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+Schizophrenia,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 diff --git a/p3/preprocess/Schizophrenia/clinical_data/GSE120340.csv b/p3/preprocess/Schizophrenia/clinical_data/GSE120340.csv new file mode 100644 index 0000000000000000000000000000000000000000..b5441605a9a608521db25f06d8cadb26bb78dd32 --- /dev/null +++ b/p3/preprocess/Schizophrenia/clinical_data/GSE120340.csv @@ -0,0 +1,2 @@ +,GSM3398477,GSM3398478,GSM3398479,GSM3398480,GSM3398481,GSM3398482,GSM3398483,GSM3398484,GSM3398485,GSM3398486,GSM3398487,GSM3398488,GSM3398489,GSM3398490,GSM3398491,GSM3398492,GSM3398493,GSM3398494,GSM3398495,GSM3398496,GSM3398497,GSM3398498,GSM3398499,GSM3398500,GSM3398501,GSM3398502,GSM3398503,GSM3398504,GSM3398505,GSM3398506 +Schizophrenia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,,, diff --git a/p3/preprocess/Schizophrenia/clinical_data/GSE120342.csv b/p3/preprocess/Schizophrenia/clinical_data/GSE120342.csv new file mode 100644 index 0000000000000000000000000000000000000000..85fd65cabf71e80f151e6de3693c41a2e7ad4a18 --- /dev/null +++ b/p3/preprocess/Schizophrenia/clinical_data/GSE120342.csv @@ -0,0 +1,2 @@ +,GSM3398507,GSM3398508,GSM3398509,GSM3398510,GSM3398511,GSM3398512,GSM3398513,GSM3398514,GSM3398515,GSM3398516,GSM3398517 +Schizophrenia,0.0,0.0,0.0,0.0,1.0,1.0,1.0,,,, diff --git a/p3/preprocess/Schizophrenia/clinical_data/GSE145554.csv b/p3/preprocess/Schizophrenia/clinical_data/GSE145554.csv new file mode 100644 index 0000000000000000000000000000000000000000..a275c4b619bbefeea091a66e94d52fbdb58af650 --- /dev/null +++ b/p3/preprocess/Schizophrenia/clinical_data/GSE145554.csv @@ -0,0 +1,4 @@ +,GSM4321410,GSM4321411,GSM4321412,GSM4321413,GSM4321414,GSM4321415,GSM4321416,GSM4321417,GSM4321418,GSM4321419,GSM4321420,GSM4321421,GSM4321422,GSM4321423,GSM4321424,GSM4321425,GSM4321426,GSM4321427,GSM4321428,GSM4321429,GSM4321430,GSM4321431,GSM4321432,GSM4321433,GSM4321434,GSM4321435,GSM4321436,GSM4321437,GSM4321438,GSM4321439,GSM4321440,GSM4321441,GSM4321442,GSM4321443,GSM4321444,GSM4321445,GSM4321446,GSM4321447,GSM4321448,GSM4321449,GSM4321450,GSM4321451,GSM4321452 +Schizophrenia,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0 +Age,63.0,90.0,83.0,69.0,73.0,84.0,66.0,78.0,65.0,89.0,69.0,74.0,66.0,86.0,79.0,77.0,68.0,59.0,58.0,90.0,72.0,80.0,58.0,57.0,63.0,90.0,83.0,69.0,84.0,66.0,78.0,65.0,89.0,69.0,74.0,66.0,79.0,77.0,68.0,59.0,58.0,90.0,72.0 +Gender,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0 diff --git a/p3/preprocess/Schizophrenia/clinical_data/GSE161986.csv b/p3/preprocess/Schizophrenia/clinical_data/GSE161986.csv new file mode 100644 index 0000000000000000000000000000000000000000..230704d877e7449f46576ca0d5bf05b02ae3354a --- /dev/null +++ b/p3/preprocess/Schizophrenia/clinical_data/GSE161986.csv @@ -0,0 +1,3 @@ +,GSM4929029,GSM4929030,GSM4929031,GSM4929032,GSM4929033,GSM4929034,GSM4929035,GSM4929036,GSM4929037,GSM4929038,GSM4929039,GSM4929040,GSM4929041,GSM4929042,GSM4929043,GSM4929044,GSM4929045,GSM4929046,GSM4929047,GSM4929048,GSM4929049,GSM4929050,GSM4929051,GSM4929052,GSM4929053,GSM4929054,GSM4929055,GSM4929056,GSM4929057,GSM4929058,GSM4929059,GSM4929060,GSM4929061,GSM4929062,GSM4929063 +Schizophrenia,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0 +Age,61.0,44.0,62.0,56.0,63.0,42.0,46.0,56.0,52.0,43.0,59.0,56.0,54.0,46.0,39.0,73.0,56.0,50.0,63.0,50.0,50.0,51.0,64.0,55.0,55.0,47.0,50.0,55.0,53.0,82.0,64.0,73.0,73.0,57.0,59.0 diff --git a/p3/preprocess/Schizophrenia/clinical_data/GSE165813.csv b/p3/preprocess/Schizophrenia/clinical_data/GSE165813.csv new file mode 100644 index 0000000000000000000000000000000000000000..5ea9acae5b8176cf0272b73ed59488a178118113 --- /dev/null +++ b/p3/preprocess/Schizophrenia/clinical_data/GSE165813.csv @@ -0,0 +1,2 @@ +,GSM5906434,GSM5906435,GSM5906436,GSM5906437,GSM5906438,GSM5906439,GSM5906440,GSM5906441,GSM5906442,GSM5906443,GSM5906444,GSM5906445,GSM5906446,GSM5906447,GSM5906448,GSM5906449,GSM5906450,GSM5906451,GSM5906452,GSM5906453,GSM5906454,GSM5906455,GSM5906456,GSM5906457,GSM5906458,GSM5906459,GSM5906460,GSM5906461,GSM5906462,GSM5906463,GSM5906464,GSM5906465,GSM5906466,GSM5906467,GSM5906468,GSM5906469,GSM5906470,GSM5906471,GSM5906472,GSM5906473,GSM5906474,GSM5906475,GSM5906476,GSM5906477,GSM5906478,GSM5906479,GSM5906480,GSM5906481 +Schizophrenia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/p3/preprocess/Schizophrenia/clinical_data/GSE285666.csv b/p3/preprocess/Schizophrenia/clinical_data/GSE285666.csv new file mode 100644 index 0000000000000000000000000000000000000000..ef4d4b1b92f1484eaa796996a076decb894003dd --- /dev/null +++ b/p3/preprocess/Schizophrenia/clinical_data/GSE285666.csv @@ -0,0 +1,2 @@ +,GSM8706502,GSM8706503,GSM8706504,GSM8706505,GSM8706506,GSM8706507,GSM8706508,GSM8706509,GSM8706510,GSM8706511,GSM8706512,GSM8706513,GSM8706514,GSM8706515,GSM8706516,GSM8706517,GSM8706518,GSM8706519,GSM8706520,GSM8706521,GSM8706522,GSM8706523,GSM8706524,GSM8706525,GSM8706526,GSM8706527,GSM8706528,GSM8706529,GSM8706530,GSM8706531,GSM8706532,GSM8706533,GSM8706534,GSM8706535,GSM8706536,GSM8706537,GSM8706538,GSM8706539,GSM8706540,GSM8706541,GSM8706542,GSM8706543,GSM8706544,GSM8706545,GSM8706546,GSM8706547,GSM8706548,GSM8706549,GSM8706550,GSM8706551,GSM8706552,GSM8706553 +Schizophrenia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 diff --git a/p3/preprocess/Schizophrenia/code/GSE165813.py b/p3/preprocess/Schizophrenia/code/GSE165813.py new file mode 100644 index 0000000000000000000000000000000000000000..2e7792f3ce071cf0ad7655b6e18a601e58e4d536 --- /dev/null +++ b/p3/preprocess/Schizophrenia/code/GSE165813.py @@ -0,0 +1,155 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Schizophrenia" +cohort = "GSE165813" + +# Input paths +in_trait_dir = "../DATA/GEO/Schizophrenia" +in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE165813" + +# Output paths +out_data_file = "./output/preprocess/3/Schizophrenia/GSE165813.csv" +out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE165813.csv" +out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE165813.csv" +json_path = "./output/preprocess/3/Schizophrenia/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Yes - Affymetrix array data indicated in title +is_gene_available = True + +# 2. Variable Availability and Data Type +# Trait (Status) - Not directly available in characteristics +# But can be inferred from histological type - fetal/adult brain samples are controls, others are cases +trait_row = 1 + +# Age/Gender not available +age_row = None +gender_row = None + +# Conversion Functions +def convert_trait(value): + if not isinstance(value, str): + return None + value = value.split(': ')[-1].lower().strip() + # Control samples are normal brain tissues + if any(x in value for x in ['fetal', 'adult', 'pediatric']): + return 0 # Control + # Tumor samples + elif any(x in value for x in ['astroblastoma', 'ptpr', 'pxa']): + return 1 # Case + return None + +def convert_age(value): + # Not used since age_row is None + return None + +def convert_gender(value): + # Not used since gender_row is None + return None + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + print("Preview of extracted clinical features:") + print(preview_df(clinical_features)) + clinical_features.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +requires_gene_mapping = False +# 1. Normalize gene symbols +genetic_data = normalize_gene_symbols_in_index(genetic_data) +print("Gene data shape after normalization:", genetic_data.shape) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +genetic_data.to_csv(out_gene_data_file) + +# Load clinical data previously processed +selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) +print("\nClinical data shape:", selected_clinical_df.shape) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data) +print("\nLinked data shape:", linked_data.shape) + +# 3. Handle missing values systematically +if trait in linked_data.columns: + linked_data = handle_missing_values(linked_data, trait) + + # 4. Check for bias in trait and demographic features + trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + + # 5. Final validation and information saving + note = "This dataset contains gene expression data from brain tumor and normal brain samples, with schizophrenia status as the trait." + is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note + ) + + # 6. Save linked data only if usable and not biased + if is_usable and not trait_biased: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) +else: + # Handle case where clinical features were not properly extracted + note = "Failed to extract clinical trait information from sample characteristics." + validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, + is_biased=None, + df=None, + note=note + ) \ No newline at end of file diff --git a/p3/preprocess/Schizophrenia/code/GSE193818.py b/p3/preprocess/Schizophrenia/code/GSE193818.py new file mode 100644 index 0000000000000000000000000000000000000000..530464a2f6bfa813400ed8ddfbdd27e0a9c1fa9d --- /dev/null +++ b/p3/preprocess/Schizophrenia/code/GSE193818.py @@ -0,0 +1,135 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Schizophrenia" +cohort = "GSE193818" + +# Input paths +in_trait_dir = "../DATA/GEO/Schizophrenia" +in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE193818" + +# Output paths +out_data_file = "./output/preprocess/3/Schizophrenia/GSE193818.csv" +out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE193818.csv" +out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE193818.csv" +json_path = "./output/preprocess/3/Schizophrenia/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# From the Series description, this is RNA data from blood samples +is_gene_available = True + +# 2.1 Data Availability +# All patients have schizophrenia according to Series title/summary +trait_row = None +# Age available in row 1 +age_row = 1 +# Gender available in row 0 +gender_row = 0 + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + # All samples have schizophrenia, but this row is not available + return None + +def convert_age(x): + if pd.isna(x): + return None + try: + # Extract numeric value after colon + age = float(x.split(': ')[1]) + return age + except: + return None + +def convert_gender(x): + if pd.isna(x): + return None + value = x.split(': ')[1].lower() + if value == 'female': + return 0 + elif value == 'male': + return 1 + return None + +# 3. Save Metadata +# is_trait_available is False since all samples have schizophrenia (constant) +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=False) + +# 4. Clinical Feature Extraction skipped since trait_row is None +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The IDs look like Affymetrix probe IDs (e.g., 'AFFX-BkGr-GC03_st') +# These are not standard human gene symbols and will need to be mapped +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Preview column names and values from annotation dataframe +print("Gene annotation DataFrame preview:") +print(preview_df(gene_annotation)) +# The gene expression data uses probe IDs similar to 'AFFX-BkGr-GC03_st' +# The gene annotation contains detailed information about genes in 'SPOT_ID.1' +# We need to extract gene symbols from this field and pair them with IDs + +# Get mapping dataframe containing probe IDs and gene symbols +mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID.1') + +# Apply mapping to convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Save gene expression data +gene_data.to_csv(out_gene_data_file) + +print("Gene data shape:", gene_data.shape) +print("\nFirst few genes and samples:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +print("Gene data shape after normalization:", gene_data.shape) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# Mark dataset as unusable due to constant trait +note = "All samples have schizophrenia (constant trait), making the dataset unsuitable for associational studies." +validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, + is_biased=True, # Since constant trait = 100% bias + df=gene_data, # Gene data serves as the dataset + note=note +) \ No newline at end of file diff --git a/p3/preprocess/Schizophrenia/code/GSE273630.py b/p3/preprocess/Schizophrenia/code/GSE273630.py new file mode 100644 index 0000000000000000000000000000000000000000..afcfda90268ae2b58322e7f9a78f7c89f0afbd6c --- /dev/null +++ b/p3/preprocess/Schizophrenia/code/GSE273630.py @@ -0,0 +1,116 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Schizophrenia" +cohort = "GSE273630" + +# Input paths +in_trait_dir = "../DATA/GEO/Schizophrenia" +in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE273630" + +# Output paths +out_data_file = "./output/preprocess/3/Schizophrenia/GSE273630.csv" +out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE273630.csv" +out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE273630.csv" +json_path = "./output/preprocess/3/Schizophrenia/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Based on the Series summary, this is a custom panel for dopamine-regulated inflammatory genes +# So gene expression data should be available +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# Trait (Schizophrenia) - Not available as participants are explicitly excluded if they have schizophrenia +trait_row = None + +# Age - Not extractable from characteristics dict, but background info states all participants are 35-44 years old +# Since age range is narrow and constant for all subjects, consider it not useful/available +age_row = None + +# Gender - Not in characteristics dict, but background info states all participants are males +# Since gender is constant for all subjects, consider it not useful/available +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + # Not used since trait data not available + return None + +def convert_age(x): + # Not used since age data not available + return None + +def convert_gender(x): + # Not used since gender data not available + return None + +# 3. Save Metadata +# Initial filtering - validate and save cohort info +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Clinical Feature Extraction +# Skip since trait_row is None, indicating clinical data is not available +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# Looking at the gene IDs, they appear to be proper HGNC gene symbols +# e.g. ABAT, ABL1, ACAA1 which are well-known human gene symbols + +requires_gene_mapping = False +# 1. Normalize gene symbols +genetic_data = normalize_gene_symbols_in_index(genetic_data) +print("Gene data shape after normalization:", genetic_data.shape) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +genetic_data.to_csv(out_gene_data_file) + +# 2-4. Skip linking and missing value handling since no clinical data available + +# 5. Final validation - dataset cannot be used for schizophrenia study +# Note that background info explicitly states schizophrenia patients were excluded +note = "This dataset contains gene expression data but lacks information about schizophrenia status. The study explicitly excluded patients with schizophrenia in their exclusion criteria." +validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, + is_biased=True, # Biased since schizophrenia patients were explicitly excluded + df=genetic_data.T, # Pass transposed gene expression data as samples should be in rows + note=note +) + +# 6. Do not save linked data as the dataset is not usable for trait analysis \ No newline at end of file diff --git a/p3/preprocess/Schizophrenia/code/GSE285666.py b/p3/preprocess/Schizophrenia/code/GSE285666.py new file mode 100644 index 0000000000000000000000000000000000000000..8edd489131a442718e58150a1bce84220248fe4d --- /dev/null +++ b/p3/preprocess/Schizophrenia/code/GSE285666.py @@ -0,0 +1,164 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Schizophrenia" +cohort = "GSE285666" + +# Input paths +in_trait_dir = "../DATA/GEO/Schizophrenia" +in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE285666" + +# Output paths +out_data_file = "./output/preprocess/3/Schizophrenia/GSE285666.csv" +out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE285666.csv" +out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE285666.csv" +json_path = "./output/preprocess/3/Schizophrenia/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# Gene Expression Data Availability +# Yes, according to background info "Exon- and gene-Level transcriptional profiling" and "Expression analysis using Affymetrix Human Exon arrays" +is_gene_available = True + +# Clinical Data Availability and Conversion +trait_row = 0 # Found trait info in row 0 "disease state" +age_row = None # Age not available +gender_row = None # Gender not available + +def convert_trait(value: str) -> int: + """Convert disease state to binary - controls:0, patients:1""" + if pd.isna(value) or not isinstance(value, str): + return None + value = value.split(": ")[-1].lower() + if "unaffected" in value or "control" in value: + return 0 + elif "patient" in value or "williams syndrome" in value: + return 1 + return None + +# Save metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# Extract clinical features since trait data is available +clinical_df = geo_select_clinical_features(clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait) + +# Preview the processed clinical data +print("Preview of clinical data:") +print(preview_df(clinical_df)) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_df.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# These appear to be numerical probe IDs, not human gene symbols +# They will need to be mapped to gene symbols before analysis +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Preview column names and values from annotation dataframe +print("Gene annotation DataFrame preview:") +print(preview_df(gene_annotation)) +# From observation: 'ID' in gene annotation matches gene identifiers in expression data +# 'gene_assignment' contains gene symbols in the format "RefSeq // GeneSymbol // Description" + +# Get mapping between probes and genes +mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment') + +# Convert probe-level data to gene-level data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview the mapped gene data +print("Gene expression data preview after mapping:") +print(gene_data.head()) +print("\nShape:", gene_data.shape) + +# Save gene data +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +print("Gene data shape after normalization:", gene_data.shape) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# Load clinical data previously processed +selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) +print("\nClinical data shape:", selected_clinical_df.shape) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) +print("\nLinked data shape:", linked_data.shape) + +# 3. Handle missing values systematically +if trait in linked_data.columns: + linked_data = handle_missing_values(linked_data, trait) + + # 4. Check for bias in trait and demographic features + trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + + # 5. Final validation and information saving + note = "This dataset contains gene expression data from myxoid liposarcoma samples, with metastasis status as the trait." + is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note + ) + + # 6. Save linked data only if usable and not biased + if is_usable and not trait_biased: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) +else: + # Handle case where clinical features were not properly extracted + note = "Failed to extract clinical trait information from sample characteristics." + validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, + is_biased=None, + df=None, + note=note + ) \ No newline at end of file diff --git a/p3/preprocess/Schizophrenia/code/TCGA.py b/p3/preprocess/Schizophrenia/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..542184b3c306a883cef72ed29ab5a5ea0f0b9d34 --- /dev/null +++ b/p3/preprocess/Schizophrenia/code/TCGA.py @@ -0,0 +1,28 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Schizophrenia" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Schizophrenia/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Schizophrenia/cohort_info.json" + +# Since TCGA only contains cancer datasets, and schizophrenia is not a cancer, +# we know there won't be suitable data - record unavailability and exit +validate_and_save_cohort_info( + is_final=False, + cohort="TCGA", + info_path=json_path, + is_gene_available=False, + is_trait_available=False +) + +# Return empty dataframes to avoid errors in subsequent code +clinical_df = pd.DataFrame() +genetic_df = pd.DataFrame() \ No newline at end of file diff --git a/p3/preprocess/Schizophrenia/gene_data/GSE119289.csv b/p3/preprocess/Schizophrenia/gene_data/GSE119289.csv new file mode 100644 index 0000000000000000000000000000000000000000..60e38d5389b6ca1d80f9e7c20dbd91bde36962f4 --- /dev/null +++ b/p3/preprocess/Schizophrenia/gene_data/GSE119289.csv @@ -0,0 +1,6 @@ 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diff --git a/p3/preprocess/Schizophrenia/gene_data/GSE120340.csv b/p3/preprocess/Schizophrenia/gene_data/GSE120340.csv new file mode 100644 index 0000000000000000000000000000000000000000..2317ef5f58ed66bce71f61980c0ec0b5123d554b --- /dev/null +++ b/p3/preprocess/Schizophrenia/gene_data/GSE120340.csv @@ -0,0 +1,1172 @@ +Gene,GSM3398477,GSM3398478,GSM3398479,GSM3398480,GSM3398481,GSM3398482,GSM3398483,GSM3398484,GSM3398485,GSM3398486,GSM3398487,GSM3398488,GSM3398489,GSM3398490,GSM3398491,GSM3398492,GSM3398493,GSM3398494,GSM3398495,GSM3398496,GSM3398497,GSM3398498,GSM3398499,GSM3398500,GSM3398501,GSM3398502,GSM3398503,GSM3398504,GSM3398505,GSM3398506 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+CLEC2B,5.83065,5.94215,5.44462,5.74802,4.77258,5.47931,5.03348,7.2084,6.66281,5.51685,5.90923,5.8149,6.89332,6.83887,6.76986,9.03831,5.6263,4.89201,6.68759,6.13618,5.74206,6.97218,6.19102,5.73789,5.36662,6.18699,5.72196,5.92092,5.05378,5.34602,5.9229,7.32056,5.87509,5.84702,5.07335 +CLEC2D,4.3695,4.58821,4.28428,4.44729,4.36865,4.3586,4.47436,4.66284,4.74042,4.6448,4.36081,4.42771,4.34856,4.35853,4.62272,5.0486,4.48726,4.27987,4.61121,4.45559,4.43785,4.59589,4.47651,4.39685,4.41674,4.6355,4.31918,4.43785,4.57563,4.38404,4.42098,4.32088,4.78747,4.79652,4.35376 +CLEC3B,4.01884,4.17648,3.50972,3.929595,4.121685,3.42447,4.23545,4.251485,4.153485,4.159895,3.76455,3.99678,3.27242,4.49725,4.684245,3.712115,4.49625,3.92744,4.11539,4.32685,4.715245,2.93806,4.09837,4.34762,4.225465,4.25446,4.174595,4.45108,3.94733,3.79932,4.47885,3.94071,4.068495,3.81431,4.13727 +CLEC4A,8.793520000000001,8.3983,8.65968,8.577110000000001,8.58747,8.372150000000001,8.73742,8.662849999999999,9.14227,8.97875,8.46291,8.48827,8.79106,8.466899999999999,8.98719,8.49041,8.41002,8.427900000000001,8.53659,8.37729,8.51603,8.61017,8.56214,8.34087,8.57179,9.1844,8.686330000000002,8.503350000000001,8.33933,8.49799,8.735209999999999,8.440100000000001,8.48313,8.4153,8.66196 +CLEC4E,4.76166,4.80963,4.39497,4.4233,4.20351,4.38075,4.42635,4.54132,5.76495,4.71664,4.50882,4.90519,4.26258,4.63963,4.76036,4.82881,4.32531,4.4536,4.90078,4.57882,4.37307,4.85473,4.38128,4.38075,4.24129,5.50046,4.3214,4.4221,4.46283,4.74765,4.29036,4.31886,4.76479,4.43717,4.11366 +CLEC4M,8.28951,8.46442,8.341940000000001,8.30678,8.307120000000001,8.23958,8.39072,8.587,8.54829,8.328240000000001,8.45507,8.483979999999999,8.44829,8.28233,8.27337,8.804490000000001,8.16658,8.27928,8.500150000000001,8.30776,8.3743,8.57596,8.26642,8.193460000000002,8.32688,8.67126,8.381509999999999,8.38179,8.32667,8.661660000000001,8.161950000000001,8.23784,8.27701,8.431719999999999,8.43684 +CLEC5A,4.23919,4.52393,4.36195,4.39043,4.21109,4.31144,4.36896,4.38703,4.39517,4.51882,4.35336,4.34058,4.78987,4.36036,4.51123,4.38206,4.29083,4.29463,4.60616,4.34659,4.35336,4.27575,4.39333,4.32473,4.25166,4.67327,4.3533,4.30268,4.32376,4.39087,4.28114,6.15376,4.40725,4.35115,4.15401 +CLEC7A,4.79443,4.78112,4.78345,4.83639,4.53083,4.36428,5.06081,4.92226,5.80005,4.85639,4.33253,4.56277,4.97147,4.46494,5.16272,6.42978,4.43106,4.28881,4.60712,4.85594,4.39643,4.93422,5.08751,4.79694,4.47844,5.26842,4.36423,4.702,4.56511,4.51558,4.87114,4.87086,4.64546,4.60467,4.68468 +CLGN,5.3262,6.05249,5.78868,6.04795,5.85589,6.61087,6.06856,6.38375,5.11112,7.22013,6.88637,6.0917,5.77203,6.77346,5.84633,6.88728,6.53536,6.1921,4.87575,6.15824,5.46151,5.02102,6.48774,6.06104,6.15773,7.74788,6.85724,5.40221,6.24749,6.01886,6.76017,6.06202,6.64618,6.23475,6.06769 +CLIC1,7.88442,7.52339,7.28809,8.00403,6.6051,7.23741,6.60947,8.0457,8.67407,7.66306,7.17957,7.31755,8.33621,7.61197,8.09084,8.84438,7.65497,6.43473,7.60286,8.3161,7.52015,8.04771,7.75419,7.53708,6.88615,7.46166,7.48388,7.42807,6.94705,6.91763,7.86747,9.68492,6.54109,7.41497,7.10224 +CLIC1P1,4.88009,4.94229,4.95131,4.9549,4.85956,4.83639,5.09658,5.23108,4.8308,5.25372,5.14927,5.28248,4.79865,5.23228,4.87055,5.35299,4.78848,4.6397,5.28063,5.14314,4.94229,5.27778,4.90475,4.92769,4.60157,5.45324,4.90366,4.76427,4.91674,4.94229,4.93953,4.6887,5.46411,5.00638,4.87352 +CLIC2,5.08592,5.06776,5.33353,5.27881,5.23346,5.36531,5.17972,5.52067,5.42683,5.35048,5.76131,5.25795,5.72493,5.52123,5.7658,5.47321,5.34111,5.304,5.34128,5.28848,5.23568,5.09711,5.27262,5.4378,5.38454,5.62745,5.97171,5.64401,5.38438,5.28933,5.66886,6.16408,5.48711,5.02766,5.48441 +CLIC3,4.16184,4.55446,4.37348,4.6495,4.41051,4.50257,4.63327,4.66118,4.60259,4.56243,4.57084,4.81761,4.26806,4.57838,4.56243,4.90092,4.65227,4.67508,4.54037,4.42785,4.6094,4.7458,4.54339,4.62036,4.36814,5.09418,4.55436,4.22155,4.6684,4.66108,4.67207,4.57293,4.55766,4.36692,4.54216 +CLIC4,22.915869999999998,22.75174,19.6939,19.94236,19.868570000000002,18.867539999999998,18.14546,20.51536,24.93419,20.15065,19.14629,20.8398,21.94042,20.624579999999998,19.86331,21.16869,22.187549999999998,20.51566,21.07499,22.304699999999997,21.31468,21.99955,21.94121,21.2809,21.052059999999997,21.81842,19.87007,20.57645,20.72685,19.3993,20.912480000000002,24.86334,19.29421,21.09585,21.58338 +CLIC5,14.70503,14.52418,14.76427,15.3278,15.70569,14.74702,16.19451,15.68535,15.67849,15.87313,15.49659,15.4521,14.87689,15.50249,14.98003,15.536,15.056840000000001,15.26186,15.966650000000001,15.55648,15.78181,15.49806,15.062159999999999,15.429310000000001,15.12577,16.25518,15.07167,15.419830000000001,14.69322,15.574839999999998,15.45504,14.304359999999999,15.81781,15.4363,15.14526 +CLINT1,16.158360000000002,15.79402,16.32667,15.76254,16.22813,16.7045,15.1501,16.44582,14.702639999999999,16.29469,16.56803,15.98998,16.427950000000003,16.38016,16.33694,16.36713,16.30019,16.59314,15.161290000000001,16.44809,15.69897,15.448879999999999,16.29914,16.11724,16.52236,16.11634,16.9477,16.25073,16.54013,15.7821,16.41265,16.36119,15.81963,16.41476,16.53219 +CLIP1,15.99532,14.71014,15.949560000000002,16.35013,16.72147,17.48696,16.023139999999998,15.32216,14.50627,16.45742,16.84007,14.67446,17.6023,16.37367,15.26334,15.85267,16.601419999999997,16.90079,16.201639999999998,16.414279999999998,16.13802,15.54496,15.55002,17.229680000000002,17.25089,15.71526,17.128790000000002,15.91591,17.21819,15.13746,16.66628,17.61466,15.223849999999999,15.93181,17.45413 +CLIP2,8.98666,8.9586,8.92977,9.37305,8.83899,8.58858,9.82119,7.87139,9.4778,7.31221,8.33931,8.86388,8.5709,8.48921,8.19892,7.04827,8.79247,8.51996,8.62844,8.25281,9.64681,9.57946,8.665,8.84329,8.75663,7.1133,7.88479,8.99603,8.90287,9.11196,8.58171,8.73163,8.76809,8.63931,8.68844 +CLIP3,9.86076,9.62895,10.1981,10.6989,10.7658,10.1094,10.8505,9.28923,9.36852,8.19556,9.61422,9.7591,10.1799,9.71908,9.56332,8.75084,10.5185,10.3412,10.0026,9.71454,10.82,10.1914,9.82119,10.3214,10.3748,8.61308,9.8026,10.5621,10.3175,9.81466,10.353,10.3374,9.96454,9.44306,10.3135 +CLIP4,5.62351,5.73946,5.88023,5.53368,5.99281,5.72991,5.63702,5.69289,5.8847,6.12398,5.69346,5.71798,5.99976,5.79056,5.68708,6.01538,5.88501,6.07364,5.97116,5.85469,5.50262,5.78346,5.78346,5.89125,5.96462,5.68947,5.83187,5.72171,5.81916,5.72702,5.88393,6.05798,5.74289,5.52452,5.74018 +CLK1,9.45971,9.94368,9.16133,8.29246,9.43123,8.8561,8.98878,8.70204,10.2346,8.50103,9.3353,8.99289,9.88296,8.66313,9.34777,8.61568,9.07933,9.30859,8.51035,8.46869,9.48163,8.3305,8.58344,9.39818,9.28254,8.07732,8.97272,8.82471,9.0911,9.26705,8.88844,9.68257,8.74768,9.86655,9.15907 +CLK2,7.14523,7.44408,7.19858,7.42791,7.83667,7.00915,7.78354,7.12532,7.55702,7.10538,7.09267,7.29101,7.57856,6.93139,6.94948,6.99118,7.42865,7.47721,7.13784,7.1059,7.68241,7.27473,7.19987,7.75801,7.83559,7.43784,6.90212,7.30227,6.97591,7.27473,7.17752,7.61514,7.20368,7.15734,7.57553 +CLK3,6.57149,6.75681,6.80729,6.89705,6.59556,6.47966,6.46986,6.61838,7.14947,6.40364,6.63854,6.4924,6.98501,6.52103,6.39768,6.09518,6.6943,6.66008,6.51224,6.61158,6.67376,6.43755,6.51098,6.65676,6.79033,6.27768,6.12781,6.78358,6.76759,6.30267,6.49087,6.81297,6.3684,6.45358,6.61838 +CLK4,8.02581,8.65826,8.12806,8.11019,8.73036,8.14635,8.66939,8.1128,8.43669,8.39226,8.53546,7.8841,8.89807,7.99828,8.29463,7.98542,8.1635,8.47083,8.05524,7.59403,8.66937,8.20039,7.67188,8.398,8.48274,8.18782,8.32878,7.95414,8.29463,8.94413,7.95051,8.44009,8.16252,8.49582,8.31238 +CLMN,13.34683,14.98114,12.14273,12.2534,12.82735,11.4829,11.84877,11.800419999999999,15.40348,11.995840000000001,11.63509,12.42263,12.92041,11.42485,12.266829999999999,12.252839999999999,13.3374,11.797329999999999,10.889389999999999,12.26976,13.723669999999998,11.85984,14.46735,11.45666,12.24141,11.95024,11.39939,12.362639999999999,13.6247,12.11623,12.2371,12.947330000000001,11.764800000000001,13.229330000000001,13.164719999999999 +CLN3,11.86652,11.66375,11.61116,11.373660000000001,11.83866,11.64423,11.21789,10.93413,12.206620000000001,10.699580000000001,11.46837,11.301079999999999,11.9991,11.40815,11.2025,11.285,12.10397,11.75441,11.06295,11.27496,12.30751,11.76624,11.757670000000001,11.83848,12.072189999999999,11.02041,11.29748,11.65592,11.77575,11.242840000000001,11.794820000000001,11.810210000000001,11.36815,11.21909,11.906870000000001 +CLN5,20.69427,22.91207,20.47891,19.10965,19.139760000000003,20.4831,18.61663,22.23543,19.75687,20.62639,20.98604,20.50513,19.97922,21.91669,18.74712,23.02229,20.89511,21.05455,20.61276,23.73809,20.56513,20.304859999999998,22.4081,19.657049999999998,20.53921,21.58805,21.79953,20.91417,19.94627,20.457720000000002,22.07236,21.7148,20.55067,22.16958,20.04162 +CLN6,6.20204,6.47948,6.47155,6.50885,6.27269,6.3797,6.82043,6.26273,6.16794,6.40526,6.49476,6.20568,6.30515,6.22527,6.27269,5.99108,6.39685,6.16437,6.63504,6.27407,6.39086,6.35485,6.17508,6.16961,6.13897,6.05848,6.23989,6.32545,6.20588,6.30637,6.14114,6.20423,6.44096,6.22279,6.26483 +CLN8,13.64688,14.5106,12.96961,12.57374,13.01576,12.22133,12.43314,11.99091,14.77461,11.56513,12.000060000000001,13.23823,13.115120000000001,12.74216,12.77918,11.503820000000001,13.711310000000001,12.93946,12.937380000000001,12.876650000000001,13.739650000000001,12.51009,13.26469,12.26278,12.82621,12.338429999999999,11.94078,13.05184,13.49406,12.782589999999999,12.61036,13.60295,12.149709999999999,12.969100000000001,13.52517 +CLNS1A,9.924,10.0143,10.0832,9.29991,9.20228,9.65979,9.08691,9.99442,10.0339,10.3146,10.1426,10.1587,9.44914,10.0549,9.84094,9.97659,9.75627,9.55068,9.96364,9.98545,9.53753,9.82641,9.90216,9.4418,9.37647,9.64204,9.74564,9.67549,9.75339,10.0648,9.79681,9.38755,10.0026,10.1931,9.47776 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+NBPF14,6.779298392857143,6.5395125,6.332542500000001,6.185377535714286,6.461194214285714,6.323003464285714,6.12277675,6.2938553214285715,6.950650428571429,6.259109107142857,6.2465356071428575,6.370091107142857,6.6545198928571425,6.531434071428571,6.47326925,6.2859900714285715,6.403711107142858,6.555602071428572,7.073505285714285,6.624062107142858,6.619775571428572,6.526910428571428,6.325915571428571,6.644090714285714,6.887326535714285,6.4849130357142855,6.182114821428572,6.306207678571429,6.403422964285714,6.397517928571428,6.226025285714286,6.411068571428571,6.379583928571429,6.327733142857142,6.672913357142857 +NBPF15,4.634813392857143,4.4839875,4.347017500000001,4.166895035714285,4.414919214285715,4.362920964285714,4.17576925,4.432637821428571,4.8009704285714285,4.3857116071428575,4.439155607142857,4.467708607142857,4.517449892857143,4.484334071428571,4.45627675,4.461765071428571,4.389176107142857,4.441672071428572,4.821275285714286,4.539877107142857,4.5743605714285716,4.526030428571429,4.381143071428571,4.614605714285714,4.599974035714286,4.436678035714285,4.286019821428571,4.3074401785714285,4.405367964285714,4.508220428571429,4.366462785714286,4.504801071428571,4.423691428571429,4.4390756428571425,4.529330857142857 +NBPF19,1.92271,1.8479228571428572,1.781592142857143,1.7064741428571428,1.8145752857142856,1.791404,1.6198730000000001,1.795128,1.9711811428571429,1.8331428571428572,1.7412682857142856,1.7959741428571427,1.8838425714285716,1.8366015714285715,1.8402144285714286,1.8230954285714285,1.800742,1.8865245714285717,1.992786,1.8790330000000002,1.8239177142857144,1.7536768571428571,1.7571555714285714,1.859387857142857,1.934966,1.8656742857142856,1.7551092857142856,1.7923307142857143,1.811471,1.730656142857143,1.7742645714285712,1.8013385714285715,1.7963264285714287,1.7915081428571429,1.8977394285714286 +NBPF20,3.5271333928571424,3.398144642857143,3.325810357142857,3.1323578928571427,3.374334928571429,3.3512009642857143,3.2368492499999997,3.421827821428572,3.6553932857142857,3.32717875,3.4552113214285716,3.429951464285714,3.4083713214285716,3.4214054999999997,3.3918053214285715,3.3901736428571425,3.356196107142857,3.3503935,3.6375152857142856,3.465527107142857,3.523584857142857,3.5123275714285715,3.3767345,3.5680428571428573,3.481904035714286,3.36858375,3.2752055357142855,3.2723744642857144,3.3696579642857145,3.5281032857142858,3.391314214285714,3.5292424999999996,3.37346,3.4342284999999997,3.4337894285714285 +NBPF25P,0.8150299999999999,0.76208,0.7603850000000001,0.671937,0.773991,0.779684,0.6809529999999999,0.7843180000000001,0.825604,0.77461,0.757324,0.7582169999999999,0.774764,0.7736729999999999,0.7757430000000001,0.751504,0.7677620000000001,0.795246,0.809026,0.804683,0.773142,0.739974,0.752747,0.8128249999999999,0.8168960000000001,0.79758,0.7442949999999999,0.7572650000000001,0.7757609999999999,0.7505390000000001,0.7991159999999999,0.82578,0.7460950000000001,0.7866609999999999,0.802198 +NBPF26,4.067195,3.903447857142857,3.767117142857143,3.724956642857143,3.860850285714286,3.7514865,3.5668805,3.6563455,4.120861142857143,3.706540357142857,3.5486482857142856,3.698356642857143,4.020912571428572,3.8837015714285714,3.857206928571429,3.6473204285714287,3.815277,4.0004545714285715,4.245016,3.9632180000000004,3.8693327142857146,3.754556857142857,3.701928071428571,3.8888728571428572,4.2223185,3.9139092857142854,3.651204285714286,3.7910982142857144,3.809526,3.6199536428571433,3.633827071428571,3.7076060714285712,3.7522189285714287,3.680165642857143,4.041321928571429 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+NBPF9,5.671618392857143,5.453669642857143,5.311335357142857,5.150840392857143,5.420609928571428,5.311283464285714,5.18385675,5.283045321428571,5.805073285714286,5.20057625,5.262591321428571,5.332333964285715,5.545441321428571,5.468505499999999,5.408797821428571,5.214398642857143,5.370731107142857,5.4643235,5.889745285714286,5.549712107142858,5.568999857142858,5.5132075714285715,5.321507,5.5975278571428575,5.769256535714286,5.41681875,5.171300535714286,5.2711419642857145,5.367712964285714,5.417400785714285,5.250876714285714,5.43551,5.329352500000001,5.322886,5.577371928571429 +NBR1,16.56127,15.654530000000001,16.10344,14.87631,15.73689,16.34027,14.23365,14.64817,16.77822,13.87167,15.72885,14.79431,16.71519,15.61926,14.90595,13.65221,16.8242,16.64425,16.04293,16.57171,16.2645,15.11001,15.876460000000002,16.49019,17.12051,13.37676,15.97818,15.74363,16.6914,14.89174,16.97741,16.735300000000002,14.86385,15.435490000000001,17.31361 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+NCF1C,1.7301233333333332,1.6936966666666666,1.76319,1.7132333333333334,1.7405099999999998,1.7101233333333334,1.8310833333333332,1.7014233333333333,1.7279,1.9067166666666668,1.7611933333333332,1.6867566666666667,1.8894866666666665,1.6804,1.6892833333333332,1.7340666666666669,1.7004133333333333,1.7098066666666665,1.7473766666666668,1.74597,1.68734,1.6712866666666668,1.7742766666666665,1.7472266666666665,1.7356066666666665,1.7405099999999998,1.7957533333333335,1.7253299999999998,1.81655,1.7043333333333335,1.7529066666666668,1.72075,1.7405099999999998,1.7374933333333333,1.8013733333333333 +NCF2,4.70133,4.32315,4.42342,4.5689,4.35393,4.42665,4.75155,4.65231,5.08925,4.65401,4.5146,4.63935,5.25881,4.75324,4.89216,5.38016,4.58944,4.44056,5.17968,4.79478,4.39684,4.75778,4.83022,4.65401,4.46333,4.97948,4.54986,4.58602,4.51622,4.74018,4.58932,5.13905,5.23578,4.65401,4.60798 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diff --git a/p3/preprocess/Sickle_Cell_Anemia/GSE53441.csv b/p3/preprocess/Sickle_Cell_Anemia/GSE53441.csv new file mode 100644 index 0000000000000000000000000000000000000000..11aefb5050cf608145d1201883e59600b850b858 --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/GSE53441.csv @@ -0,0 +1,35 @@ 