Because trait data is not available (trait_row is None), skip clinical-genetic linking, 
+#    missing-value handling, bias checks, and saving any linked data.
+
+# 3. Conduct final validation to record that the dataset does not contain trait data.
+#    Provide a dummy DataFrame and a placeholder is_biased value to satisfy function requirements.
+dummy_df = pd.DataFrame()
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,     # We do have gene expression data
+    is_trait_available=False,   # Trait not available for analysis
+    is_biased=False,            # Placeholder
+    df=dummy_df,
+    note="Trait not available; dataset skipped for trait-based analysis."
+)
+
+# Since the dataset is not usable for trait-based analysis, do not save any linked data.

p1/preprocess/Multiple_sclerosis/code/GSE193442.py

@@ -0,0 +1,99 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE193442"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE193442"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_sclerosis/GSE193442.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_sclerosis/gene_data/GSE193442.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_sclerosis/clinical_data/GSE193442.csv"
+json_path = "./output/preprocess/1/Multiple_sclerosis/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. Gene Expression Data Availability
+# From the background info ("Transcriptional profiling..."), we infer it is likely gene expression data.
+is_gene_available = True
+
+# Step 2. Variable Availability and Data Type Conversion
+# Sample characteristics dictionary:
+# {0: ['tissue: PBMC'], 1: ['cell type: KIR+ CD8 T']}
+#
+# We do not find any key indicating the trait "Multiple_sclerosis", age, or gender. 
+# Hence all these rows are None.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define data conversion functions (they won't be applied since rows are None, but are required by the specification).
+def convert_trait(value: str):
+    """Convert trait data to binary (0 or 1), or return None for unknown."""
+    if not value:
+        return None
+    # Extract the portion after the colon
+    parts = value.split(':')
+    raw_val = parts[-1].strip() if len(parts) > 1 else value.strip()
+    # No actual data is available in this dataset, so default to None here.
+    return None
+
+def convert_age(value: str):
+    """Convert age data to a continuous variable, or return None for unknown."""
+    if not value:
+        return None
+    parts = value.split(':')
+    raw_val = parts[-1].strip() if len(parts) > 1 else value.strip()
+    # No actual data is available in this dataset, so default to None here.
+    return None
+
+def convert_gender(value: str):
+    """Convert gender data to binary (female=0, male=1), or return None for unknown."""
+    if not value:
+        return None
+    parts = value.split(':')
+    raw_val = parts[-1].strip() if len(parts) > 1 else value.strip()
+    # No actual data is available in this dataset, so default to None here.
+    return None
+
+# Step 3. Initial filtering and save metadata
+# 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
+)
+
+# Step 4. Clinical Feature Extraction
+# Since trait_row is None, we skip this step as instructed.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])

p1/preprocess/Multiple_sclerosis/code/GSE203241.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE203241"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE203241"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_sclerosis/GSE203241.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_sclerosis/gene_data/GSE203241.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_sclerosis/clinical_data/GSE203241.csv"
+json_path = "./output/preprocess/1/Multiple_sclerosis/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 series description mentioning "blood mononuclear cell transcriptome",
+# we consider that gene expression data is available for this cohort.
+is_gene_available = True
+
+# 2) Variable Availability
+# From the sample characteristics dictionary, we see:
+#   key=0 => gender: Male/Female
+#   key=1 => age (years): numeric
+# No evident row containing trait (Multiple_sclerosis) data is found; hence trait_row = None.
+trait_row = None
+age_row = 1
+gender_row = 0
+
+# 2.2) Data Type Conversion Functions
+
+def convert_trait(x: str):
+    """
+    Since 'trait_row' is None for this dataset, this function won't be called.
+    We define a dummy function here for demonstration.
+    """
+    return None
+
+def convert_age(x: str):
+    """
+    Convert 'age (years): X' to a float. If conversion fails or the value is invalid, return None.
+    """
+    try:
+        # Split by colon and strip whitespace
+        parts = x.split(':')
+        if len(parts) < 2:
+            return None
+        val_str = parts[1].strip()
+        return float(val_str)
+    except:
+        return None
+
+def convert_gender(x: str):
+    """
+    Convert 'gender: Male/Female' to binary (Male=1, Female=0). Unknown values => None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip().lower()
+    if val_str == 'male':
+        return 1
+    elif val_str == 'female':
+        return 0
+    return None
+
+# 3) Save Metadata with initial filtering
+# Trait data availability is determined by whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+# Perform initial validation and save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction is only performed if trait_row is not None.
+# Since trait_row = None, we skip this step.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the identifiers like "1007_s_at", "1053_at", etc., these are Affymetrix probe sets rather than
+# direct human gene symbols. Therefore, mapping to 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))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the annotation columns that match the probe IDs from gene expression data and the gene symbols.
+probe_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Extract the corresponding mapping from the gene_annotation dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply the mapping to convert the probe-level measurements in 'gene_data' to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optional: Check the resulting shape or partial content
+print("Mapped gene_data dimensions:", gene_data.shape)
+print("Mapped gene_data (first 5 rows):")
+print(gene_data.head(5))
+# STEP7
+# Since trait_row = None, there is no trait data to link. We cannot do final validation because
+# validate_and_save_cohort_info(...) raises ValueError if is_final=True but df or is_biased is None.
+# Therefore, we only normalize and save gene expression data, and skip the rest.
+
+# 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. Because trait data is unavailable, we skip clinical–genetic linking, missing value handling, and bias checks.
+# 3. We also skip final validation, since trait data is not available.

p1/preprocess/Multiple_sclerosis/code/TCGA.py

@@ -0,0 +1,61 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Multiple_sclerosis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Multiple_sclerosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Multiple_sclerosis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Multiple_sclerosis/cohort_info.json"
+
+import os
+
+# Step 1: Identify subdirectory that might relate to "Multiple_sclerosis"
+subdirs = [
+    '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)'
+]
+
+suitable_subdir = None
+
+for sd in subdirs:
+    if "multiple_sclerosis" in sd.lower():
+        suitable_subdir = sd
+        break
+
+if not suitable_subdir:
+    print("No suitable subdirectory found for trait 'Multiple_sclerosis'. Skipping this trait.")
+    # Mark as completed but unavailable in metadata
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # Step 2: Identify clinical and genetic file paths
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, suitable_subdir))
+
+    # Step 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')
+
+    # Step 4: Print clinical data columns
+    print("Clinical Data Columns:", clinical_df.columns.tolist())

p1/preprocess/Multiple_sclerosis/cohort_info.json

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+{"GSE203241": {"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}, "GSE193442": {"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}, "GSE189788": {"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": "Trait not available; dataset skipped for trait-based analysis."}, "GSE146383": {"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}, "GSE141804": {"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}, "GSE141381": {"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": "Trait data is not available, so this dataset is not usable for trait-based analysis."}, "GSE135511": {"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": 50, "note": "Dataset includes multiple sclerosis trait from post-mortem motor cortex samples."}, "GSE131282": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 184, "note": "Dataset includes multiple sclerosis trait from post-mortem motor cortex samples."}, "GSE131281": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 106, "note": "Dataset includes multiple sclerosis trait from post-mortem motor cortex samples."}, "GSE131279": {"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}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p1/preprocess/Multiple_sclerosis/gene_data/GSE131279.csv

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p1/preprocess/Multiple_sclerosis/gene_data/GSE141381.csv

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p1/preprocess/Multiple_sclerosis/gene_data/GSE146383.csv

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p1/preprocess/Obesity/GSE84046.csv

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p1/preprocess/Obesity/clinical_data/GSE123086.csv

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