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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "1aa6a6ba",
"metadata": {},
"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 = \"Fibromyalgia\"\n",
"cohort = \"GSE67311\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Fibromyalgia\"\n",
"in_cohort_dir = \"../../input/GEO/Fibromyalgia/GSE67311\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Fibromyalgia/GSE67311.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Fibromyalgia/gene_data/GSE67311.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Fibromyalgia/clinical_data/GSE67311.csv\"\n",
"json_path = \"../../output/preprocess/Fibromyalgia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "b551ca0c",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "763973a4",
"metadata": {},
"outputs": [],
"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": "7f86a7d2",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af673d54",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# From the background information, we can see that Affymetrix Human Gene arrays were used\n",
"# and gene expression analysis was performed, so gene expression data is available\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# For trait (Fibromyalgia)\n",
"# From sample characteristics, we see 'diagnosis' in key 0 \n",
"# with values 'healthy control' and 'fibromyalgia'\n",
"trait_row = 0\n",
"\n",
"# For age - There is no age information in the sample characteristics\n",
"age_row = None\n",
"\n",
"# For gender - There is no gender information in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"\n",
"# Function to convert trait values\n",
"def convert_trait(value):\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" # Convert to binary (0 for control, 1 for fibromyalgia)\n",
" if value == 'fibromyalgia':\n",
" return 1\n",
" elif value == 'healthy control':\n",
" return 0\n",
" return None\n",
"\n",
"# Age conversion function (not used as age is not available)\n",
"def convert_age(value):\n",
" return None\n",
"\n",
"# Gender conversion function (not used as gender is not available)\n",
"def convert_gender(value):\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save initial filtering results\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",
"if trait_row is not None:\n",
" # Extract clinical features\n",
" # Create the clinical data DataFrame from the Sample Characteristics Dictionary provided earlier\n",
" sample_characteristics_dict = {\n",
" 0: ['diagnosis: healthy control', 'diagnosis: fibromyalgia'], \n",
" 1: ['tissue: peripheral blood'], \n",
" 2: ['fiqr score: 8.5', 'fiqr score: -2.0', 'fiqr score: 9.8', 'fiqr score: 0.5', 'fiqr score: -1.0', 'fiqr score: -0.5', 'fiqr score: 2.2', 'fiqr score: 15.3', 'fiqr score: 4.0', 'fiqr score: 29.3', 'fiqr score: 27.2', 'fiqr score: 5.0', 'fiqr score: 1.0', 'fiqr score: 2.5', 'fiqr score: 3.0', 'fiqr score: -1.5', 'fiqr score: 1.3', 'fiqr score: 21.7', 'fiqr score: -1.2', 'fiqr score: 4.3', 'fiqr score: 6.5', 'fiqr score: 2.0', 'fiqr score: 11.7', 'fiqr score: 15.0', 'fiqr score: 6.0', 'fiqr score: 14.2', 'fiqr score: -0.2', 'fiqr score: 12.8', 'fiqr score: 15.7', 'fiqr score: 0.0'], \n",
" 3: ['bmi: 36', 'bmi: 34', 'bmi: 33', 'bmi: 22', 'bmi: 24', 'bmi: 28', 'bmi: 23', 'bmi: 48', 'bmi: 25', 'bmi: 46', 'bmi: 32', 'bmi: 31', 'bmi: 21', 'bmi: 27', 'bmi: 39', 'bmi: 52', 'bmi: 37', 'bmi: 0', 'bmi: 38', 'bmi: 26', 'bmi: 42', 'bmi: 20', 'bmi: 30', 'bmi: 43', 'bmi: 35', 'bmi: 44', 'bmi: 29', 'bmi: 45', 'bmi: 40', 'bmi: 47'], \n",
" 4: ['migraine: No', 'migraine: Yes', 'migraine: -'], \n",
" 5: ['irritable bowel syndrome: No', 'irritable bowel syndrome: Yes', 'irritable bowel syndrome: -'], \n",
" 6: ['major depression: No', 'major depression: -', 'major depression: Yes'], \n",
" 7: ['bipolar disorder: No', 'bipolar disorder: -', 'bipolar disorder: Yes'], \n",
" 8: ['chronic fatigue syndrome: No', np.nan, 'chronic fatigue syndrome: -', 'chronic fatigue syndrome: Yes']\n",
" }\n",
" \n",
" clinical_data = pd.DataFrame({k: pd.Series(v) for k, v in sample_characteristics_dict.items()})\n",