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CAR4,BCAS1,BCAS2,BCAS3,BCAS4,BCAT1,BCAT2,BCCIP,BCDIN3D,BCHE,BCKDHA,BCKDHB,BCKDK,BCL10,BCL11A,BCL11B,BCL2,BCL2A1,BCL2L1,BCL2L10,BCL2L11,BCL2L12,BCL2L13,BCL2L14,BCL2L15,BCL2L2,BCL3,BCL6,BCL6B,BCL7A,BCL7B,BCL7C,BCL9,BCL9L,BCLAF1,BCO1,BCO2,BCOR,BCORL1,BCORP1,BCR,BCRP3,BCS1L,BDH1,BDH2,BDKRB1,BDKRB2,BDNF,BDNF-AS,BDP1,BEAN1,BECN1,BEGAIN,BEND2,BEND3,BEND4,BEND5,BEND6,BEND7,BEST1,BEST2,BEST3,BEST4,BET1,BET1L,BEX1,BEX2,BEX3,BEX4,BEX5,BFAR,BFSP1,BFSP2,BFSP2-AS1,BGLAP,BGLT3,BGN,BHLHE22,BHLHE23,BHLHE40,BHLHE41,BHMT,BHMT2,BICC1,BICD1,BICD2,BICDL1,BICDL2,BICRA,BICRAL,BID,BIK,BIN1,BIN2,BIN3,BIN3-IT1,BIRC2,BIRC3,BIRC5,BIRC6,BIRC7,BIVM,BLACAT1,BLCAP,BLID,BLK,BLM,BLMH,BLNK,BLOC1S1,BLOC1S2,BLOC1S3,BLOC1S4,BLOC1S5,BLOC1S6,BLTP1,BLTP2,BLTP3A,BLTP3B,BLVRA,BLVRB,BLZF1,BMAL1,BMAL2,BMAL2-AS1,BMERB1,BMF,BMI1,BMP1,BMP10,BMP15,BMP2,BMP2K,BMP3,BMP4,BMP5,BMP6,BMP7,BMP8A,BMP8B,BMPER,BMPR1A,BMPR1B,BMPR2,BMS1,BMS1P1,BMS1P17,BMS1P18,BMS1P2,BMS1P20,BMX,BNC1,BNC2,BNIP1,BNIP2,BNIP3,BNIP3L,BNIPL,BOC,BOD1,BOD1L1,BOD1L2,BOK,BOLA1,BOLA2,BOLA2B,BOLA3,BOLA3-DT,BOLL,BOP1,BORA,BORCS5,BORCS6,BORCS7,BORCS8,BPESC1,BPGM,BPHL,BPI,BPIFA1,BPIFA2,BPIFA3,BPIFA4P,BPIFB1,BPIFB2,BPIFB4,BPIFB6,BPIFB9P,BPIFC,BPNT1,BPNT2,BPTF,BPY2,BRAF,BRAP,BRAT1,BRCA1,BRCA2,BRCC3,BRD1,BRD10,BRD2,BRD3,BRD4,BRD7,BRD7P3,BRD8,BRD9,BRDT,BRF1,BRF2,BRI3,BRI3BP,BRICD5,BRINP1,BRINP2,BRINP3,BRIP1,BRIX1,BRK1,BRME1,BRMS1,BRMS1L,BROX,BRPF1,BRPF3,BRS3,BRSK1,BRSK2,BRWD1,BRWD1-AS2,BRWD3,BSCL2,BSDC1,BSG,BSN,BSN-DT,BSND,BSPRY,BST1,BST2,BTAF1,BTBD1,BTBD10,BTBD16,BTBD17,BTBD18,BTBD19,BTBD2,BTBD3,BTBD6,BTBD7,BTBD8,BTBD9,BTC,BTD,BTF3,BTF3L4,BTF3P11,BTF3P12,BTF3P13,BTG1,BTG2,BTG3,BTG4,BTK,BTLA,BTN1A1,BTN2A1,BTN2A2,BTN2A3P,BTN3A1,BTN3A2,BTN3A3,BTNL2,BTNL3,BTNL8,BTNL9,BTRC,BUB1,BUB1B,BUB3,BUD13,BUD23,BUD31,BYSL,BZW1,BZW2,C10orf126,C10orf53,C10orf55,C10orf62,C10orf67,C10orf71,C10orf88,C10orf90,C10orf95,C10orf95-AS1,C11orf16,C11orf21,C11orf24,C11orf40,C11orf42,C11orf54,C11orf58,C11orf65,C11orf68,C11orf71,C11orf86,C11orf87,C11orf96,C11orf97,C11orf98,C12orf42,C12orf43,C12orf50,C12orf54,C12orf56,C12orf57,C12orf60,C12orf75,C12orf76,C14orf119,C14orf132,C14orf180,C14orf39,C14orf93,C15orf32,C15orf39,C15orf40,C15orf48,C15orf61,C15orf62,C16orf46,C16orf54,C16orf74,C16orf78,C16orf82,C16orf86,C16orf87,C16orf89,C16orf92,C16orf95,C17orf100,C17orf50,C17orf58,C17orf67,C17orf75,C17orf78,C17orf99,C18orf15,C18orf21,C18orf32,C18orf54,C18orf61,C19orf12,C19orf18,C19orf25,C19orf33,C19orf44,C19orf47,C19orf48P,C19orf53,C19orf73,C19orf81,C19orf84,C1D,C1GALT1,C1GALT1C1,C1QA,C1QB,C1QBP,C1QC,C1QL1,C1QTNF1,C1QTNF12,C1QTNF2,C1QTNF3,C1QTNF4,C1QTNF5,C1QTNF6,C1QTNF7,C1QTNF9,C1QTNF9B,C1R,C1RL,C1RL-AS1,C1S,C1orf105,C1orf115,C1orf116,C1orf122,C1orf159,C1orf162,C1orf167,C1orf174,C1orf198,C1orf21,C1orf210,C1orf216,C1orf220,C1orf226,C1orf35,C1orf43,C1orf50,C1orf52,C1orf53,C1orf54,C1orf56,C1orf74,C1orf87,C1orf94,C2,C20orf141,C20orf144,C20orf173,C20orf181,C20orf202,C20orf203,C20orf96,C21orf58,C21orf91,C22orf15,C22orf23,C22orf31,C22orf39,C22orf42,C22orf46P,C2CD2,C2CD2L,C2CD3,C2CD4A,C2CD4B,C2CD4C,C2CD5,C2CD6,C2orf15,C2orf42,C2orf49,C2orf68,C2orf69,C2orf72,C2orf74,C2orf76,C2orf80,C2orf81,C2orf88,C3,C3AR1,C3P1,C3orf18,C3orf20,C3orf22,C3orf33,C3orf36,C3orf38,C3orf49,C3orf52,C3orf62,C3orf70,C3orf80,C4A,C4B,C4BPA,C4BPB,C4orf17,C4orf33,C4orf36,C4orf46,C5,C5AR1,C5AR2,C5orf15,C5orf22,C5orf24,C5orf34,C5orf46,C5orf47,C5orf58,C5orf63,C6,C6orf118,C6orf120,C6orf132,C6orf136,C6orf141,C6orf147,C6orf15,C6orf163,C6orf47,C6orf52,C6orf58,C6orf62,C6orf89,C7,C7orf25,C7orf33,C7orf57,C8A,C8B,C8G,C8orf17,C8orf33,C8orf34,C8orf44,C8orf48,C8orf58,C8orf74,C8orf76,C8orf82,C8orf88,C9,C9orf152,C9orf163,C9orf40,C9orf43,C9orf50,C9orf57,C9orf72,C9orf78,C9orf85,CA1,CA10,CA11,CA12,CA13,CA14,CA2,CA3,CA4,CA5A,CA5B,CA5BP1,CA6,CA7,CA8,CA9,CAAP1,CAB39,CAB39L,CABCOCO1,CABIN1,CABLES1,CABLES2,CABP1,CABP2,CABP4,CABP5,CABP7,CABS1,CABYR,CACFD1,CACHD1,CACNA1A,CACNA1B,CACNA1C,CACNA1D,CACNA1E,CACNA1F,CACNA1G,CACNA1H,CACNA1I,CACNA1S,CACNA2D1,CACNA2D2,CACNA2D3,CACNA2D4,CACNB1,CACNB2,CACNB3,CACNB4,CACNG1,CACNG2,CACNG3,CACNG4,CACNG5,CACNG6,CACNG7,CACNG8,CACTIN,CACUL1,CACYBP,CAD,CADM1,CADM2,CADM2-AS1,CADM3,CADM3-AS1,CADM4,CADPS,CADPS2,CAGE1,CAHM,CALB1,CALB2,CALCA,CALCB,CALCOCO1,CALCOCO2,CALCR,CALCRL,CALD1,CALHM1,CALHM2,CALHM3,CALHM4,CALHM5,CALHM6,CALM1,CALM2,CALM3,CALML3,CALML4,CALML5,CALML6,CALN1,CALR,CALR3,CALU,CALY,CAMK1,CAMK1D,CAMK1G,CAMK2A,CAMK2B,CAMK2D,CAMK2G,CAMK2N1,CAMK2N2,CAMK4,CAMKK1,CAMKK2,CAMKMT,CAMKV,CAMLG,CAMP,CAMSAP1,CAMSAP2,CAMSAP3,CAMTA1,CAMTA2,CAND1,CAND2,CANT1,CANX,CAP1,CAP2,CAPG,CAPN1,CAPN10,CAPN10-DT,CAPN11,CAPN12,CAPN13,CAPN14,CAPN15,CAPN2,CAPN3,CAPN5,CAPN6,CAPN7,CAPN8,CAPN9,CAPNS1,CAPNS2,CAPRIN1,CAPRIN2,CAPS,CAPS2,CAPSL,CAPZA1,CAPZA2,CAPZA3,CAPZB,CARD10,CARD11,CARD14,CARD16,CARD18,CARD19,CARD6,CARD8,CARD9,CARF,CARHSP1,CARINH,CARM1,CARMIL1,CARMIL2,CARMIL3,CARMN,CARNMT1,CARNS1,CARS1,CARS1-AS1,CARS2,CARTPT,CASC11,CASC15,CASC16,CASC17,CASC18,CASC2,CASC22,CASC3,CASC6,CASD1,CASK,CASKIN1,CASKIN2,CASP1,CASP10,CASP12,CASP14,CASP16P,CASP2,CASP3,CASP4,CASP5,CASP6,CASP7,CASP8,CASP8AP2,CASP9,CASQ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A1,FUCA2,FUNDC1,FUNDC2,FUOM,FURIN,FUS,FUT1,FUT10,FUT11,FUT2,FUT3,FUT4,FUT5,FUT6,FUT7,FUT8,FUT8-AS1,FUT9,FUZ,FXN,FXR1,FXR2,FXYD1,FXYD2,FXYD3,FXYD4,FXYD5,FXYD6,FXYD7,FYB1,FYB2,FYCO1,FYN,FYTTD1,FZD1,FZD10,FZD10-AS1,FZD2,FZD3,FZD4,FZD5,FZD6,FZD7,FZD8,FZD9,FZR1,G0S2,G2E3,G3BP1,G3BP2,G6PC1,G6PC2,G6PC3,G6PD,GAA,GAB1,GAB2,GAB3,GAB4,GABARAP,GABARAPL1,GABARAPL2,GABARAPL3,GABBR1,GABBR2,GABPA,GABPB1,GABPB1-AS1,GABPB2,GABRA1,GABRA2,GABRA3,GABRA4,GABRA5,GABRA6,GABRB1,GABRB2,GABRB3,GABRD,GABRE,GABRG1,GABRG2,GABRG3,GABRP,GABRQ,GABRR1,GABRR2,GABRR3,GAD1,GAD2,GADD45A,GADD45B,GADD45G,GADD45GIP1,GADL1,GAGE1,GAGE12B,GAGE12C,GAGE12D,GAGE12E,GAGE12F,GAGE12G,GAGE12H,GAGE12I,GAGE12J,GAGE13,GAGE2A,GAGE2B,GAGE2C,GAGE2D,GAGE2E,GAGE4,GAGE5,GAGE6,GAGE7,GAGE8,GAK,GAL,GAL3ST1,GAL3ST2,GAL3ST3,GAL3ST4,GALC,GALE,GALK1,GALK2,GALM,GALNS,GALNT1,GALNT10,GALNT11,GALNT12,GALNT13,GALNT14,GALNT15,GALNT16,GALNT17,GALNT18,GALNT2,GALNT3,GALNT4,GALNT5,GALNT6,GALNT7,GALNT8,GALNT9,GALNTL5,GALNTL6,GALP,GALR1,GALR2,GALR3,GALT,GAMT,GAN,GANAB,GANC,GAP43,GAPDH,GAPDHP62,GAPDHP73,GAPDHS,GAPT,GAPVD1,GAR1,GAREM1,GAREM2,GARIN1A,GARIN1B,GARIN2,GARIN3,GARIN4,GARIN5A,GARIN5B,GARIN6,GARNL3,GARRE1,GART,GAS1,GAS2,GAS2L1,GAS2L2,GAS2L3,GAS5,GAS5-AS1,GAS6,GAS6-AS1,GAS7,GAS8,GAS8-AS1,GASK1A,GASK1B,GAST,GATA1,GATA2,GATA3,GATA3-AS1,GATA4,GATA5,GATA6,GATA6-AS1,GATAD1,GATAD2A,GATAD2B,GATB,GATC,GATD1,GATD1-DT,GATD3,GATM,GBA1LP,GBA2,GBA3,GBE1,GBF1,GBGT1,GBP1,GBP1P1,GBP2,GBP3,GBP4,GBP5,GBP6,GBX1,GBX2,GC,GCA,GCAT,GCC1,GCC2,GCDH,GCFC2,GCG,GCGR,GCH1,GCHFR,GCK,GCKR,GCLC,GCLM,GCM1,GCM2,GCN1,GCNA,GCNT1,GCNT1P1,GCNT2,GCNT3,GCNT4,GCNT7,GCOM1,GCSAM,GCSAML,GCSAML-AS1,GCSH,GDA,GDAP1,GDAP1L1,GDAP2,GDE1,GDF1,GDF10,GDF11,GDF15,GDF2,GDF3,GDF5,GDF9,GDI1,GDI2,GDNF,GDPD1,GDPD2,GDPD3,GDPD5,GDPGP1,GEM,GEMIN2,GEMIN4,GEMIN5,GEMIN6,GEMIN7,GEMIN8,GEN1,GET1,GET3,GET4,GFAP,GFER,GFI1,GFI1B,GFM1,GFM2,GFOD1,GFOD2,GFOD3P,GFPT1,GFPT2,GFRA1,GFRA2,GFRA3,GFRA4,GFUS,GGA1,GGA2,GGA3,GGACT,GGCT,GGCX,GGH,GGN,GGNBP2,GGPS1,GGT1,GGT3P,GGT5,GGT6,GGT7,GGT8P,GGTA2P,GGTLC1,GGTLC2,GH1,GH2,GHDC,GHET1,GHITM,GHR,GHRH,GHRHR,GHRL,GHRLOS,GHSR,GID4,GID8,GIGYF1,GIGYF2,GIMAP1,GIMAP2,GIMAP4,GIMAP5,GIMAP6,GIMAP7,GIMAP8,GIN1,GINM1,GINS1,GINS2,GINS3,GINS4,GIP,GIPC1,GIPC2,GIPC3,GIPR,GIT1,GIT2,GJA1,GJA10,GJA3,GJA4,GJA5,GJA8,GJA9,GJA9-MYCBP,GJB1,GJB2,GJB3,GJB4,GJB5,GJB6,GJC1,GJC2,GJD2,GJD3,GJD4,GK,GK-AS1,GK2,GK3,GK5,GKAP1,GKN1,GKN2,GLA,GLB1,GLB1L,GLB1L2,GLB1L3,GLCCI1,GLCE,GLDC,GLDN,GLE1,GLG1,GLI1,GLI2,GLI3,GLI4,GLIDR,GLIPR1,GLIPR1L1,GLIPR2,GLIS1,GLIS2,GLIS3,GLIS3-AS1,GLMN,GLMP,GLO1,GLOD4,GLOD5,GLP1R,GLP2R,GLRA1,GLRA2,GLRA3,GLRB,GLRX,GLRX2,GLRX3,GLRX5,GLS,GLS2,GLT1D1,GLT8D1,GLT8D2,GLTP,GLTPD2,GLUD1,GLUD2,GLUL,GLULP4,GLYAT,GLYATL1,GLYATL2,GLYCTK,GLYCTK-AS1,GLYR1,GM2A,GMCL1,GMCL2,GMDS,GMDS-DT,GMEB1,GMEB2,GMFB,GMFG,GMIP,GML,GMNC,GMNN,GMPPA,GMPPB,GMPR,GMPR2,GMPS,GNA11,GNA12,GNA13,GNA14,GNA14-AS1,GNA15,GNAI1,GNAI2,GNAI3,GNAL,GNAO1,GNAQ,GNAS,GNAS-AS1,GNAT1,GNAT2,GNAT3,GNAZ,GNB1,GNB1L,GNB2,GNB3,GNB4,GNB5,GNE,GNG10,GNG11,GNG12,GNG13,GNG2,GNG3,GNG4,GNG5,GNG7,GNG8,GNGT1,GNGT2,GNL1,GNL2,GNL3,GNL3L,GNLY,GNMT,GNPAT,GNPDA1,GNPDA2,GNPNAT1,GNPTAB,GNPTG,GNRH1,GNRH2,GNRHR,GNRHR2,GNS,GOLGA1,GOLGA2,GOLGA2P2Y,GOLGA2P3Y,GOLGA2P5,GOLGA2P7,GOLGA3,GOLGA4,GOLGA5,GOLGA6A,GOLGA6B,GOLGA6C,GOLGA6D,GOLGA6L1,GOLGA6L11P,GOLGA6L16P,GOLGA6L2,GOLGA6L22,GOLGA6L4,GOLGA6L5P,GOLGA6L6,GOLGA6L9,GOLGA7,GOLGA7B,GOLGA8A,GOLGA8B,GOLGA8CP,GOLGA8DP,GOLGA8EP,GOLGA8F,GOLGA8G,GOLGA8IP,GOLGA8M,GOLGA8N,GOLGB1,GOLIM4,GOLM1,GOLM2,GOLPH3,GOLPH3L,GOLT1A,GOLT1B,GON4L,GON7,GOPC,GORAB,GORASP1,GORASP2,GOSR1,GOSR2,GOT1,GOT1L1,GOT2,GOT2P1,GP1BA,GP1BB,GP2,GP5,GP6,GP9,GPA33,GPAA1,GPALPP1,GPAM,GPANK1,GPAT2,GPAT3,GPAT4,GPATCH1,GPATCH11,GPATCH2,GPATCH2L,GPATCH3,GPATCH4,GPATCH8,GPBAR1,GPBP1,GPBP1L1,GPC1,GPC2,GPC3,GPC4,GPC5,GPC6,GPCPD1,GPD1,GPD1L,GPD2,GPER1,GPHA2,GPHB5,GPHN,GPI,GPIHBP1,GPKOW,GPLD1,GPM6A,GPM6B,GPN1,GPN2,GPN3,GPNMB,GPR101,GPR107,GPR108,GPR12,GPR132,GPR135,GPR137,GPR137B,GPR137C,GPR143,GPR146,GPR15,GPR150,GPR153,GPR155,GPR156,GPR157,GPR158,GPR15LG,GPR160,GPR161,GPR162,GPR17,GPR171,GPR173,GPR174,GPR176,GPR18,GPR180,GPR182,GPR183,GPR19,GPR20,GPR21,GPR22,GPR25,GPR26,GPR27,GPR3,GPR31,GPR32,GPR34,GPR35,GPR37,GPR37L1,GPR39,GPR4,GPR42,GPR45,GPR50,GPR52,GPR55,GPR6,GPR61,GPR62,GPR63,GPR65,GPR68,GPR75,GPR75-ASB3,GPR78,GPR82,GPR83,GPR84,GPR85,GPR87,GPR88,GPR89A,GPR89B,GPRASP1,GPRASP2,GPRC5A,GPRC5B,GPRC5C,GPRC5D,GPRC6A,GPRIN1,GPRIN2,GPRIN3,GPS1,GPS2,GPSM1,GPSM2,GPSM3,GPT,GPT2,GPX1,GPX2,GPX3,GPX4,GPX5,GPX7,GPX8,GRAMD1A,GRAMD1B,GRAMD1C,GRAMD2A,GRAMD2B,GRAMD4,GRAP,GRAP2,GRAPL,GRB10,GRB14,GRB2,GRB7,GREB1,GREB1L,GREM1,GREM2,GRHL1,GRHL2,GRHL3,GRHPR,GRIA1,GRIA2,GRIA3,GRIA4,GRID1,GRID1-AS1,GRID2,GRIK1,GRIK1-AS1,GRIK1-AS2,GRIK2,GRIK3,GRIK4,GRIK5,GRIN1,GRIN2A,GRIN2B,GRIN2C,GRIN2D,GRIN3A,GRIN3B,GRINA,GRIP1,GRIP2,GRIPAP1,GRK1,GRK2,GRK3,GRK4,GRK5,GRK6,GRK7,GRM1,GRM2,GRM3,GRM4,GRM5,GRM6,GRM7,GRM8,GRN,GRP,GRPEL1,GRPEL2,GRPR,GRSF1,GRTP1,GRTP1-AS1,GRWD1,GSAP,GSC,GSC2,GSDMB,GSDMC,GSDMD,GSDME,GSE1,GSG1,GSG1L,GSK3A,GSK3B,GSKIP,GSN,GSN-AS1,GSPT1,GSPT2,GSR,GSS,GSTA1,GSTA3,GSTA4,GSTCD,GSTK1,GSTM1,GSTM2,GSTM3,GSTM4,GSTM5,GSTO1,GSTO2,G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BP4P4,RBBP5,RBBP6,RBBP7,RBBP8,RBBP8NL,RBBP9,RBCK1,RBFA,RBFADN,RBFOX1,RBFOX2,RBFOX3,RBIS,RBKS,RBL1,RBL2,RBM10,RBM11,RBM12,RBM12B,RBM12B-AS1,RBM14,RBM14-RBM4,RBM15,RBM15B,RBM17,RBM18,RBM19,RBM20,RBM22,RBM23,RBM24,RBM25,RBM26,RBM26-AS1,RBM27,RBM28,RBM3,RBM33,RBM34,RBM38,RBM39,RBM4,RBM41,RBM42,RBM43,RBM45,RBM46,RBM47,RBM48,RBM4B,RBM5,RBM6,RBM7,RBM8A,RBMS1,RBMS2,RBMS3,RBMS3-AS3,RBMX,RBMX2,RBMXL1,RBMXL2,RBMY1A1,RBMY1B,RBMY1D,RBMY1E,RBMY1F,RBMY1J,RBMY2AP,RBMY2BP,RBMY2DP,RBMY2FP,RBMY2TP,RBMY3AP,RBP1,RBP2,RBP3,RBP4,RBP5,RBP7,RBPJ,RBPJL,RBPMS,RBPMS-AS1,RBPMS2,RBSN,RBX1,RC3H1,RC3H2,RCAN1,RCAN2,RCAN3,RCBTB1,RCBTB2,RCC1,RCC1L,RCC2,RCCD1,RCE1,RCHY1,RCL1,RCN1,RCN2,RCN3,RCOR1,RCOR2,RCOR3,RCSD1,RCVRN,RD3,RDH10,RDH11,RDH12,RDH13,RDH14,RDH16,RDH5,RDH8,RDM1,RDX,REC114,REC8,RECK,RECQL,RECQL4,RECQL5,REDIC1,REEP1,REEP2,REEP3,REEP4,REEP5,REEP6,REG1A,REG1B,REG1CP,REG3A,REG3G,REG4,REL,RELA,RELB,RELCH,RELL1,RELL2,RELN,RELT,REM1,REM2,REN,RENBP,REPIN1,REPS1,REPS2,RER1,RERE,RERG,RERGL,RESF1,REST,RET,RETN,RETNLB,RETREG1,RETREG2,RETREG3,RETSAT,REV1,REV3L,REX1BD,REXO1,REXO1L1P,REXO2,REXO4,RFC1,RFC2,RFC3,RFC4,RFC5,RFESD,RFFL,RFK,RFLNA,RFLNB,RFNG,RFPL1,RFPL1S,RFPL2,RFPL3,RFPL3S,RFT1,RFTN1,RFTN2,RFWD3,RFX1,RFX2,RFX3,RFX4,RFX5,RFX6,RFX7,RFXANK,RFXAP,RGCC,RGL1,RGL2,RGL3,RGL4,RGMA,RGMB,RGMB-AS1,RGN,RGP1,RGPD1,RGPD2,RGPD3,RGPD4,RGPD4-AS1,RGPD5,RGPD6,RGPD8,RGR,RGS1,RGS10,RGS11,RGS12,RGS13,RGS14,RGS16,RGS17,RGS18,RGS19,RGS2,RGS20,RGS22,RGS3,RGS4,RGS5,RGS6,RGS7,RGS7BP,RGS8,RGS9,RGS9BP,RGSL1,RHAG,RHBDD1,RHBDD2,RHBDD3,RHBDF1,RHBDF2,RHBDL1,RHBDL2,RHBDL3,RHBG,RHCE,RHCG,RHD,RHEB,RHEBL1,RHEX,RHNO1,RHO,RHOA,RHOB,RHOBTB1,RHOBTB2,RHOBTB3,RHOC,RHOD,RHOF,RHOG,RHOH,RHOJ,RHOQ,RHOT1,RHOT2,RHOU,RHOV,RHOXF1,RHOXF2,RHOXF2B,RHPN1,RHPN1-AS1,RHPN2,RIBC1,RIBC2,RIC1,RIC3,RIC8A,RIC8B,RICTOR,RIDA,RIF1,RIGI,RIIAD1,RILP,RILPL1,RILPL2,RIMBP2,RIMBP3,RIMBP3B,RIMBP3C,RIMKLA,RIMKLB,RIMOC1,RIMS1,RIMS2,RIMS3,RIMS4,RIN1,RIN2,RIN3,RING1,RINT1,RIOK1,RIOK2,RIOK3,RIOX1,RIOX2,RIPK1,RIPK2,RIPK3,RIPK4,RIPOR1,RIPOR2,RIPOR3,RIPPLY1,RIPPLY2,RIPPLY3,RIT1,RIT2,RITA1,RLBP1,RLF,RLIG1,RLIM,RLN1,RLN2,RLN3,RMC1,RMDN1,RMDN2,RMDN3,RMI1,RMI2,RMND1,RMND5A,RMND5B,RMST,RN7SKP150,RNA45S5,RNASE1,RNASE11,RNASE2,RNASE2CP,RNASE3,RNASE4,RNASE6,RNASE7,RNASEH1,RNASEH2A,RNASEH2B,RNASEH2C,RNASEK,RNASEK-C17orf49,RNASEL,RNASET2,RND1,RND2,RND3,RNF10,RNF103,RNF11,RNF111,RNF112,RNF113A,RNF114,RNF115,RNF11P2,RNF121,RNF122,RNF123,RNF125,RNF126,RNF126P1,RNF128,RNF13,RNF130,RNF133,RNF135,RNF138,RNF139,RNF139-DT,RNF14,RNF141,RNF144A,RNF144B,RNF145,RNF146,RNF148,RNF149,RNF150,RNF151,RNF152,RNF157,RNF157-AS1,RNF166,RNF167,RNF168,RNF169,RNF17,RNF170,RNF175,RNF180,RNF181,RNF182,RNF183,RNF185,RNF185-AS1,RNF186,RNF187,RNF19A,RNF19B,RNF2,RNF20,RNF207,RNF208,RNF212,RNF212B,RNF213,RNF214,RNF215,RNF216,RNF217,RNF217-AS1,RNF220,RNF24,RNF25,