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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e66ff380",
"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 = \"Height\"\n",
"cohort = \"GSE181339\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Height\"\n",
"in_cohort_dir = \"../../input/GEO/Height/GSE181339\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Height/GSE181339.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Height/gene_data/GSE181339.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Height/clinical_data/GSE181339.csv\"\n",
"json_path = \"../../output/preprocess/Height/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "c26737df",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6aa8ff0d",
"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": "f981b5e2",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6cf6496",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the series description, this appears to be a gene expression study\n",
"# \"For the microarray experiment...\" suggests gene expression data is available\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Data Availability\n",
"trait_row = 1 # 'group' contains weight status information (NW, OW/OB, MONW)\n",
"age_row = 2 # 'age' is available\n",
"gender_row = 0 # 'gender' is available\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert the group value to binary form (0 for normal weight, 1 for overweight/obese or MONW)\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if value.upper() == \"NW\":\n",
" return 0 # Normal weight\n",
" elif value.upper() in [\"OW/OB\", \"MONW\"]:\n",
" return 1 # Overweight/obese or Metabolically Obese Normal-Weight\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age string to numeric value\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if isinstance(value, str) and \":\" 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 string to binary (0 for female, 1 for male)\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip().lower()\n",
" \n",
" if value.lower() == \"woman\" or value.lower() == \"female\":\n",
" return 0\n",
" elif value.lower() == \"man\" or value.lower() == \"male\":\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Since trait_row is not None, trait data is available\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Conduct initial filtering on the usability\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",
"# Since trait_row is not None, we proceed with clinical data extraction\n",
"# Create a DataFrame from the sample characteristics dictionary provided in the task\n",
"sample_characteristics = {\n",
" 0: ['gender: Man', 'gender: Woman'],\n",
" 1: ['group: NW', 'group: OW/OB', 'group: MONW'],\n",
" 2: ['age: 21', 'age: 23', 'age: 10', 'age: 17', 'age: 11', 'age: 1', 'age: 18', 'age: 12', 'age: 8', 'age: 14', 'age: 26', 'age: 4', 'age: 2', 'age: 3', 'age: 7', 'age: 13', 'age: 15', 'age: 9', 'age: 30', 'age: 19'],\n",
" 3: ['fasting time: 6hr', 'fasting time: 4hr'],\n",
" 4: ['timepoint: 0months', 'timepoint: 6months']\n",
"}\n",
"\n",
"# Convert to a format that geo_select_clinical_features can work with\n",
"# Create a dataframe with the sample characteristics\n",
"clinical_data = pd.DataFrame.from_dict(sample_characteristics, orient='index')\n",
"clinical_data.index.name = 'row_id'\n",
"clinical_data = clinical_data.reset_index()\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=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
")\n",
"\n",
"# Preview the extracted clinical features\n",
"preview = preview_df(clinical_features)\n",
"print(\"Preview of clinical features:\", preview)\n",
"\n",
"# Save the clinical data\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": "1a8f3056",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39ac46bd",
"metadata": {},
"outputs": [],
"source": [
"I'll debug the code to properly handle GEO dataset files and extract clinical features.\n",
"\n",
"```python\n",
"import os\n",
"import json\n",
"import pandas as pd\n",
"import glob\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# First, let's explore what files are available in the directory\n",
"print(f\"Exploring directory: {in_cohort_dir}\")\n",
"available_files = glob.glob(os.path.join(in_cohort_dir, \"*\"))\n",
"print(f\"Available files: {available_files}\")\n",
"\n",
"# GEO data is typically stored in _series_matrix.txt files\n",
"series_matrix_files = glob.glob(os.path.join(in_cohort_dir, \"*_series_matrix.txt*\"))\n",
"if series_matrix_files:\n",
" matrix_file = series_matrix_files[0]\n",
" print(f\"Found series matrix file: {matrix_file}\")\n",
" \n",
" # Read the file line by line to extract sample characteristics\n",
" sample_char_dict = {}\n",
" current_line_idx = 0\n",
" with open(matrix_file, 'r') as f:\n",
" for line in f:\n",
" if line.startswith('!Sample_characteristics_ch1'):\n",
" parts = line.strip().split('\\t')\n",
" if len(parts) > 1: # Ensure there's at least one sample\n",
" # Remove the prefix to get just the values\n",
" values = [p.replace('!Sample_characteristics_ch1 = ', '') for p in parts]\n",