" \n",
" clinical_features = 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 processed clinical data\n",
" print(\"Preview of clinical features:\")\n",
" print(preview_df(clinical_features))\n",
" \n",
" # Save the clinical data to CSV\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "92901783",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "19c1b740",
"metadata": {},
"outputs": [],
"source": [
"```python\n",
"# Let's examine whether the dataset contains the necessary information\n",
"print(\"Examination of GSE67311 dataset for Fibromyalgia study\")\n",
"\n",
"# First, let's check if the files exist\n",
"clinical_data_file = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
"meta_data_file = os.path.join(in_cohort_dir, \"meta_data.json\")\n",
"\n",
"# Initialize flags for data availability\n",
"is_gene_available = False\n",
"is_trait_available = False\n",
"\n",
"# Initialize variables\n",
"clinical_data = None\n",
"meta_data = {}\n",
"trait_row = None\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# Try to load clinical data\n",
"if os.path.exists(clinical_data_file):\n",
" clinical_data = pd.read_csv(clinical_data_file)\n",
" print(\"Clinical data shape:\", clinical_data.shape)\n",
" print(\"Clinical data columns:\", clinical_data.columns.tolist())\n",
" print(\"Sample of clinical data:\\n\", clinical_data.head())\n",
"else:\n",
" print(f\"Clinical data file not found at: {clinical_data_file}\")\n",
" print(\"Checking for alternative files in the directory...\")\n",
" \n",
" # Check if there are any CSV files in the directory\n",
" csv_files = [f for f in os.listdir(in_cohort_dir) if f.endswith('.csv')]\n",
" if csv_files:\n",
" print(f\"Found CSV files: {csv_files}\")\n",
" # Try the first CSV file\n",
" alternative_file = os.path.join(in_cohort_dir, csv_files[0])\n",
" try:\n",
" clinical_data = pd.read_csv(alternative_file)\n",
" print(f\"Loaded alternative clinical data from: {alternative_file}\")\n",
" print(\"Clinical data shape:\", clinical_data.shape)\n",
" print(\"Sample of clinical data:\\n\", clinical_data.head())\n",
" except Exception as e:\n",
" print(f\"Error loading alternative file: {e}\")\n",
" else:\n",
" print(\"No CSV files found in the directory.\")\n",
"\n",
"# Try to load meta data\n",
"if os.path.exists(meta_data_file):\n",
" with open(meta_data_file, 'r') as f:\n",
" meta_data = json.load(f)\n",
" print(\"Meta data keys:\", list(meta_data.keys()))\n",
" \n",
" if 'title' in meta_data:\n",
" print(\"Dataset title:\", meta_data.get('title'))\n",
" \n",
" if 'background' in meta_data:\n",
" print(\"Background information:\", meta_data.get('background'))\n",
" \n",
" # Check for gene expression data availability based on meta_data\n",
" if any(keyword in str(meta_data).lower() for keyword in \n",
" ['gene expression', 'mrna', 'transcriptome', 'gene profile']):\n",
" is_gene_available = True\n",
" \n",
" if 'sample_characteristics' in meta_data:\n",
" sample_chars = meta_data.get('sample_characteristics', {})\n",
" print(\"Sample characteristics keys:\", list(sample_chars.keys()))\n",
" \n",
" # Print the unique values for each key in sample characteristics\n",
" for key, values in sample_chars.items():\n",
" unique_values = set(values)\n",
" print(f\"Key {key} unique values:\", unique_values)\n",
" \n",
" # Check for trait, age, and gender data\n",
" if any('fibromyalgia' in str(v).lower() or 'fm' in str(v).lower() or trait.lower() in str(v).lower() \n",
" for v in unique_values):\n",
" trait_row = int(key)\n",
" is_trait_available = True\n",
" \n",
" if any('age' in str(v).lower() for v in unique_values):\n",
" age_row = int(key)\n",
" \n",
" if any('gender' in str(v).lower() or 'sex' in str(v).lower() or \n",
" 'female' in str(v).lower() or 'male' in str(v).lower() for v in unique_values):\n",
" gender_row = int(key)\n",
"else:\n",
" print(f\"Meta data file not found at: {meta_data_file}\")\n",
" print(\"Checking for alternative JSON files in the directory...\")\n",
" \n",
" # Check if there are any JSON files in the directory\n",
" json_files = [f for f in os.listdir(in_cohort_dir) if f.endswith('.json')]\n",
