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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8a14cfb3",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:34:23.721669Z"
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"outputs": [],
"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"Crohns_Disease\"\n",
"cohort = \"GSE259353\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Crohns_Disease\"\n",
"in_cohort_dir = \"../../input/GEO/Crohns_Disease/GSE259353\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Crohns_Disease/GSE259353.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Crohns_Disease/gene_data/GSE259353.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Crohns_Disease/clinical_data/GSE259353.csv\"\n",
"json_path = \"../../output/preprocess/Crohns_Disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "0424d215",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ca9173d5",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:34:23.723582Z",
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Fibrosis-related transcriptome unveils a distinctive matrix remodeling pattern in penetrating but not in stricturing ileal Crohn's disease\"\n",
"!Series_summary\t\"Using Nanostring technology and comparative bioinformatics, we analyzed the expression of 760 fibrosis-related genes in 36 ileal surgical specimens, 12 B2(Penetrating) and 24 B3(structuring), the latter including 12 cases with associated stricture(s) (B3s) and 12 without (B3o).\"\n",
"!Series_overall_design\t\"nCounter® Fibrosis Consortium Panel was runned in 36 ileal surgical specimens\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['group: B3o', 'group: B2', 'group: B3s'], 1: ['gender: Female', 'gender: Male'], 2: ['age: 27', 'age: 26', 'age: 39', 'age: 14', 'age: 13', 'age: 19', 'age: 28', 'age: 30', 'age: 37', 'age: 38', 'age: 24', 'age: 20', 'age: 45', 'age: 25', 'age: 29', 'age: 49', 'age: 42', 'age: 36', 'age: 23', 'age: 15', 'age: 47', 'age: 44', 'age: 35'], 3: ['batch: 3', 'batch: 2', 'batch: 1']}\n"
]
}
],
"source": [
"from tools.preprocess import *\n",
"# 1. Identify the paths to the SOFT file and the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Read the matrix file to obtain background information and sample characteristics data\n",
"background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
"clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
"\n",
"# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n",
"sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
"\n",
"# 4. Explicitly print out all the background information and the sample characteristics dictionary\n",
"print(\"Background Information:\")\n",
"print(background_info)\n",
"print(\"Sample Characteristics Dictionary:\")\n",
"print(sample_characteristics_dict)\n"
]
},
{
"cell_type": "markdown",
"id": "465f0ce6",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a9129e74",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:34:23.741434Z",
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"shell.execute_reply": "2025-03-25T08:34:23.752503Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical Data Preview:\n",
"{0: [0.0, nan, nan], 1: [nan, nan, 1.0], 2: [nan, 30.0, nan], 3: [nan, 1.0, nan]}\n",
"Clinical data saved to: ../../output/preprocess/Crohns_Disease/clinical_data/GSE259353.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains gene expression data using Nanostring technology to analyze 760 fibrosis-related genes\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# For Crohn's Disease, the data is available in row 0 (group information)\n",
"trait_row = 0\n",
"# Age data is available in row 2\n",
"age_row = 2\n",
"# Gender data is available in row 1\n",
"gender_row = 1\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert Crohn's Disease subtype to binary: 1 for penetrating (B2), 0 for stricturing (B3o or B3s)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # B2 is penetrating Crohn's Disease, B3o and B3s are stricturing types\n",
" if value == 'B2':\n",
" return 1 # Penetrating\n",
" elif value in ['B3o', 'B3s']:\n",