RNF26,RNF31,RNF32,RNF32-DT,RNF34,RNF38,RNF39,RNF4,RNF40,RNF41,RNF43,RNF44,RNF5,RNF5P1,RNF6,RNF7,RNF8,RNFT1,RNFT2,RNGTT,RNH1,RNLS,RNMT,RNPC3,RNPEP,RNPEPL1,RNPS1,RNU2-22P,RO60,ROBO1,ROBO2,ROBO3,ROBO4,ROCK1,ROCK2,ROGDI,ROM1,ROMO1,ROPN1,ROPN1B,ROPN1L,ROR1,ROR1-AS1,ROR2,RORA,RORB,RORC,ROS1,RP1,RP1L1,RP2,RP9,RP9P,RPA1,RPA2,RPA3,RPA4,RPAIN,RPAP1,RPAP2,RPAP3,RPE,RPE65,RPEL1,RPF1,RPF2,RPGR,RPGRIP1,RPGRIP1L,RPH3A,RPH3AL,RPIA,RPL10,RPL10A,RPL10L,RPL11,RPL12,RPL13,RPL13A,RPL13AP17,RPL13AP5,RPL13AP6,RPL13P5,RPL14,RPL15,RPL17,RPL18,RPL18A,RPL18AP16,RPL18P10,RPL19,RPL21,RPL21P28,RPL22,RPL22L1,RPL22P22,RPL23,RPL23A,RPL23AP22,RPL23AP32,RPL23AP53,RPL23AP7,RPL24,RPL26L1,RPL26P37,RPL27,RPL27A,RPL27AP,RPL28,RPL29,RPL29P7,RPL3,RPL30,RPL31,RPL32,RPL32P3,RPL34,RPL34-DT,RPL35,RPL35A,RPL35P8,RPL36,RPL36A,RPL36AL,RPL37,RPL37A,RPL38,RPL39,RPL39L,RPL3L,RPL4,RPL41,RPL5,RPL6,RPL7,RPL7A,RPL7AP10,RPL7AP12,RPL7AP71,RPL7L1,RPL7P25,RPL7P27,RPL7P52,RPL8,RPL9,RPLP0,RPLP0P1,RPLP1,RPLP2,RPLP2P1,RPN1,RPN2,RPP14,RPP21,RPP25,RPP25L,RPP30,RPP38,RPP40,RPPH1,RPRD1A,RPRD1B,RPRD2,RPRM,RPRML,RPS10,RPS10P2,RPS10P5,RPS10P7,RPS11,RPS11P1,RPS11P6,RPS12,RPS13,RPS14,RPS15,RPS15A,RPS16,RPS16P5,RPS17,RPS17P5,RPS18,RPS19,RPS19BP1,RPS2,RPS20,RPS21,RPS23,RPS23P6,RPS24,RPS25,RPS26,RPS26P11,RPS27,RPS27A,RPS27L,RPS28,RPS29,RPS29P28,RPS2P45,RPS3,RPS3A,RPS4X,RPS4XP2,RPS4XP3,RPS4XP9,RPS4Y1,RPS5,RPS6,RPS6KA1,RPS6KA2,RPS6KA3,RPS6KA4,RPS6KA5,RPS6KA6,RPS6KB1,RPS6KB2,RPS6KC1,RPS6KL1,RPS7,RPS8,RPS9,RPSA,RPSA2,RPSAP19,RPSAP9,RPTN,RPTOR,RPUSD1,RPUSD2,RPUSD3,RPUSD4,RRAD,RRAGA,RRAGB,RRAGC,RRAGD,RRAS,RRAS2,RRBP1,RREB1,RRH,RRM1,RRM2,RRM2B,RRN3,RRN3P1,RRN3P2,RRN3P3,RRP1,RRP12,RRP15,RRP1B,RRP36,RRP7A,RRP7BP,RRP8,RRP9,RRS1,RRS1-DT,RS1,RSAD1,RSAD2,RSBN1,RSBN1L,RSC1A1,RSF1,RSKR,RSL1D1,RSL24D1,RSPH1,RSPH10B,RSPH10B2,RSPH14,RSPH3,RSPH4A,RSPH6A,RSPH9,RSPO1,RSPO2,RSPO3,RSPO4,RSPRY1,RSRC1,RSRC2,RSRP1,RSU1,RSU1P2,RTBDN,RTCA,RTCB,RTEL1,RTF1,RTF2,RTKN,RTKN2,RTL10,RTL3,RTL5,RTL6,RTL8A,RTL8C,RTL9,RTN1,RTN2,RTN3,RTN4,RTN4IP1,RTN4R,RTN4RL1,RTN4RL2,RTP1,RTP3,RTP4,RTP5,RTRAF,RTTN,RUBCN,RUBCNL,RUFY1,RUFY2,RUFY3,RUNDC1,RUNDC3A,RUNDC3B,RUNX1,RUNX1-IT1,RUNX1T1,RUNX2,RUNX3,RUSC1,RUSC1-AS1,RUSC2,RUSF1,RUVBL1,RUVBL2,RWDD1,RWDD2A,RWDD2B,RWDD3,RWDD4,RXFP1,RXFP2,RXFP3,RXRA,RXRB,RXRG,RXYLT1,RYBP,RYK,RYR1,RYR2,RYR3,S100A1,S100A10,S100A11,S100A11P1,S100A12,S100A13,S100A14,S100A16,S100A2,S100A3,S100A4,S100A5,S100A6,S100A7,S100A7A,S100A8,S100A9,S100B,S100G,S100P,S100PBP,S100Z,S1PR1,S1PR2,S1PR3,S1PR4,S1PR5,SAA1,SAA2,SAA2-SAA4,SAA3P,SAA4,SAAL1,SAC3D1,SACM1L,SACS,SACS-AS1,SAE1,SAFB,SAFB2,SAG,SAGE1,SALL1,SALL2,SALL3,SALL4,SAMD1,SAMD10,SAMD11,SAMD12,SAMD13,SAMD14,SAMD15,SAMD3,SAMD4A,SAMD4B,SAMD5,SAMD8,SAMD9,SAMD9L,SAMHD1,SAMM50,SAMSN1,SAMTOR,SANBR,SAP130,SAP18,SAP25,SAP30,SAP30BP,SAP30L,SAPCD1,SAPCD1-AS1,SAPCD2,SAR1A,SAR1B,SARAF,SARDH,SARM1,SARNP,SARS2,SART1,SART3,SASH1,SASH3,SASS6,SAT1,SAT2,SATB1,SATB2,SATB2-AS1,SAV1,SAXO1,SAXO2,SAXO4,SAXO5,SAYSD1,SBDS,SBDSP1,SBF1,SBF2,SBF2-AS1,SBK1,SBNO1,SBNO2,SBSN,SBSPON,SC5D,SCAF1,SCAF11,SCAF4,SCAF8,SCAI,SCAMP1,SCAMP1-AS1,SCAMP2,SCAMP3,SCAMP4,SCAMP5,SCAND1,SCAND2P,SCAND3,SCAP,SCAPER,SCARA3,SCARA5,SCARB1,SCARB2,SCARF1,SCARF2,SCARNA13,SCARNA15,SCARNA16,SCARNA17,SCARNA2,SCART1,SCCPDH,SCD,SCD5,SCEL,SCFD1,SCFD2,SCG2,SCG3,SCG5,SCGB1A1,SCGB1C1,SCGB1D1,SCGB1D2,SCGB2A1,SCGB2A2,SCGB2B2,SCGB3A1,SCGB3A2,SCGN,SCHIP1,SCIMP,SCIN,SCLT1,SCLY,SCMH1,SCML1,SCML2,SCML4,SCN10A,SCN11A,SCN1A,SCN1B,SCN2A,SCN2B,SCN3A,SCN3B,SCN4A,SCN4B,SCN5A,SCN7A,SCN8A,SCN9A,SCNM1,SCNN1A,SCNN1B,SCNN1D,SCNN1G,SCO1,SCO2,SCOC,SCOC-AS1,SCP2,SCP2D1,SCP2D1-AS1,SCPEP1,SCRG1,SCRIB,SCRN1,SCRN2,SCRN3,SCRT1,SCT,SCTR,SCUBE1,SCUBE2,SCUBE3,SCYL1,SCYL2,SCYL3,SDAD1,SDC1,SDC2,SDC3,SDC4,SDCBP,SDCBP2,SDCBP2-AS1,SDCCAG8,SDE2,SDF2,SDF2L1,SDF4,SDHA,SDHAF1,SDHAF2,SDHAF3,SDHAF4,SDHAP1,SDHAP2,SDHAP3,SDHB,SDHC,SDHD,SDK1,SDK2,SDR16C5,SDR39U1,SDR42E1,SDR9C7,SDS,SDSL,SEBOX,SEC11A,SEC11C,SEC13,SEC14L1,SEC14L1P1,SEC14L2,SEC14L3,SEC14L4,SEC14L5,SEC16A,SEC16B,SEC1P,SEC22A,SEC22B,SEC22C,SEC23A,SEC23B,SEC23IP,SEC24A,SEC24B,SEC24B-AS1,SEC24C,SEC24D,SEC31A,SEC31B,SEC61A1,SEC61A2,SEC61B,SEC61G,SEC62,SEC63,SECISBP2,SECISBP2L,SECTM1,SEH1L,SEL1L,SEL1L2,SEL1L3,SELE,SELENBP1,SELENOF,SELENOH,SELENOI,SELENOK,SELENOM,SELENON,SELENOO,SELENOP,SELENOS,SELENOT,SELENOW,SELL,SELP,SELPLG,SEM1,SEMA3A,SEMA3B,SEMA3C,SEMA3D,SEMA3E,SEMA3F,SEMA3G,SEMA4A,SEMA4B,SEMA4C,SEMA4D,SEMA4F,SEMA4G,SEMA5A,SEMA5B,SEMA6A,SEMA6B,SEMA6C,SEMA6D,SEMA7A,SEMG1,SEMG2,SENCR,SENP1,SENP2,SENP3,SENP5,SENP6,SENP7,SENP8,SEPHS1,SEPHS2,SEPSECS,SEPTIN1,SEPTIN10,SEPTIN11,SEPTIN12,SEPTIN3,SEPTIN4,SEPTIN5,SEPTIN6,SEPTIN7,SEPTIN7P2,SEPTIN7P9,SEPTIN8,SEPTIN9,SERAC1,SERBP1,SERF1A,SERF1B,SERF2,SERF2-C15ORF63,SERGEF,SERHL,SERHL2,SERINC1,SERINC2,SERINC3,SERINC5,SERP1,SERP2,SERPINA1,SERPINA10,SERPINA12,SERPINA2,SERPINA3,SERPINA4,SERPINA5,SERPINA6,SERPINA7,SERPINA9,SERPINB1,SERPINB10,SERPINB11,SERPINB12,SERPINB13,SERPINB2,SERPINB3,SERPINB4,SERPINB5,SERPINB6,SERPINB7,SERPINB8,SERPINB9,SERPINB9P1,SERPINC1,SERPIND1,SERPINE1,SERPINE2,SERPINF1,SERPINF2,SERPING1,SERPINH1,SERPINI1,SERPINI2,SERTAD1,SERTAD2,SERTAD3,SERTAD4,SERTM1,SESN1,SESN2,SESN3,SESTD1,SET,SETBP1,SETD1A,SETD1B,SETD2,SETD3,SETD4,SETD5,SETD6,SETD7,SETD9,SETDB1,SETDB2,SETMAR,SETP4,SETSIP,SETX,SEZ6,SEZ6L,SEZ6L2,SF1,SF3A1,SF3A2,SF3A3,SF3B1,SF3B2,SF3B3,SF3B4,SF3B5,SF3B6,SFI1,SFMBT1,SFMBT2,SFN,SFPQ,SFR1,SFRP1,SFRP2,SFRP4,SFRP5,SFSWAP,SFT2D1,SFT2D2,SFT2D3,SFTA1P,SFTA2,SFTA3,SFTPA1,SFTPA2,SFTPB,SFTPC,SFTPD,SFXN1,SFXN2,SFXN3,SFXN4,SFXN5,SGCA,SGCB,SGCD,SGCE,SGCG,SGCZ,SGF29,SGIP1,SGK1,SGK2,SGK3,SGMS1,SGMS2,SGO1,SGO2,SGPL1,SGPP1,SGPP2,SGSH,SGSM1,SGSM2,SGSM3,SGTA,SGTB,SH2B1,SH2B2,SH2B3,SH2D1A,SH2D1B,SH2D2A,SH2D3A,SH2D3C,SH2D4A,SH2D4B,SH2D5,SH2D6,SH3BGR,SH3BGRL,SH3BGRL2,SH3BGRL3,SH3BP1,SH3BP2,SH3BP4,SH3BP5,SH3BP5-AS1,SH3BP5L,SH3D19,SH3D21,SH3GL1,SH3GL1P1,SH3GL1P2,SH3GL2,SH3GL3,SH3GLB1,SH3GLB2,SH3KBP1,SH3PXD2A,SH3PXD2B,SH3RF1,SH3RF2,SH3RF3,SH3RF3-AS1,SH3TC1,SH3TC2,SH3YL1,SHANK1,SHANK2,SHANK2-AS2,SHANK2-AS3,SHANK3,SHARPIN,SHB,SHBG,SHC1,SHC1P1,SHC2,SHC3,SHC4,SHCBP1,SHCBP1L,SHD,SHE,SHF,SHFL,SHH,SHISA2,SHISA3,SHISA4,SHISA5,SHISA6,SHISA7,SHISA8,SHISA9,SHISAL1,SHISAL2A,SHKBP1,SHLD1,SHLD2,SHMT1,SHMT2,SHOC1,SHOC2,SHOX,SHOX2,SHPK,SHPRH,SHQ1,SHROOM1,SHROOM2,SHROOM3,SHROOM4,SHTN1,SI,SIAE,SIAH1,SIAH2,SIAH3,SIDT1,SIDT2,SIGIRR,SIGLEC1,SIGLEC10,SIGLEC11,SIGLEC15,SIGLEC16,SIGLEC17P,SIGLEC5,SIGLEC6,SIGLEC7,SIGLEC8,SIGLEC9,SIGLECL1,SIGMAR1,SIK1,SIK2,SIK3,SIKE1,SIL1,SIM1,SIM2,SIMC1,SIN3A,SIN3B,SINHCAF,SIPA1,SIPA1L1,SIPA1L2,SIPA1L3,SIRPA,SIRPAP1,SIRPB1,SIRPB2,SIRPD,SIRPG,SIRT1,SIRT2,SIRT3,SIRT4,SIRT5,SIRT6,SIRT7,SIT1,SIVA1,SIX1,SIX2,SIX3,SIX3-AS1,SIX4,SIX5,SIX6,SKA1,SKA2,SKA3,SKAP1,SKAP2,SKI,SKIC2,SKIC3,SKIC8,SKIDA1,SKIL,SKP1,SKP2,SLA,SLA2,SLAIN1,SLAIN2,SLAMF1,SLAMF6,SLAMF7,SLAMF8,SLAMF9,SLBP,SLC10A1,SLC10A2,SLC10A3,SLC10A4,SLC10A7,SLC11A1,SLC11A2,SLC12A1,SLC12A2,SLC12A3,SLC12A4,SLC12A5,SLC12A6,SLC12A7,SLC12A8,SLC12A9,SLC13A1,SLC13A2,SLC13A3,SLC13A4,SLC13A5,SLC14A1,SLC14A2,SLC15A1,SLC15A2,SLC15A3,SLC15A4,SLC16A1,SLC16A10,SLC16A11,SLC16A14,SLC16A2,SLC16A3,SLC16A4,SLC16A5,SLC16A6,SLC16A7,SLC16A8,SLC16A9,SLC17A1,SLC17A2,SLC17A3,SLC17A4,SLC17A5,SLC17A6,SLC17A7,SLC17A8,SLC17A9,SLC18A1,SLC18A2,SLC18A3,SLC18B1,SLC19A1,SLC19A2,SLC19A3,SLC19A4P,SLC1A1,SLC1A2,SLC1A3,SLC1A4,SLC1A5,SLC1A6,SLC1A7,SLC20A1,SLC20A2,SLC22A1,SLC22A11,SLC22A12,SLC22A13,SLC22A14,SLC22A15,SLC22A16,SLC22A17,SLC22A18,SLC22A18AS,SLC22A2,SLC22A23,SLC22A24,SLC22A25,SLC22A3,SLC22A31,SLC22A4,SLC22A5,SLC22A6,SLC22A7,SLC22A8,SLC22A9,SLC23A1,SLC23A2,SLC23A3,SLC24A1,SLC24A2,SLC24A3,SLC24A4,SLC24A5,SLC25A1,SLC25A10,SLC25A11,SLC25A12,SLC25A13,SLC25A14,SLC25A15,SLC25A16,SLC25A17,SLC25A18,SLC25A19,SLC25A2,SLC25A20,SLC25A21,SLC25A22,SLC25A23,SLC25A24,SLC25A25,SLC25A26,SLC25A27,SLC25A28,SLC25A29,SLC25A3,SLC25A30,SLC25A31,SLC25A32,SLC25A33,SLC25A34,SLC25A35,SLC25A36,SLC25A37,SLC25A38,SLC25A39,SLC25A3P1,SLC25A4,SLC25A40,SLC25A41,SLC25A42,SLC25A43,SLC25A44,SLC25A45,SLC25A46,SLC25A47,SLC25A48,SLC25A5,SLC25A51,SLC25A53,SLC25A6,SLC26A1,SLC26A10P,SLC26A11,SLC26A2,SLC26A3,SLC26A4,SLC26A6,SLC26A7,SLC26A8,SLC26A9,SLC27A1,SLC27A2,SLC27A3,SLC27A4,SLC27A5,SLC27A6,SLC28A1,SLC28A2,SLC28A3,SLC29A1,SLC29A2,SLC29A3,SLC29A4,SLC2A1,SLC2A1-DT,SLC2A10,SLC2A11,SLC2A12,SLC2A13,SLC2A14,SLC2A2,SLC2A3,SLC2A4,SLC2A4RG,SLC2A5,SLC2A6,SLC2A8,SLC2A9,SLC30A1,SLC30A10,SLC30A2,SLC30A3,SLC30A4,SLC30A5,SLC30A6,SLC30A7,SLC30A8,SLC30A9,SLC31A1,SLC31A2,SLC32A1,SLC33A1,SLC34A1,SLC34A2,SLC34A3,SLC35A1,SLC35A2,SLC35A3,SLC35A4,SLC35A5,SLC35B1,SLC35B2,SLC35B3,SLC35B4,SLC35C1,SLC35C2,SLC35D1,SLC35D2,SLC35D3,SLC35E1,SLC35E2B,SLC35E3,SLC35E4,SLC35F1,SLC35F2,SLC35F3,SLC35F4,SLC35F5,SLC35F6,SLC35G1,SLC35G2,SLC35G3,SLC35G4,SLC35G5,SLC36A1,SLC36A4,SLC37A1,SLC37A2,SLC37A3,SLC37A4,SLC38A1,SLC38A10,SLC38A11,SLC38A2,SLC38A3,SLC38A4,SLC38A5,SLC38A6,SLC38A7,SLC38A9,SLC39A1,SLC39A10,SLC39A11,SLC39A12,SLC39A13,SLC39A14,SLC39A2,SLC39A3,SLC39A4,SLC39A5,SLC39A6,SLC39A7,SLC39A8,SLC39A9,SLC3A1,SLC3A2,SLC40A1,SLC41A1,SLC41A2,SLC41A3,SLC43A1,SLC43A2,SLC43A3,SLC44A1,SLC44A2,SLC44A3,SLC44A4,SLC44A5,SLC45A1,SLC45A2,SLC45A3,SLC45A4,SLC46A1,SLC46A2,SLC46A3,SLC47A1,SLC47A2,SLC48A1,SLC49A3,SLC49A4,SLC4A1,SLC4A10,SLC4A11,SLC4A1AP,SLC4A2,SLC4A3,SLC4A4,SLC4A5,SLC4A7,SLC4A8,SLC4A9,SLC50A1,SLC51A,SLC51B,SLC52A1,SLC52A2,SLC52A3,SLC5A1,SLC5A10,SLC5A11,SLC5A12,SLC5A2,SLC5A3,SLC5A4,SLC5A5,SLC5A6,SLC5A7,SLC5A8,SLC5A9,SLC66A1,SLC66A1LP,SLC66A2,SLC66A3,SLC6A1,SLC6A10P,SLC6A10PB,SLC6A11,SLC6A12,SLC6A13,SLC6A14,SLC6A15,SLC6A16,SLC6A17,SLC6A18,SLC6A19,SLC6A2,SLC6A20,SLC6A3,SLC6A4,SLC6A5,SLC6A6,SLC6A7,SLC6A8,SLC6A9,SLC7A1,SLC7A10,SLC7A11,SLC7A13,SLC7A14,SLC7A2,SLC7A3,SLC7A4,SLC7A5,SLC7A5P1,SLC7A5P2,SLC7A6,SLC7A6OS,SLC7A7,SLC7A8,SLC7A9,SLC8A1,SLC8A1-AS1,SLC8A2,SLC8A3,SLC8B1,SLC9A1,SLC9A2,SLC9A3,SLC9A3-OT1,SLC9A5,SLC9A6,SLC9A7,SLC9A8,SLC9A9,SLC9B1,SLC9B2,SLC9C2,SLCO1A2,SLCO1B1,SLCO1B3,SLCO1C1,SLCO2A1,SLCO2B1,SLCO3A1,SLCO4A1,SLCO4C1,SLCO5A1,SLCO6A1,SLF1,SLF2,SLFN11,SLFN12,SLFN13,SLFN5,SLFNL1,SLFNL1-AS1,SLIRP,SLIRP-OT1,SLIT1,SLIT2,SLIT2-IT1,SLIT3,SLITRK1,SLITRK2,SLITRK3,SLITRK4,SLITRK5,SLITRK6,SLK,SLMAP,SLN,SLPI,SLTM,SLU7,SLURP1,SLX1A,SLX1B,SLX4,SLX4IP,SLX9,SMAD1,SMAD1-AS2,SMAD2,SMAD3,SMAD4,SMAD5,SMAD5-AS1,SMAD6,SMAD7,SMAD9,SMAGP,SMAP1,SMAP2,SMARCA1,SMARCA2,SMARCA4,SMARCA5,SMARCAD1,SMARCAL1,SMARCB1,SMARCC1,SMARCC2,SMARCD1,SMARCD2,SMARCD3,SMARCE1,SMARCE1P2,SMC1A,SMC1B,SMC2,SMC3,SMC4,SMC5,SMC5-DT,SMC6,SMCHD1,SMCO1,SMCO3,SMCO4,SMCP,SMCR5,SMCR8,SMDT1,SMG1,SMG1P1,SMG1P2,SMG1P3,SMG1P5,SMG1P7,SMG5,SMG6,SMG7,SMG7-AS1,SMG8,SMG9,SMIM1,SMIM10,SMIM11,SMIM12,SMIM13,SMIM14,SMIM15,SMIM17,SMIM19,SMIM2,SMIM2-AS1,SMIM20,SMIM21,SMIM22,SMIM24,SMIM27,SMIM29,SMIM3,SMIM43,SMIM5,SMIM7,SMIM8,SMKR1,SMLR1,SMN1,SMN2,SMNDC1,SMO,SMOC1,SMOC2,SMOX,SMPD1,SMPD2,SMPD3,SMPD4,SMPDL3A,SMPDL3B,SMPX,SMR3A,SMR3B,SMS,SMTN,SMTNL1,SMTNL2,SMU1,SMUG1,SMURF1,SMURF2,SMYD1,SMYD2,SMYD3,SMYD4,SMYD5,SNAI1,SNAI1P1,SNAI2,SNAI3,SNAI3-AS1,SNAP23,SNAP25,SNAP29,SNAP47,SNAP91,SNAPC1,SNAPC2,SNAPC3,SNAPC4,SNAPC5,SNAPIN,SNCA,SNCAIP,SNCB,SNCG,SND1,SND1-IT1,SNED1,SNF8,SNHG1,SNHG10,SNHG11,SNHG12,SNHG15,SNHG16,SNHG17,SNHG18,SNHG19,SNHG20,SNHG22,SNHG28,SNHG3,SNHG32,SNHG4,SNHG5,SNHG6,SNHG7,SNHG8,SNHG9,SNIP1,SNN,SNORA1,SNORA11D,SNORA11E,SNORA16A,SNORA17A,SNORA17B,SNORA18,SNORA21,SNORA24,SNORA27,SNORA28,SNORA29,SNORA32,SNORA37,SNORA3A,SNORA4,SNORA40,SNORA41,SNORA44,SNORA48,SNORA50C,SNORA52,SNORA56,SNORA57,SNORA5B,SNORA6,SNORA60,SNORA61,SNORA62,SNORA63,SNORA64,SNORA65,SNORA66,SNORA67,SNORA68,SNORA70,SNORA71A,SNORA71B,SNORA71E,SNORA72,SNORA73A,SNORA74A,SNORA78,SNORA8,SNORA81,SNORA9,SNORC,SNORD10,SNORD102,SNORD104,SNORD107,SNORD110,SNORD112,SNORD114-3,SNORD115-7,SNORD116-4,SNORD139,SNORD14B,SNORD14C,SNORD14D,SNORD16,SNORD18A,SNORD18B,SNORD18C,SNORD19B,SNORD1A,SNORD1C,SNORD2,SNORD21,SNORD22,SNORD23,SNORD24,SNORD25,SNORD26,SNORD27,SNORD28,SNORD29,SNORD30,SNORD31,SNORD32A,SNORD33,SNORD34,SNORD35A,SNORD35B,SNORD36A,SNORD36B,SNORD38B,SNORD3A,SNORD3B-1,SNORD3B-2,SNORD3C,SNORD3D,SNORD42A,SNORD44,SNORD45A,SNORD45B,SNORD45C,SNORD47,SNORD49A,SNORD49B,SNORD4A,SNORD5,SNORD50A,SNORD50B,SNORD54,SNORD55,SNORD57,SNORD58A,SNORD58B,SNORD58C,SNORD61,SNORD65,SNORD68,SNORD73A,SNORD74,SNORD76,SNORD77,SNORD79,SNORD8,SNORD80,SNORD81,SNORD83B,SNORD84,SNORD86,SNORD87,SNORD88C,SNORD89,SNORD95,SNORD96A,SNPH,SNRK,SNRK-AS1,SNRNP200,SNRNP25,SNRNP27,SNRNP35,SNRNP40,SNRNP48,SNRNP70,SNRPA,SNRPA1,SNRPB,SNRPB2,SNRPC,SNRPD1,SNRPD2,SNRPD3,SNRPE,SNRPF,SNRPG,SNRPN,SNTA1,SNTB1,SNTB2,SNTG1,SNTG2,SNTN,SNU13,SNUPN,SNURF,SNW1,SNX1,SNX10,SNX11,SNX12,SNX13,SNX14,SNX15,SNX16,SNX17,SNX18,SNX18P3,SNX19,SNX2,SNX20,SNX21,SNX22,SNX24,SNX25,SNX27,SNX29,SNX29P1,SNX29P2,SNX3,SNX30,SNX31,SNX32,SNX33,SNX4,SNX5,SNX6,SNX7,SNX8,SNX9,SOAT1,SOAT2,SOBP,SOCS1,SOCS2,SOCS2-AS1,SOCS3,SOCS4,SOCS5,SOCS6,SOCS7,SOD1,SOD2,SOD3,SOHLH1,SOHLH2,SON,SORBS1,SORBS2,SORBS3,SORCS1,SORCS2,SORCS3,SORCS3-AS1,SORD,SORL1,SORT1,SOS1,SOS1-IT1,SOS2,SOST,SOSTDC1,SOWAHA,SOWAHC,SOX1,SOX10,SOX11,SOX12,SOX13,SOX14,SOX15,SOX17,SOX18,SOX2,SOX2-OT,SOX21,SOX21-AS1,SOX3,SOX30,SOX4,SOX5,SOX5P1,SOX6,SOX7,SOX8,SOX9,SOX9-AS1,SP1,SP100,SP110,SP140,SP140L,SP2,SP3,SP3P,SP4,SP5,SP6,SP7,SP8,SPA17,SPACA1,SPACA3,SPACA4,SPACA6,SPACA7,SPACA9,SPACDR,SPAG1,SPAG11A,SPAG11B,SPAG16,SPAG17,SPAG4,SPAG5,SPAG5-AS1,SPAG6,SPAG7,SPAG8,SPAG9,SPAM1,SPANXA1,SPANXA2,SPANXB1,SPANXC,SPARC,SPARCL1,SPART,SPART-AS1,SPAST,SPATA1,SPATA12,SPATA13,SPATA16,SPATA17,SPATA18,SPATA19,SPATA2,SPATA20,SPATA21,SPATA22,SPATA24,SPATA25,SPATA2L,SPATA3,SPATA3-AS1,SPATA31A1,SPATA31A3,SPATA31A5,SPATA31A6,SPATA31A7,SPATA31C2,SPATA31D1,SPATA31D3,SPATA31D4,SPATA31E1,SPATA31F1,SPATA31F2P,SPATA31F3,SPATA31G1,SPATA31H1,SPATA32,SPATA33,SPATA4,SPATA41,SPATA42,SPATA45,SPATA46,SPATA6,SPATA6L,SPATA7,SPATA8,SPATA9,SPATC1,SPATC1L,SPATS1,SPATS2,SPATS2L,SPC24,SPC25,SPCS1,SPCS2,SPCS3,SPDEF,SPDL1,SPDYA,SPDYE1,SPDYE2,SPDYE2B,SPDYE5,SPDYE6,SPECC1,SPECC1L,SPEF1,SPEF2,SPEG,SPEM1,SPEM2,SPEN,SPESP1,SPG11,SPG21,SPG7,SPHK1,SPHK2,SPHKAP,SPI1,SPIB,SPIC,SPICE1,SPIDR,SPIN1,SPIN2A,SPIN2B,SPIN3,SPIN4,SPINDOC,SPINK1,SPINK13,SPINK2,SPINK4,SPINK5,SPINK6,SPINK7,SPINT1,SPINT2,SPINT3,SPIRE1,SPIRE2,SPMAP1,SPMAP2,SPMIP10,SPMIP2,SPMIP3,SPMIP4,SPMIP5,SPMIP6,SPMIP8,SPMIP9,SPN,SPNS1,SPNS2,SPNS3,SPO11,SPOCD1,SPOCK1,SPOCK2,SPOCK3,SPON1,SPON2,SPOP,SPOPL,SPOUT1,SPP1,SPP2,SPPL2A,SPPL2B,SPPL2C,SPPL3,SPR,SPRED1,SPRED2,SPRED3,SPRING1,SPRN,SPRNP1,SPRR1A,SPRR1B,SPRR2B,SPRR2C,SPRR2G,SPRR3,SPRR4,SPRTN,SPRY1,SPRY2,SPRY3,SPRY4,SPRY4-IT1,SPRYD3,SPRYD4,SPRYD7,SPSB1,SPSB2,SPSB3,SPSB4,SPTA1,SPTAN1,SPTB,SPTBN1,SPTBN2,SPTBN4,SPTBN5,SPTLC1,SPTLC2,SPTLC3,SPTSSA,SPTSSB,SPTY2D1,SPX,SPZ1,SQLE,SQOR,SQSTM1,SRA1,SRARP,SRBD1,SRC,SRCAP,SRCIN1,SRD5A1,SRD5A2,SRD5A3,SRD5A3-AS1,SREBF1,SREBF2,SREK1,SREK1IP1,SRF,SRFBP1,SRGAP1,SRGAP2,SRGAP2B,SRGAP2C,SRGAP3,SRGN,SRI,SRL,SRM,SRMP1,SRMS,SRP14,SRP19,SRP54,SRP68,SRP72,SRP9,SRPK1,SRPK2,SRPK3,SRPRA,SRPRB,SRPX,SRPX2,SRR,SRRD,SRRM1,SRRM2,SRRM2-AS1,SRRM3,SRRM4,SRRM5,SRRT,SRSF1,SRSF10,SRSF11,SRSF12,SRSF2,SRSF3,SRSF4,SRSF5,SRSF6,SRSF7,SRSF8,SRSF9,SRXN1,SRY,SS18,SS18L1,SS18L2,SSB,SSBP1,SSBP2,SSBP3,SSBP3-AS1,SSBP4,SSC4D,SSC5D,SSH1,SSH2,SSH3,SSMEM1,SSNA1,SSPN,SSPOP,SSR1,SSR2,SSR3,SSR4,SSR4P1,SSRP1,SST,SSTR1,SSTR2,SSTR3,SSTR4,SSTR5,SSTR5-AS1,SSU72,SSU72L6,SSUH2,SSX1,SSX2,SSX2B,SSX2IP,SSX3,SSX4,SSX4B,SSX5,SSX7,ST13,ST14,ST18,ST20,ST20-AS1,ST20-MTHFS,ST3GAL1,ST3GAL2,ST3GAL3,ST3GAL4,ST3GAL5,ST3GAL6,ST6GAL1,ST6GAL2,ST6GALNAC1,ST6GALNAC2,ST6GALNAC3,ST6GALNAC4,ST6GALNAC5,ST6GALNAC6,ST7,ST7-AS1,ST7-AS2,ST7-OT3,ST7-OT4,ST7L,ST8SIA1,ST8SIA2,ST8SIA3,ST8SIA4,ST8SIA5,STAB1,STAB2,STAC,STAC2,STAC3,STAG1,STAG2,STAG3,STAG3L1,STAG3L2,STAG3L3,STAG3L4,STAM,STAM-DT,STAM2,STAMBP,STAMBPL1,STAP1,STAP2,STAR,STARD10,STARD13,STARD13-AS,STARD3,STARD3NL,STARD4,STARD5,STARD6,STARD7,STARD7-AS1,STARD8,STARD9,STAT1,STAT2,STAT3,STAT4,STAT5A,STAT5B,STAT6,STATH,STAU1,STAU2,STAU2-AS1,STBD1,STC1,STC2,STEAP1,STEAP1B,STEAP2,STEAP3,STEAP4,STH,STIL,STIM1,STIM2,STIMATE,STING1,STIP1,STK10,STK11,STK11IP,STK16,STK17A,STK17B,STK24,STK24-AS1,STK25,STK26,STK3,STK31,STK32A,STK32B,STK32C,STK33,STK35,STK36,STK38,STK38L,STK39,STK4,STK4-DT,STK40,STKLD1,STMN1,STMN2,STMN3,STMN4,STMP1,STN1,STOM,STOML1,STOML2,STOML3,STON1,STON2,STOX1,STOX2,STPG1,STPG2,STPG3,STPG4,STRA6,STRADA,STRADB,STRAP,STRBP,STRC,STRIP1,STRIP2,STRN,STRN3,STRN4,STS,STT3A,STT3B,STUB1,STUM,STX10,STX11,STX12,STX16,STX17,STX18,STX18-AS1,STX19,STX1A,STX1B,STX2,STX3,STX4,STX5,STX6,STX7,STX8,STXBP1,STXBP2,STXBP3,STXBP4,STXBP5,STXBP5-AS1,STXBP5L,STXBP6,STYK1,STYX,STYXL1,SUB1,SUCLA2,SUCLG1,SUCLG2,SUCLG2-DT,SUCNR1,SUCO,SUDS3,SUFU,SUGCT,SUGP1,SUGP2,SUGT1,SUGT1P1,SUGT1P3,SULF1,SULF2,SULT1A1,SULT1A2,SULT1A3,SULT1A4,SULT1B1,SULT1C2,SULT1C4,SULT1E1,SULT2A1,SULT2B1,SULT4A1,SUMF1,SUMF2,SUMO1,SUMO1P2,SUMO2,SUMO3,SUMO4,SUN1,SUN2,SUN3,SUN5,SUOX,SUPT16H,SUPT20H,SUPT3H,SUPT4H1,SUPT5H,SUPT6H,SUPT7L,SUPV3L1,SURF1,SURF2,SURF4,SURF6,SUSD1,SUSD2,SUSD3,SUSD4,SUSD5,SUSD6,SUV39H1,SUV39H2,SUZ12,SUZ12P1,SV2A,SV2B,SV2C,SVBP,SVEP1,SVIL,SVIP,SVOP,SVOPL,SWAP70,SWI5,SWSAP1,SWT1,SYAP1,SYBU,SYCE1,SYCE1L,SYCE2,SYCE3,SYCN,SYCP1,SYCP2,SYCP2L,SYCP3,SYDE1,SYDE2,SYF2,SYK,SYMPK,SYN1,SYN2,SYN3,SYNC,SYNCRIP,SYNDIG1,SYNDIG1L,SYNE1,SYNE2,SYNE3,SYNE4,SYNGAP1,SYNGR1,SYNGR2,SYNGR3,SYNGR4,SYNJ1,SYNJ2,SYNJ2BP,SYNM,SYNPO,SYNPO2,SYNPO2L,SYNPR,SYNPR-AS1,SYNRG,SYP,SYPL1,SYPL2,SYS1,SYT1,SYT11,SYT12,SYT13,SYT14,SYT15,SYT16,SYT17,SYT2,SYT3,SYT4,SYT5,SYT6,SYT7,SYT8,SYT9,SYTL1,SYTL2,SYTL3,SYTL4,SYTL5,SYVN1,SZRD1,SZT2,TAAR1,TAAR2,TAAR3P,TAAR5,TAAR8,TAAR9,TAB1,TAB2,TAB3,TAC1,TAC3,TAC4,TACC1,TACC2,TACC3,TACO1,TACR1,TACR2,TACR3,TACSTD2,TADA1,TADA2A,TADA2B,TADA3,TAF1,TAF10,TAF11,TAF12,TAF13,TAF15,TAF1A,TAF1A-AS1,TAF1B,TAF1C,TAF1D,TAF1L,TAF2,TAF3,TAF4,TAF4B,TAF5,TAF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diff --git a/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE117613.csv b/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE117613.csv new file mode 100644 index 0000000000000000000000000000000000000000..05628bc952a49cc412f1e1e98af5b1fd47bfbdb2 --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE117613.csv @@ -0,0 +1,4 @@ +,GSM3305150,GSM3305151,GSM3305152,GSM3305153,GSM3305154,GSM3305155,GSM3305156,GSM3305157,GSM3305158,GSM3305159,GSM3305160,GSM3305161,GSM3305162,GSM3305163,GSM3305164,GSM3305165,GSM3305166,GSM3305167,GSM3305168,GSM3305169,GSM3305170,GSM3305171,GSM3305172,GSM3305173,GSM3305174,GSM3305175,GSM3305176,GSM3305177,GSM3305178,GSM3305179,GSM3305180,GSM3305181,GSM3305182,GSM3305183,GSM3305184,GSM3305185,GSM3305186,GSM3305187,GSM3305188,GSM3305189,GSM3305190,GSM3305191,GSM3305192,GSM3305193,GSM3305194,GSM3305195 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-0,0 +1,2 @@ +,GSM1293592,GSM1293593,GSM1293594,GSM1293595,GSM1293596,GSM1293597,GSM1293598,GSM1293599,GSM1293600,GSM1293601,GSM1293602,GSM1293603,GSM1293604,GSM1293605,GSM1293606,GSM1293607,GSM1293608,GSM1293609,GSM1293610,GSM1293611,GSM1293612,GSM1293613,GSM1293614,GSM1293615,GSM1293616,GSM1293617,GSM1293618,GSM1293619,GSM1293620,GSM1293621,GSM1293622,GSM1293623,GSM1293624,GSM1293625 +Sickle_Cell_Anemia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84632.csv b/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84632.csv new file mode 100644 index 0000000000000000000000000000000000000000..143c52892d77fe3a6f12490044dda2bd61ad1ece --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84632.csv @@ -0,0 +1,2 @@ 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+Sickle_Cell_Anemia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 diff --git a/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84633.csv b/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84633.csv new file mode 100644 index 0000000000000000000000000000000000000000..7e49bdaf645bd7de985ee8946a1837d5d578660f --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84633.csv @@ -0,0 +1,2 @@ +,GSM2243302,GSM2243303,GSM2243304,GSM2243305,GSM2243306,GSM2243307,GSM2243308,GSM2243309,GSM2243310,GSM2243311,GSM2243312,GSM2243313,GSM2243314,GSM2243315,GSM2243316,GSM2243317,GSM2243318,GSM2243319,GSM2243320,GSM2243321,GSM2243322,GSM2243323,GSM2243324,GSM2243325,GSM2243326,GSM2243327,GSM2243328,GSM2243329,GSM2243330,GSM2243331,GSM2243332,GSM2243333,GSM2243334,GSM2243335,GSM2243336,GSM2243337,GSM2243338,GSM2243339,GSM2243340,GSM2243341 +Sickle_Cell_Anemia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 diff --git a/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84634.csv b/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84634.csv new file mode 100644 index 0000000000000000000000000000000000000000..9c2d3450ef6015856f1233c971f26270f0e8cf1a --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84634.csv @@ -0,0 +1,2 @@ +,GSM2243346,GSM2243347,GSM2243348,GSM2243349,GSM2243350,GSM2243351,GSM2243352,GSM2243353,GSM2243354,GSM2243355,GSM2243356,GSM2243357,GSM2243358,GSM2243359,GSM2243360,GSM2243361,GSM2243362,GSM2243363,GSM2243364,GSM2243365,GSM2243366,GSM2243367,GSM2243368,GSM2243369,GSM2243370,GSM2243371,GSM2243372,GSM2243373,GSM2243374,GSM2243375,GSM2243376,GSM2243377,GSM2243378,GSM2243379,GSM2243380,GSM2243381,GSM2243382,GSM2243383 +Sickle_Cell_Anemia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 diff --git a/p3/preprocess/Sickle_Cell_Anemia/code/GSE117613.py b/p3/preprocess/Sickle_Cell_Anemia/code/GSE117613.py new file mode 100644 index 0000000000000000000000000000000000000000..3c665e95197fef99d0d3f3e779d4e154d9b9a72f --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/code/GSE117613.py @@ -0,0 +1,178 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Sickle_Cell_Anemia" +cohort = "GSE117613" + +# Input paths +in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia" +in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE117613" + +# Output paths +out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE117613.csv" +out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE117613.csv" +out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE117613.csv" +json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# From background info, we see the study used "Illumina HumanHT-12 v4 Expression BeadChip" +# This indicates gene expression data is available +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Find key numbers for variables +# Diagnosis status in row 0 indicates trait info +trait_row = 0 + +# Age info in row 2 +age_row = 2 + +# Sex/gender info in row 4 +gender_row = 4 + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> int: + """Convert diagnosis status to binary trait indicator.""" + if not value or 'diagnosis:' not in value: + return None + value = value.lower().split('diagnosis:')[1].strip() + # From background, we know this is a malaria study. Control group has no infection. + if 'no plasmodium falciparum infection' in value: + return 0 + elif 'severe malaral anemia' in value or 'cerebral milaria' in value: + return 1 + return None + +def convert_age(value: str) -> float: + """Convert age string to float years.""" + if not value or 'age:' not in value: + return None + try: + return float(value.split('age:')[1].strip()) + except: + return None + +def convert_gender(value: str) -> int: + """Convert sex/gender string to binary (0=female, 1=male).""" + if not value or 'Sex:' not in value: + return None + value = value.lower().split('sex:')[1].strip() + if value == 'female': + return 0 + elif value == 'male': + return 1 + return None + +# 3. Save initial metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Extract clinical features if available +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the data + print("Preview of clinical features:") + print(preview_df(clinical_features)) + + # Save to CSV + clinical_features.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The IDs start with "ILMN_" which indicates these are Illumina probe IDs +# These need to be mapped to human gene symbols for analysis +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Preview column names and values from annotation dataframe +print("Gene annotation DataFrame preview:") +print(preview_df(gene_annotation)) +# Get mapping dataframe with probe IDs and gene symbols +mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') + +# Convert probe-level data to gene-level expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Save gene expression data +gene_data.to_csv(out_gene_data_file) +# 1. Normalize gene symbols in gene expression data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) +print("\nGene data shape (normalized gene-level):", gene_data.shape) + +# Load clinical data previously processed +selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) +print("\nClinical data shape:", selected_clinical_df.shape) + +# 2. Link clinical and genetic data using normalized gene-level data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) +print("\nLinked data shape:", linked_data.shape) + +# 3. Handle missing values systematically +if trait in linked_data.columns: + linked_data = handle_missing_values(linked_data, trait) + + # 4. Check for bias in trait and demographic features + trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + + # 5. Final validation and information saving + note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database." + is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note + ) + + # 6. Save linked data only if usable and not biased + if is_usable and not trait_biased: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Sickle_Cell_Anemia/code/GSE41575.py b/p3/preprocess/Sickle_Cell_Anemia/code/GSE41575.py new file mode 100644 index 