" if json_files:\n",
" print(f\"Found JSON files: {json_files}\")\n",
" # Try the first JSON file\n",
" alternative_file = os.path.join(in_cohort_dir, json_files[0])\n",
" try:\n",
" with open(alternative_file, 'r') as f:\n",
" meta_data = json.load(f)\n",
" print(f\"Loaded alternative meta data from: {alternative_file}\")\n",
" except Exception as e:\n",
" print(f\"Error loading alternative JSON file: {e}\")\n",
" else:\n",
" print(\"No JSON files found in the directory.\")\n",
"\n",
"# Check for data in any other files in the directory\n",
"if not is_gene_available:\n",
" # Look for files that might contain gene expression data\n",
" gene_data_indicators = ['gene', 'expression', 'probe', 'mrna', 'matrix', 'series']\n",
" all_files = os.listdir(in_cohort_dir)\n",
" potential_gene_files = [f for f in all_files if any(indicator in f.lower() for indicator in gene_data_indicators)]\n",
" \n",
" if potential_gene_files:\n",
" print(f\"Found potential gene expression files: {potential_gene_files}\")\n",
" is_gene_available = True\n",
" else:\n",
" print(\"No files indicating gene expression data found.\")\n",
"\n",
"# Output the identified rows\n",
"print(f\"Identified trait_row: {trait_row}\")\n",
"print(f\"Identified age_row: {age_row}\")\n",
"print(f\"Identified gender_row: {gender_row}\")\n",
"print(f\"Is gene expression data available: {is_gene_available}\")\n",
"print(f\"Is trait data available: {is_trait_available}\")\n",
"\n",
"# Define conversion functions regardless of data availability\n",
"def convert_trait(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary (0 for control, 1 for fibromyalgia)\n",
" if 'fibromyalgia' in value or 'fm' in value or 'patient' in value:\n",
" return 1\n",
" elif 'control' in value or 'healthy' in value:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Extract numeric age using regex\n",
" import re\n",
" match = re.search(r'(\\d+(\\.\\d+)?)', value)\n",
" if match:\n",
" return float(match.group(1))\n",
" else:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary (0 for female, 1 for male)\n",
" if 'female' in value or 'f' == value.strip():\n",
" return 0\n",
" elif 'male' in value or 'm' == value.strip():\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# Use validate_and_save_cohort_info for initial filtering\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",
"# Extract clinical features if data is available\n",
"if trait_row is not None and clinical_data is not None:\n",
" # Extract clinical features using the geo_select_clinical_features function\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row\n"
]
},
{
"cell_type": "markdown",
"id": "27313159",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "31f56ec1",
"metadata": {},
"outputs": [],
"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": "c463287f",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2b3b6757",
"metadata": {},
"outputs": [],
"source": [
"# Analyzing the gene identifiers from the previous step\n",
"\n",
"# These appear to be microarray probe IDs (likely Affymetrix Human Gene 1.0 ST Array)\n",
"# They are 7-digit numeric IDs (7892501, 7892502, etc.) which are typical for\n",
"# probesets in microarray platforms, not standard human gene symbols\n",
"# Human gene symbols would be alphanumeric (like BRCA1, TP53, etc.)\n",
"\n",
"# Since these are probe IDs and not human gene symbols, they will need to be mapped\n",
"# to standard gene symbols for proper biological interpretation\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "0fd33e6b",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01cd7f56",
"metadata": {},
"outputs": [],
"source": [
"# 1. First get the file paths using geo_get_relevant_filepaths function\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 3. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "acc24bfc",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8bea9ace",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify the columns that contain gene identifiers and gene symbols\n",
"# From the gene annotation preview, I can see:\n",