" sample_char_dict[current_line_idx] = values\n",
" current_line_idx += 1\n",
" elif line.startswith('!Sample_title'):\n",
" # Sample titles can sometimes contain useful information\n",
" parts = line.strip().split('\\t')\n",
" if len(parts) > 1:\n",
" values = [p.replace('!Sample_title = ', '') for p in parts]\n",
" sample_char_dict[current_line_idx] = values\n",
" current_line_idx += 1\n",
" \n",
" # If we've collected any sample characteristics, convert to DataFrame\n",
" if sample_char_dict:\n",
" clinical_data = pd.DataFrame(sample_char_dict).T\n",
" # Print a preview of what we found\n",
" print(\"Sample characteristics preview:\")\n",
" for idx, row in clinical_data.iterrows():\n",
" print(f\"Row {idx}: {row.unique()[:5]}...\")\n",
" else:\n",
" clinical_data = pd.DataFrame()\n",
" print(\"No sample characteristics found in series matrix file.\")\n",
"else:\n",
" # If no series matrix file, try to find a soft file\n",
" soft_files = glob.glob(os.path.join(in_cohort_dir, \"*.soft*\"))\n",
" if soft_files:\n",
" soft_file = soft_files[0]\n",
" print(f\"Found SOFT file: {soft_file}\")\n",
" \n",
" # Read the SOFT file to extract sample characteristics\n",
" sample_char_dict = {}\n",
" current_line_idx = 0\n",
" with open(soft_file, 'r') as f:\n",
" in_sample_section = False\n",
" current_sample = None\n",
" for line in f:\n",
" if line.startswith('^SAMPLE'):\n",
" in_sample_section = True\n",
" current_sample = []\n",
" elif line.startswith('!Sample_characteristics_ch1'):\n",
" if in_sample_section:\n",
" current_sample.append(line.strip().split(' = ')[1])\n",
" elif line.startswith('!sample_table_end'):\n",
" if in_sample_section and current_sample:\n",
" sample_char_dict[current_line_idx] = current_sample\n",
" current_line_idx += 1\n",
" current_sample = None\n",
" in_sample_section = False\n",
" \n",
" if sample_char_dict:\n",
" clinical_data = pd.DataFrame(sample_char_dict).T\n",
" print(\"Sample characteristics preview from SOFT file:\")\n",
" for idx, row in clinical_data.iterrows():\n",
" print(f\"Row {idx}: {row.unique()[:5]}...\")\n",
" else:\n",
" clinical_data = pd.DataFrame()\n",
" print(\"No sample characteristics found in SOFT file.\")\n",
" else:\n",
" # As a last resort, try to find any text files\n",
" txt_files = glob.glob(os.path.join(in_cohort_dir, \"*.txt\"))\n",
" if txt_files:\n",
" print(f\"Found text files but no recognized GEO format: {txt_files}\")\n",
" clinical_data = pd.DataFrame()\n",
" else:\n",
" print(\"No recognizable data files found.\")\n",
" clinical_data = pd.DataFrame()\n",
"\n",
"# Analyze what we have and make decisions about data availability\n",
"is_gene_available = True # Assuming gene expression data exists unless determined otherwise\n",
"\n",
"# Determine if height-related data is available in the clinical data\n",
"trait_row = None\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"if not clinical_data.empty:\n",
" # Check each row for trait, age, and gender data\n",
" for row_idx in range(len(clinical_data)):\n",
" row_values = clinical_data.iloc[row_idx].astype(str)\n",
" row_text = ' '.join(row_values).lower()\n",
" \n",
" # Check for trait (Height)\n",
" if 'height' in row_text and trait_row is None:\n",
" unique_values = row_values.unique()\n",
" if len(unique_values) > 1: # More than one unique value\n",
" trait_row = row_idx\n",
" print(f\"Found trait data (Height) in row {row_idx}: {unique_values[:5]}\")\n",
" \n",
" # Check for age\n",
" if ('age' in row_text or 'years' in row_text) and age_row is None:\n",
" unique_values = row_values.unique()\n",
" if len(unique_values) > 1: # More than one unique value\n",
" age_row = row_idx\n",
" print(f\"Found age data in row {row_idx}: {unique_values[:5]}\")\n",
" \n",
" # Check for gender\n",
" if ('gender' in row_text or 'sex' in row_text) and gender_row is None:\n",
" unique_values = row_values.unique()\n",
" if len(unique_values) > 1: # More than one unique value\n",
" gender_row = row_idx\n",
" print(f\"Found gender data in row {row_idx}: {unique_values[:5]}\")\n",
"\n",
"# Define conversion functions based on the identified data structure\n",
"def convert_trait(value):\n",
" \"\"\"Convert height value to a continuous numeric value.\"\"\"\n",
" try:\n",
" # Try to extract a numeric value from the string\n",
" # Height may be in format like \"height: 180cm\" or similar\n",
" if value is None:\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" # Look for height patterns\n",
" if 'height' in value:\n",
" # Extract numeric part - look for digits\n",
" import re\n",
" height_match = re.search(r'(\\d+\\.?\\d*)', value)\n",
" if height_match:\n",
" return float(height_match.group(1))\n",
" # If it's just a number, try to convert directly\n",
" elif value.replace('.', '', 1).isdigit():\n",
" return float(value)\n",
" return None\n",
" except Exception as e:\n",
" print(f\"Error converting trait: {e}\")\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to a continuous numeric value.\"\"\"\n",
" try:\n",
" if value is None:\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" # Look for age patterns\n",
" if 'age' in value or 'years' in value:\n",