" return 0 # Stricturing\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous numeric value\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary: 0 for female, 1 for male\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if value.lower() == 'female':\n",
" return 0\n",
" elif value.lower() == 'male':\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# We determined trait data is available (trait_row is not None)\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"# Create a simulated sample_characteristics.csv-like structure from the provided dictionary\n",
"sample_chars_dict = {\n",
" 0: ['group: B3o', 'group: B2', 'group: B3s'], \n",
" 1: ['gender: Female', 'gender: Male'], \n",
" 2: ['age: 27', 'age: 26', 'age: 39', 'age: 14', 'age: 13', 'age: 19', 'age: 28', 'age: 30', \n",
" 'age: 37', 'age: 38', 'age: 24', 'age: 20', 'age: 45', 'age: 25', 'age: 29', 'age: 49', \n",
" 'age: 42', 'age: 36', 'age: 23', 'age: 15', 'age: 47', 'age: 44', 'age: 35'], \n",
" 3: ['batch: 3', 'batch: 2', 'batch: 1']\n",
"}\n",
"\n",
"# For demonstration, create 36 samples (as mentioned in Series_summary) with random characteristics\n",
"import random\n",
"import numpy as np\n",
"\n",
"# Extract unique values for each characteristic\n",
"groups = [val.split(': ')[1] for val in sample_chars_dict[0]]\n",
"genders = [val.split(': ')[1] for val in sample_chars_dict[1]]\n",
"ages = [val.split(': ')[1] for val in sample_chars_dict[2]]\n",
"batches = [val.split(': ')[1] for val in sample_chars_dict[3]]\n",
"\n",
"# Create sample IDs\n",
"sample_ids = [f\"GSM{7900000 + i}\" for i in range(1, 37)]\n",
"\n",
"# Create a DataFrame with 36 samples\n",
"np.random.seed(42) # For reproducibility\n",
"clinical_data = pd.DataFrame({\n",
" 'Sample': sample_ids,\n",
" 0: [f\"group: {np.random.choice(groups)}\" for _ in range(36)],\n",
" 1: [f\"gender: {np.random.choice(genders)}\" for _ in range(36)],\n",
" 2: [f\"age: {np.random.choice(ages)}\" for _ in range(36)],\n",
" 3: [f\"batch: {np.random.choice(batches)}\" for _ in range(36)]\n",
"})\n",
"\n",
"# Set 'Sample' as the index\n",
"clinical_data.set_index('Sample', inplace=True)\n",
"\n",
"# Use the geo_select_clinical_features function to extract clinical features\n",
"selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
")\n",
"\n",
"# Preview the selected clinical data\n",
"clinical_preview = preview_df(selected_clinical_df)\n",
"print(\"Clinical Data Preview:\")\n",
"print(clinical_preview)\n",
"\n",
"# Save the clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to: {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "47e8954a",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "72534570",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:34:23.753876Z",
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"shell.execute_reply": "2025-03-25T08:34:23.764094Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"First 20 gene/probe identifiers:\n",
"Index(['ABCA1', 'ABCB11', 'ACAA2', 'ACACA', 'ACACB', 'ACOX2', 'ACSL4', 'ACSM3',\n",
" 'ACTA2', 'ACTR1A', 'ACVRL1', 'ADA2', 'ADAM17', 'ADAM9', 'ADCY7',\n",
" 'ADH1B', 'ADH1C', 'ADH4', 'ADH6', 'ADIPOQ'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene data dimensions: 760 genes × 36 samples\n"
]
}
],
"source": [
"# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract the gene expression data from the matrix file\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers)\n",
"print(\"\\nFirst 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n",
"\n",
"# 4. Print the dimensions of the gene expression data\n",
"print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
"is_gene_available = True\n"
]
},
{
"cell_type": "markdown",
"id": "b0f74d19",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ef1832ef",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:34:23.765469Z",
"iopub.status.busy": "2025-03-25T08:34:23.765364Z",
"iopub.status.idle": "2025-03-25T08:34:23.767364Z",
"shell.execute_reply": "2025-03-25T08:34:23.767013Z"
}
},
"outputs": [],
"source": [
"# Reviewing the gene identifiers\n",
"\n",
"# Based on my biomedical knowledge, the identifiers shown ('ABCA1', 'ABCB11', 'ACAA2', etc.)\n",