"# - 'ID' column has identifiers like '7896736' which match the format in gene expression data\n",
"# - 'gene_assignment' column contains gene symbols and annotations\n",
"\n",
"# 2. Create a gene mapping dataframe\n",
"# The 'gene_assignment' column contains complex text with gene symbols\n",
"# Using get_gene_mapping to extract IDs and gene symbols\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')\n",
"\n",
"# Print the mapping dataframe to verify\n",
"print(\"Gene mapping dataframe preview:\")\n",
"print(preview_df(mapping_df))\n",
"\n",
"# 3. Convert probe-level measurements to gene expression data\n",
"# Using the library function to apply gene mapping\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Print the dimensions and preview of the gene expression data after mapping\n",
"print(f\"\\nGene expression data after mapping: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"print(\"First few rows of the mapped gene expression data:\")\n",
"print(preview_df(gene_data))\n",
"\n",
"# Normalize gene symbols\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"\\nAfter normalizing gene symbols: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n"
]
},
{
"cell_type": "markdown",
"id": "2b61d458",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "809544ee",
"metadata": {},
"outputs": [],
"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",
"# Extract the gene expression data from the matrix file \n",
"gene_expression_data = get_genetic_data(matrix_file)\n",
"\n",
"# Extract gene annotation data from the SOFT file\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')\n",
"\n",
"# Apply gene mapping and normalize gene symbols\n",
"gene_data = apply_gene_mapping(gene_expression_data, mapping_df)\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"\n",
"# 1. Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Process clinical data\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
"\n",
"# Determine trait row (from previous step)\n",
"trait_row = 0 # 'diagnosis: healthy control' or 'diagnosis: fibromyalgia'\n",
"\n",
"# Define conversion function for trait\n",
"def convert_trait(value):\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" # Convert to binary (0 for control, 1 for fibromyalgia)\n",
" if value == 'fibromyalgia':\n",
" return 1\n",
" elif value == 'healthy control':\n",
" return 0\n",
" return None\n",
"\n",
"# Extract clinical features\n",
"clinical_features = 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=None,\n",
" convert_age=None,\n",
" gender_row=None,\n",
" convert_gender=None\n",
")\n",
"\n",
"# Save clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_features.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"print(\"Clinical features preview:\")\n",
"print(preview_df(clinical_features))\n",
"\n",
"# 2. Link clinical and genetic data\n",
"if not clinical_features.empty:\n",
" linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
" print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n",
" \n",
" # 3. Handle missing values\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" # 4. Determine if trait and demographic features are biased\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" print(f\"Data shape after removing biased features: {linked_data.shape}\")\n",
" \n",
" # 5. Validate and save cohort info\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,\n",
" note=\"Dataset contains gene expression data from peripheral blood of Fibromyalgia patients and healthy controls.\"\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.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(\"Dataset deemed not usable for associational studies.\")\n",
"else:\n",
" # No clinical data available\n",
" print(\"Clinical data is empty. Dataset not usable for association studies.\")\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=None,\n",
" df=pd.DataFrame(index=normalized_gene_data.columns),\n",
" note=\"Dataset contains gene expression data but lacks usable clinical metadata for Fibromyalgia studies.\"\n",
" )"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|