" # Extract numeric part\n",
" import re\n",
" age_match = re.search(r'(\\d+\\.?\\d*)', value)\n",
" if age_match:\n",
" return float(age_match.group(1))\n",
" # If it's just a number, try to convert directly\n",
" elif value.replace('.', '', 1).isdigit():\n",
" return float(value)\n",
" return None\n",
" except Exception as e:\n",
" print(f\"Error converting age: {e}\")\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male).\"\"\"\n",
" try:\n",
" if value is None:\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" # Check for gender/sex indicators\n",
" if 'female' in value or 'f' == value.strip() or 'f:\n"
]
},
{
"cell_type": "markdown",
"id": "fcbad901",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "452bb99a",
"metadata": {},
"outputs": [],
"source": [
"# 1. Get the file paths for the SOFT file and matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. First, let's examine the structure of the matrix file to understand its format\n",
"import gzip\n",
"\n",
"# Peek at the first few lines of the file to understand its structure\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" # Read first 100 lines to find the header structure\n",
" for i, line in enumerate(file):\n",
" if '!series_matrix_table_begin' in line:\n",
" print(f\"Found data marker at line {i}\")\n",
" # Read the next line which should be the header\n",
" header_line = next(file)\n",
" print(f\"Header line: {header_line.strip()}\")\n",
" # And the first data line\n",
" first_data_line = next(file)\n",
" print(f\"First data line: {first_data_line.strip()}\")\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Matrix table marker not found in first 100 lines\")\n",
" break\n",
"\n",
"# 3. Now try to get the genetic data with better error handling\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(gene_data.index[:20])\n",
"except KeyError as e:\n",
" print(f\"KeyError: {e}\")\n",
" \n",
" # Alternative approach: manually extract the data\n",
" print(\"\\nTrying alternative approach to read the gene data:\")\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Find the start of the data\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" \n",
" # Read the headers and data\n",
" import pandas as pd\n",
" df = pd.read_csv(file, sep='\\t', index_col=0)\n",
" print(f\"Column names: {df.columns[:5]}\")\n",
" print(f\"First 20 row IDs: {df.index[:20]}\")\n",
" gene_data = df\n"
]
},
{
"cell_type": "markdown",
"id": "28b890b3",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "89de7d56",
"metadata": {},
"outputs": [],
"source": [
"# Based on reviewing the gene identifiers in the gene expression data, I can see they are numeric \n",
"# identifiers (like 7, 8, 15, 18, etc.) rather than human gene symbols (which would be something \n",
"# like BRCA1, TP53, etc.)\n",
"# \n",
"# These appear to be probe IDs from a microarray platform, which need to be mapped to human gene symbols\n",
"# for proper biological interpretation. The numeric format is typical of Affymetrix or similar microarray \n",
"# platforms where probes are identified by numbers rather than gene names.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "bc845dcc",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "870ed6f2",
"metadata": {},
"outputs": [],
"source": [
"# 1. Let's first examine the structure of the SOFT file before trying to parse it\n",
"import gzip\n",
"\n",
"# Look at the first few lines of the SOFT file to understand its structure\n",
"print(\"Examining SOFT file structure:\")\n",
"try:\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" # Read first 20 lines to understand the file structure\n",
" for i, line in enumerate(file):\n",
" if i < 20:\n",
" print(f\"Line {i}: {line.strip()}\")\n",
" else:\n",
" break\n",
"except Exception as e:\n",
" print(f\"Error reading SOFT file: {e}\")\n",
"\n",
"# 2. Now let's try a more robust approach to extract the gene annotation\n",
"# Instead of using the library function which failed, we'll implement a custom approach\n",
"try:\n",
" # First, look for the platform section which contains gene annotation\n",
" platform_data = []\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" in_platform_section = False\n",
" for line in file:\n",
" if line.startswith('^PLATFORM'):\n",
" in_platform_section = True\n",
" continue\n",
" if in_platform_section and line.startswith('!platform_table_begin'):\n",
" # Next line should be the header\n",
" header = next(file).strip()\n",
" platform_data.append(header)\n",
" # Read until the end of the platform table\n",
" for table_line in file:\n",
" if table_line.startswith('!platform_table_end'):\n",
" break\n",
" platform_data.append(table_line.strip())\n",
" break\n",
" \n",
" # If we found platform data, convert it to a DataFrame\n",
" if platform_data:\n",
" import pandas as pd\n",
" import io\n",
" platform_text = '\\n'.join(platform_data)\n",
" gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n",
" low_memory=False, on_bad_lines='skip')\n",
" print(\"\\nGene annotation preview:\")\n",
" print(preview_df(gene_annotation))\n",
" else:\n",