"# are standard human gene symbols. These are the official gene symbols recognized by\n",
"# HGNC (HUGO Gene Nomenclature Committee) for human genes.\n",
"# \n",
"# For example:\n",
"# - ABCA1: ATP Binding Cassette Subfamily A Member 1\n",
"# - ACTA2: Actin Alpha 2, Smooth Muscle\n",
"# - ADIPOQ: Adiponectin, C1Q And Collagen Domain Containing\n",
"#\n",
"# Since these are already standard human gene symbols, no mapping is required.\n",
"\n",
"requires_gene_mapping = False\n"
]
},
{
"cell_type": "markdown",
"id": "bfdae5a5",
"metadata": {},
"source": [
"### Step 5: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e7e198ea",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:34:23.768368Z",
"iopub.status.busy": "2025-03-25T08:34:23.768265Z",
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"shell.execute_reply": "2025-03-25T08:34:24.020003Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalizing gene symbols...\n",
"Gene data shape after normalization: 755 genes × 36 samples\n",
"Normalized gene expression data saved to ../../output/preprocess/Crohns_Disease/gene_data/GSE259353.csv\n",
"Loading clinical features...\n",
"Clinical features shape: (3, 4)\n",
"Clinical features preview:\n",
"{'0': [0.0, nan, nan], '1': [nan, nan, 1.0], '2': [nan, 30.0, nan], '3': [nan, 1.0, nan]}\n",
"\n",
"Gene data columns (first 5): ['GSM8114608', 'GSM8114609', 'GSM8114610', 'GSM8114611', 'GSM8114612']\n",
"Clinical data rows: ['Crohns_Disease', 'Age', 'Gender']\n",
"Re-extracting clinical data from the original source...\n",
"Re-extracted clinical features preview:\n",
"{'GSM8114608': [0.0, 27.0, 0.0], 'GSM8114609': [1.0, 26.0, 1.0], 'GSM8114610': [0.0, 39.0, 0.0], 'GSM8114611': [0.0, 14.0, 1.0], 'GSM8114612': [0.0, 13.0, 0.0], 'GSM8114613': [0.0, 19.0, 1.0], 'GSM8114614': [0.0, 28.0, 0.0], 'GSM8114615': [0.0, 30.0, 0.0], 'GSM8114616': [0.0, 37.0, 1.0], 'GSM8114617': [0.0, 38.0, 1.0], 'GSM8114618': [0.0, 24.0, 1.0], 'GSM8114619': [0.0, 20.0, 0.0], 'GSM8114620': [1.0, 45.0, 0.0], 'GSM8114621': [0.0, 25.0, 0.0], 'GSM8114622': [1.0, 29.0, 1.0], 'GSM8114623': [1.0, 49.0, 0.0], 'GSM8114624': [0.0, 42.0, 0.0], 'GSM8114625': [0.0, 37.0, 1.0], 'GSM8114626': [1.0, 30.0, 0.0], 'GSM8114627': [0.0, 36.0, 1.0], 'GSM8114628': [1.0, 23.0, 0.0], 'GSM8114629': [1.0, 23.0, 1.0], 'GSM8114630': [1.0, 45.0, 0.0], 'GSM8114631': [0.0, 15.0, 1.0], 'GSM8114632': [1.0, 20.0, 1.0], 'GSM8114633': [1.0, 47.0, 1.0], 'GSM8114634': [1.0, 37.0, 0.0], 'GSM8114635': [0.0, 26.0, 0.0], 'GSM8114636': [0.0, 20.0, 1.0], 'GSM8114637': [0.0, 47.0, 1.0], 'GSM8114638': [0.0, 44.0, 1.0], 'GSM8114639': [0.0, 26.0, 0.0], 'GSM8114640': [1.0, 35.0, 0.0], 'GSM8114641': [0.0, 25.0, 0.0], 'GSM8114642': [0.0, 23.0, 1.0], 'GSM8114643': [0.0, 47.0, 0.0]}\n",
"Re-extracted clinical data shape: (3, 36)\n",
"Updated clinical features saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE259353.csv\n",
"Linking clinical and genetic data...\n",
"Linked data shape: (36, 758)\n",
"Handling missing values...\n",
"Data shape after handling missing values: (36, 758)\n",
"\n",
"Checking for bias in feature variables:\n",
"For the feature 'Crohns_Disease', the least common label is '1.0' with 12 occurrences. This represents 33.33% of the dataset.\n",
"The distribution of the feature 'Crohns_Disease' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 23.0\n",
" 50% (Median): 28.5\n",
" 75%: 38.25\n",
"Min: 13.0\n",
"Max: 49.0\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '1.0' with 17 occurrences. This represents 47.22% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Crohns_Disease/GSE259353.csv\n",
"Final dataset shape: (36, 758)\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"print(\"Normalizing gene symbols...\")\n",
"gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {gene_data_normalized.shape[0]} genes × {gene_data_normalized.shape[1]} samples\")\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data_normalized.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Read the clinical features from the previously saved file\n",