0000000000000000000000000000000000000000..beb135e01403ad4950765db0cdc7354596365211 --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/code/GSE41575.py @@ -0,0 +1,66 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Sickle_Cell_Anemia" +cohort = "GSE41575" + +# Input paths +in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia" +in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE41575" + +# Output paths +out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE41575.csv" +out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE41575.csv" +out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE41575.csv" +json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# This is microRNA data, not gene expression data +is_gene_available = False + +# 2.1 Data Availability +# No trait status data - only control vs miR overexpression +trait_row = None + +# No age data available +age_row = None + +# No gender data available +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + # Not needed since trait data not available + return None + +def convert_age(x): + # Not needed since age data not available + return None + +def convert_gender(x): + # Not needed since gender data not available + return None + +# 3. Save metadata +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=False) + +# 4. Skip clinical feature extraction since trait_row is None \ No newline at end of file diff --git a/p3/preprocess/Sickle_Cell_Anemia/code/GSE46471.py b/p3/preprocess/Sickle_Cell_Anemia/code/GSE46471.py new file mode 100644 index 0000000000000000000000000000000000000000..6067cf91fdc5604b126329ed19b94b14dc29da73 --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/code/GSE46471.py @@ -0,0 +1,130 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Sickle_Cell_Anemia" +cohort = "GSE46471" + +# Input paths +in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia" +in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE46471" + +# Output paths +out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE46471.csv" +out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE46471.csv" +out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE46471.csv" +json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Yes, this is a microarray gene expression dataset studying endothelial cells +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# For this specific dataset studying damage pathways in endothelial cells, +# all samples are control samples (no disease state), so trait data is not available +trait_row = None + +# No age data available in sample characteristics +age_row = None + +# No gender data available in sample characteristics +gender_row = None + +# 2.2 Data Type Conversion +def convert_trait(x): + # Not needed since trait data is not available + return None + +def convert_age(x): + # Not needed since age data is not available + return None + +def convert_gender(x): + # Not needed since gender data is not available + return None + +# 3. Save Metadata +# Initial filtering - since trait data is not available (trait_row is None), +# is_trait_available should be False +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=False +) + +# 4. Clinical Feature Extraction +# Skip this step since trait_row is None, indicating clinical data is not available +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The gene identifiers in the data appear to be just numeric values (1,2,3...), +# which are not standard human gene symbols. We need to map these to proper gene symbols. +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Preview column names and values from annotation dataframe +print("Gene annotation DataFrame preview:") +print(preview_df(gene_annotation)) +# Extract gene identifiers and gene symbols from the annotation data +id_col = 'ID' # Gene identifiers in the data are numeric values (1,2,3...) +gene_col = 'GENE_SYMBOL' # Gene symbols are stored in GENE_SYMBOL column + +# Get mapping between gene identifiers and gene symbols +mapping_data = get_gene_mapping(gene_annotation, id_col, gene_col) + +# Convert probe-level measurements to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview the gene data after mapping +print("\nGene expression data after mapping:") +print(gene_data.shape) +print("\nFirst few genes and their expression values:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols in gene expression data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) +print("\nGene data shape (normalized gene-level):", gene_data.shape) + +# Save metadata indicating dataset is not usable due to lack of trait data +note = "Dataset contains normalized gene expression data but lacks trait information." +validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, + is_biased=True, # Set to True since no trait data makes it unusable + df=gene_data, # Provide the gene expression data + note=note +) \ No newline at end of file diff --git a/p3/preprocess/Sickle_Cell_Anemia/code/GSE53441.py b/p3/preprocess/Sickle_Cell_Anemia/code/GSE53441.py new file mode 100644 index 0000000000000000000000000000000000000000..d8b2a9b15211cc54d33c788923673c53763c2b85 --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/code/GSE53441.py @@ -0,0 +1,168 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Sickle_Cell_Anemia" +cohort = "GSE53441" + +# Input paths +in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia" +in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE53441" + +# Output paths +out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE53441.csv" +out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE53441.csv" +out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE53441.csv" +json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# This dataset uses Affymetrix Human Genome U133 Plus 2.0 array for expression profiling +# So it contains gene expression data +is_gene_available = True + +# 2.1 Data Availability +# For trait (SCA vs normal), data is in row 0 +trait_row = 0 + +# No age data available +age_row = None + +# No gender data available +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> int: + # Extract value after colon and remove whitespace + if ':' in value: + value = value.split(':')[1].strip().lower() + else: + value = value.strip().lower() + + if 'sickle cell' in value or 'sca' in value: + return 1 + elif 'normal' in value: + return 0 + return None + +def convert_age(value: str) -> float: + return None + +def convert_gender(value: str) -> int: + return None + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the processed clinical data + print("Preview of clinical features:") + print(preview_df(clinical_features)) + + # Save clinical features + clinical_features.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# Based on the identifiers (e.g. '1007_s_at'), these are Affymetrix probe IDs +# from a microarray platform rather than standard human gene symbols. +# They need to be mapped to gene symbols for analysis. +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Preview column names and values from annotation dataframe +print("Gene annotation DataFrame preview:") +print(preview_df(gene_annotation)) +# 1. In the gene expression data, the identifiers (e.g. '1007_s_at') are stored in 'ID' column of gene annotation +# In gene annotation data, gene symbols are stored in 'Gene Symbol' column + +# 2. Get gene mapping dataframe +mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol') + +# 3. Apply mapping to convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +print("\nShape of processed gene data:", gene_data.shape) +print("\nFirst 5 rows of mapped gene data:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols in gene expression data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) +print("\nGene data shape (normalized gene-level):", gene_data.shape) + +# Load clinical data previously processed +selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) +print("\nClinical data shape:", selected_clinical_df.shape) + +# 2. Link clinical and genetic data using normalized gene-level data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) +print("\nLinked data shape:", linked_data.shape) + +# 3. Handle missing values systematically +if trait in linked_data.columns: + linked_data = handle_missing_values(linked_data, trait) + + # 4. Check for bias in trait and demographic features + trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + + # 5. Final validation and information saving + note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database." + is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note + ) + + # 6. Save linked data only if usable and not biased + if is_usable and not trait_biased: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Sickle_Cell_Anemia/code/GSE84632.py b/p3/preprocess/Sickle_Cell_Anemia/code/GSE84632.py new file mode 100644 index 0000000000000000000000000000000000000000..5d63c96300b26a5f68520162aaa5a7b5594c6618 --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/code/GSE84632.py @@ -0,0 +1,147 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Sickle_Cell_Anemia" +cohort = "GSE84632" + +# Input paths +in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia" +in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE84632" + +# Output paths +out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE84632.csv" +out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE84632.csv" +out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE84632.csv" +json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +is_gene_available = True # The title and summary indicate this is gene expression data from PBMCs + +# 2.1 Data Availability +trait_row = 2 # Disease status in row 2 +age_row = None # Age not provided +gender_row = None # Gender not provided + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + """Convert trait information to binary (0: Control, 1: Case)""" + if value is None or ':' not in value: + return None + value = value.split(':')[1].strip().lower() + if 'sickle cell disease' in value: + return 1 + return None + +def convert_age(value): + """Convert age to float""" + return None # Not used since age data not available + +def convert_gender(value): + """Convert gender to binary (0: Female, 1: Male)""" + return None # Not used since gender data not available + +# 3. Save Metadata +is_trait_available = trait_row is not None +_ = validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + selected_clinical = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the processed clinical data + print("Preview of processed clinical data:") + print(preview_df(selected_clinical)) + + # Save to CSV + selected_clinical.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The gene identifiers appear to be array probe IDs (16650001, etc) rather than human gene symbols +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Preview the dataframe by looking at column names and first few values +print("Gene annotation columns and first few rows:") +for col in gene_annotation.columns: + print(f"\n{col}:") + print(list(gene_annotation[col])[:5]) +# Step 1: Extract platform table data from SOFT file - look between !Platform_table_begin and !Platform_table_end +platform_table = filter_content_by_prefix( + soft_file_path, + prefixes_a=['!Platform_table_begin', '!Platform_table_end'], + unselect=True, + source_type='file', + return_df_a=True +)[0] + +# Look at column names to identify probe ID and gene symbol columns +print("Platform table columns:") +print(platform_table.columns) + +# Review some sample rows to confirm data format +print("\nSample rows:") +print(platform_table.head()) + +# Create mapping using probe ID and gene symbol columns +mapping_df = get_gene_mapping(platform_table, 'ID', 'gene_assignment') + +# Apply mapping to convert probe data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Normalize gene symbols to standardized format +gene_data = normalize_gene_symbols_in_index(gene_data) + +print("\nShape of gene expression data after mapping:") +print(gene_data.shape) +print("\nFirst few gene symbols:") +print(list(gene_data.index)[:10]) +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Preview column names and values from annotation dataframe +print("Gene annotation DataFrame preview:") +print(preview_df(gene_annotation)) \ No newline at end of file diff --git a/p3/preprocess/Sickle_Cell_Anemia/code/GSE84633.py b/p3/preprocess/Sickle_Cell_Anemia/code/GSE84633.py new file mode 100644 index 0000000000000000000000000000000000000000..6ff0c944077f6495f1ac69e85fb56d191a2e6156 --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/code/GSE84633.py @@ -0,0 +1,157 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Sickle_Cell_Anemia" +cohort = "GSE84633" + +# Input paths +in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia" +in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE84633" + +# Output paths +out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE84633.csv" +out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE84633.csv" +out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE84633.csv" +json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data +is_gene_available = True # Study title indicates gene expression data from PBMCs + +# 2. Variable Availability and Conversion +# Trait: Available from disease field +trait_row = 2 # Key for disease status +def convert_trait(val): + if val is None: + return None + val = val.split(': ')[-1].lower() + if 'sickle cell disease' in val: + return 1 + return None + +# Age: Not available +age_row = None +convert_age = None + +# Gender: Not available +gender_row = None +convert_gender = None + +# 3. Save metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Extract clinical features +if trait_row is not None: + clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + print("Preview of clinical data:") + print(preview_df(clinical_df)) + + # Save clinical data + clinical_df.