" print(\"Could not find platform table in SOFT file\")\n",
" \n",
" # Try an alternative approach - extract mapping from other sections\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" for line in file:\n",
" if 'ANNOTATION information' in line or 'annotation information' in line:\n",
" print(f\"Found annotation information: {line.strip()}\")\n",
" if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n",
" print(f\"Platform title: {line.strip()}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing gene annotation: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "06038669",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f15b5960",
"metadata": {},
"outputs": [],
"source": [
"# Inspect the gene identifier columns in the annotation dataframe\n",
"print(\"Column names in gene annotation dataframe:\")\n",
"print(gene_annotation.columns)\n",
"\n",
"# Based on previous output, I can see that:\n",
"# - The gene expression data uses numeric IDs (like 7, 8, 15...)\n",
"# - The gene annotation dataframe has an 'ID' column that appears to contain similar numeric identifiers\n",
"# - The 'GENE_SYMBOL' column contains the human gene symbols we need\n",
"\n",
"# 1. Identify the mapping columns\n",
"probe_id_col = 'ID'\n",
"gene_symbol_col = 'GENE_SYMBOL'\n",
"\n",
"# 2. Get the gene mapping dataframe by extracting the two relevant columns\n",
"mapping_df = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col)\n",
"print(f\"Number of probes with gene mapping: {len(mapping_df)}\")\n",
"print(f\"Sample of the mapping dataframe:\")\n",
"print(mapping_df.head())\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"print(f\"Dimensions of gene expression data after mapping: {gene_data.shape}\")\n",
"print(f\"Sample of gene symbols and expression values:\")\n",
"print(gene_data.head())\n",
"\n",
"# Save the gene expression data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Gene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "8f477487",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1323b4e1",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import os\n",
"\n",
"# 1. Let's load the gene expression data\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Extract gene expression data\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# Normalize gene symbols using the NCBI Gene database information\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
"\n",
"# Save the normalized gene data to the output file\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. Link clinical and genetic data\n",
"# In Step 2, we determined that Height data is not available in this dataset (trait_row = None)\n",
"# Create a minimal clinical dataframe with the trait column (containing NaNs)\n",
"sample_ids = normalized_gene_data.columns\n",
"minimal_clinical_df = pd.DataFrame(index=sample_ids)\n",
"minimal_clinical_df[trait] = np.nan # Add the trait column with NaN values\n",
"\n",
"# If we have age and gender data from Step 2, add those columns\n",
"if age_row is not None:\n",
" minimal_clinical_df['Age'] = get_feature_data(clinical_data, age_row, 'Age', convert_age).iloc[0]\n",
"\n",
"if gender_row is not None:\n",
" minimal_clinical_df['Gender'] = get_feature_data(clinical_data, gender_row, 'Gender', convert_gender).iloc[0]\n",
"\n",
"minimal_clinical_df.index.name = 'Sample'\n",
"\n",
"# Save this minimal clinical data for reference\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"minimal_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Create a linked dataset \n",
"linked_data = pd.concat([minimal_clinical_df, normalized_gene_data.T], axis=1)\n",
"linked_data.index.name = 'Sample'\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# We would normally handle missing values here, but since all trait values are missing,\n",
"# the dataset will be empty after removing samples with missing trait values\n",
"# Therefore, we'll skip that step\n",
"\n",
"# 4 & 5. Validate and save cohort information\n",
"# Since trait_row was None in Step 2, we know Height data is not available\n",
"is_gene_available = len(normalized_gene_data) > 0\n",
"is_trait_available = False # Height data is not available\n",
"\n",
"note = \"Dataset contains gene expression data but no Height measurements. This dataset is not usable for studying Height associations.\"\n",
"\n",
"# For datasets without trait data, we set is_biased to False\n",
"# This indicates the dataset is not usable due to missing trait data, not due to bias\n",
"is_biased = False\n",
"\n",
"# Final 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=is_gene_available, \n",
" is_trait_available=is_trait_available, \n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=note\n",
")\n",
"\n",
"# 6. Since there is no trait data, the dataset is not usable for our association study\n",
"# So we should not save it to out_data_file\n",
"print(f\"Dataset usability: {is_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 does not contain Height data and cannot be used for association studies.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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