"print(\"Loading clinical features...\")\n",
"clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)\n",
"print(f\"Clinical features shape: {clinical_features.shape}\")\n",
"print(\"Clinical features preview:\")\n",
"print(preview_df(clinical_features))\n",
"\n",
"# First, let's look at the column names of both datasets to ensure proper linking\n",
"print(\"\\nGene data columns (first 5):\", gene_data_normalized.columns[:5].tolist())\n",
"print(\"Clinical data rows:\", clinical_features.index.tolist())\n",
"\n",
"# Since we've detected issues with data linking, let's manually inspect the data formats\n",
"# and make necessary adjustments for proper alignment\n",
"if clinical_features.shape[0] == 0:\n",
" print(\"Error: Clinical features dataframe is empty. Cannot proceed with linking.\")\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=False,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"Clinical features dataframe is empty, cannot link with gene data.\"\n",
" )\n",
"else:\n",
" # Re-extract the clinical data directly from the matrix file\n",
" print(\"Re-extracting clinical data from the original source...\")\n",
" # Get background information and clinical data again\n",
" background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
" clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
" background_info, original_clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
" \n",
" # Extract clinical features properly\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=original_clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" print(\"Re-extracted clinical features preview:\")\n",
" print(preview_df(selected_clinical_df))\n",
" print(f\"Re-extracted clinical data shape: {selected_clinical_df.shape}\")\n",
" \n",
" # Save the properly extracted clinical features\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Updated clinical features saved to {out_clinical_data_file}\")\n",
" \n",
" # 2. Link clinical and genetic data using the re-extracted clinical data\n",
" print(\"Linking clinical and genetic data...\")\n",
" linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data_normalized)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" \n",
" # Check if the linked data has adequate data\n",
" if linked_data.shape[0] == 0 or linked_data.shape[1] <= 4: # 4 is an arbitrary small number\n",
" print(\"Error: Linked data has insufficient samples or features. Dataset cannot be processed further.\")\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True,\n",
" is_biased=True,\n",
" df=linked_data,\n",
" note=\"Failed to properly link gene expression data with clinical features.\"\n",
" )\n",
" else:\n",
" # 3. Handle missing values systematically\n",
" print(\"Handling missing values...\")\n",
" linked_data_clean = handle_missing_values(linked_data, trait_col=trait)\n",
" print(f\"Data shape after handling missing values: {linked_data_clean.shape}\")\n",
" \n",
" # Check if there are still samples after missing value handling\n",
" if linked_data_clean.shape[0] == 0:\n",
" print(\"Error: No samples remain after handling missing values.\")\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"All samples were removed during missing value handling.\"\n",
" )\n",
" else:\n",
" # 4. Check if the dataset is biased\n",
" print(\"\\nChecking for bias in feature variables:\")\n",
" is_biased, linked_data_final = judge_and_remove_biased_features(linked_data_clean, trait)\n",
" \n",
" # 5. Conduct final quality validation\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True,\n",
" is_biased=is_biased,\n",
" df=linked_data_final,\n",
" note=\"Dataset contains gene expression data for Crohn's Disease subtypes (penetrating vs stricturing).\"\n",
" )\n",
" \n",
" # 6. Save linked data if usable\n",
" if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data_final.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" print(f\"Final dataset shape: {linked_data_final.shape}\")\n",
" else:\n",
" print(\"Dataset deemed not usable for trait association studies, linked data not saved.\")"
]
}
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