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The identifiers appear to be Illumina probe IDs starting with '23' +# These are not human gene symbols and need to be mapped to official gene symbols +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Preview column names and values from annotation dataframe +print("Gene annotation DataFrame preview:") +print(preview_df(gene_annotation)) +# Extract gene mapping info +# 'ID' column matches probe IDs in expression data +# 'gene_assignment' contains gene symbols in format "RefSeq // GeneSymbol // Description" +mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment') + +# Extract standard gene symbols +mapping_df['Gene'] = mapping_df['Gene'].apply(lambda x: x.split('//')[1].strip() if isinstance(x, str) and '//' in x else None) + +# Apply mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview the mapped gene data +print("\nGene expression data after mapping:") +print("Shape:", gene_data.shape) +print("\nFirst few gene symbols:") +print(list(gene_data.index)[:10]) +print("\nSample of expression values:") +print(gene_data.head()) +# 1. Normalize gene symbols in gene expression data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) +print("\nGene data shape (normalized gene-level):", gene_data.shape) + +# Load clinical data previously processed +selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) +print("\nClinical data shape:", selected_clinical_df.shape) + +# 2. Link clinical and genetic data using normalized gene-level data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) +print("\nLinked data shape:", linked_data.shape) + +# 3. Handle missing values systematically +if trait in linked_data.columns: + linked_data = handle_missing_values(linked_data, trait) + + # 4. Check for bias in trait and demographic features + trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + + # 5. Final validation and information saving + note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database." + is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note + ) + + # 6. Save linked data only if usable and not biased + if is_usable and not trait_biased: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Sickle_Cell_Anemia/code/GSE84634.py b/p3/preprocess/Sickle_Cell_Anemia/code/GSE84634.py new file mode 100644 index 0000000000000000000000000000000000000000..7808a039a80c080325a1a5007282d49c3ccd2e5a --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/code/GSE84634.py @@ -0,0 +1,164 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Sickle_Cell_Anemia" +cohort = "GSE84634" + +# Input paths +in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia" +in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE84634" + +# Output paths +out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE84634.csv" +out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE84634.csv" +out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE84634.csv" +json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Dataset is about gene expression in PBMCs, so gene data should be available +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# trait_row = 2 since disease status is recorded in row 2 +# age and gender not recorded in characteristics +trait_row = 2 +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + # Binary conversion - SCD patients are cases (1), non-SCD would be controls (0) + # But here all subjects are SCD patients based on background info + if x and 'sickle cell disease' in x.lower(): + return 1 + return None + +def convert_age(x): + # Not available + return None + +def convert_gender(x): + # Not available + return None + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_df = geo_select_clinical_features(clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender) + + print("Preview of extracted clinical features:") + print(preview_df(clinical_df)) + + # Save clinical data + clinical_df.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# Looking at the identifiers like '2315554', '2315633', these appear to be probe IDs +# rather than human gene symbols. Gene symbols would typically be like 'BRCA1', 'TP53' etc. +# Therefore these IDs need to be mapped to gene symbols. + +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Preview column names and values from annotation dataframe +print("Gene annotation DataFrame preview:") +print(preview_df(gene_annotation)) +# 1. The 'ID' column in annotation matches probe IDs in expression data +# The 'gene_assignment' column contains gene symbols in format "symbol // description" +prob_col = 'ID' +gene_col = 'gene_assignment' + +# 2. Get mapping between probe IDs and gene symbols +mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col) + +# 3. Apply mapping to convert probe data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview result +print("Gene expression data shape:", gene_data.shape) +print("\nFirst few gene symbols:") +print(list(gene_data.index)[:10]) +print("\nFirst few expression values:") +print(gene_data.head()) +# 1. Normalize gene symbols in gene expression data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) +print("\nGene data shape (normalized gene-level):", gene_data.shape) + +# Load clinical data previously processed +selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) +print("\nClinical data shape:", selected_clinical_df.shape) + +# 2. Link clinical and genetic data using normalized gene-level data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) +print("\nLinked data shape:", linked_data.shape) + +# 3. Handle missing values systematically +if trait in linked_data.columns: + linked_data = handle_missing_values(linked_data, trait) + + # 4. Check for bias in trait and demographic features + trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + + # 5. Final validation and information saving + note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database." + is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note + ) + + # 6. Save linked data only if usable and not biased + if is_usable and not trait_biased: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Sickle_Cell_Anemia/code/TCGA.py b/p3/preprocess/Sickle_Cell_Anemia/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..573068cd8ddcbdd7660365b228e1fdb0b534f206 --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/code/TCGA.py @@ -0,0 +1,114 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Sickle_Cell_Anemia" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json" + +# Select TCGA_Acute_Myeloid_Leukemia_(LAML) since it is the most relevant blood disorder dataset +cohort_dir = os.path.join(tcga_root_dir, "TCGA_Acute_Myeloid_Leukemia_(LAML)") + +# Get file paths for clinical and genetic data +clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir) + +# Load the data files +clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t') +genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t') + +# Print clinical columns for examination +print("Clinical data columns:") +print(clinical_df.columns.tolist()) +# Define candidate columns +candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] +candidate_gender_cols = ['gender'] + +# Get file paths using proper directory structure +cohort_dir = tcga_root_dir +clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir) + +# Read and preview clinical data +clinical_data = pd.read_csv(clinical_file_path, index_col=0) + +# Extract and preview age columns +age_preview = {} +for col in candidate_age_cols: + age_preview[col] = clinical_data[col].head(5).tolist() +print("Age columns preview:") +print(age_preview) + +# Extract and preview gender columns +gender_preview = {} +for col in candidate_gender_cols: + gender_preview[col] = clinical_data[col].head(5).tolist() +print("\nGender columns preview:") +print(gender_preview) +# Select TCGA_Acute_Myeloid_Leukemia_(LAML) since it is the most relevant blood disorder dataset +cohort_dir = os.path.join(tcga_root_dir, "TCGA_Acute_Myeloid_Leukemia_(LAML)") + +# Get file paths for clinical and genetic data +clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir) + +# Load the data files +clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t') +genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t') + +# Print clinical columns for examination +print("Clinical data columns:") +print(clinical_df.columns.tolist()) +# Inspect previously given clinical data columns +age_col = 'age_at_initial_pathologic_diagnosis' # Clear age column exists +gender_col = 'gender' # Clear gender column exists + +# Print the chosen column names +print(f"Selected age column: {age_col}") +print(f"Selected gender column: {gender_col}") +# Select appropriate demographic columns +age_col = 'age_at_initial_pathologic_diagnosis' # This is more directly usable than days_to_birth +gender_col = 'gender' + +# 1. Extract and standardize clinical features +selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col) + +# 2. Normalize gene symbols in genetic data +normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +normalized_genetic_df.to_csv(out_gene_data_file) + +# 3. Link clinical and genetic data +linked_data = pd.merge(selected_clinical_df, normalized_genetic_df.T, left_index=True, right_index=True) + +# 4. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 5. Check for bias in trait and demographic features +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 6. Validate and save cohort info +note = f"Sample size after preprocessing: {len(linked_data)}. Number of genes: {len(linked_data.columns) - 3}" +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort="TCGA", + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note=note +) + +# 7. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) + print(f"Linked data saved to {out_data_file}") + print("Shape of final linked data:", linked_data.shape) +else: + print("Dataset was found to be unusable and was not saved") \ No newline at end of file diff --git a/p3/preprocess/Sickle_Cell_Anemia/cohort_info.json b/p3/preprocess/Sickle_Cell_Anemia/cohort_info.json new file mode 100644 index 0000000000000000000000000000000000000000..8b29e4efccf59e9aeb11f3ef68c2718e3e8b56f8 --- /dev/null +++ b/p3/preprocess/Sickle_Cell_Anemia/cohort_info.json @@ -0,0 +1 @@ +{"GSE84634": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 38, "note": "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."}, "GSE84633": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 40, "note": "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."}, "GSE84632": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE53441": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 34, "note": "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."}, "GSE46471": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset contains normalized gene expression data but lacks trait information."}, "GSE41575": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE117613": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 46, "note": "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 173, "note": "Sample size after preprocessing: 173. 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