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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a8edb263",
"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 = \"Autism_spectrum_disorder_(ASD)\"\n",
"cohort = \"GSE89594\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)\"\n",
"in_cohort_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)/GSE89594\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/GSE89594.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE89594.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE89594.csv\"\n",
"json_path = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "698b20ab",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de5856fd",
"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": "e2684c57",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c539510b",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability \n",
"# Based on the background information, this dataset seems to contain gene expression data (\"integrated transcriptome analysis\")\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Data Availability\n",
"# For trait (Autism Spectrum Disorder)\n",
"trait_row = 0 # The diagnosis information is in row 0\n",
"# For age\n",
"age_row = 2 # Age information is in row 2\n",
"# For gender\n",
"gender_row = 3 # Gender information is in row 3\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait values to binary (0 for control, 1 for ASD)\"\"\"\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip().lower()\n",
" \n",
" if \"autism\" in value or \"asd\" in value:\n",
" return 1 # ASD is present\n",
" elif \"control\" in value:\n",
" return 0 # Control\n",
" # Williams Syndrome is not our trait of interest\n",
" elif \"williams\" in value or \"ws\" in value:\n",
" return None\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age values to continuous numeric values\"\"\"\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip().lower()\n",
" \n",
" # Extract numeric age from strings like \"age: 22y\"\n",
" if 'y' in value:\n",
" try:\n",
" age = int(value.replace('y', ''))\n",
" return age\n",
" except ValueError:\n",
" return None\n",
" else:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender values to binary (0 for female, 1 for male)\"\"\"\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip().lower()\n",
" \n",
" if value == \"female\":\n",
" return 0\n",
" elif value == \"male\":\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Trait data is available if 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",
"# If trait data is available, extract and save clinical features\n",
"if trait_row is not None:\n",
" # Create sample characteristics dictionary\n",
" sample_characteristics_dict = {\n",
" 0: ['diagnosis: control', 'diagnosis: autism spectrum disorder (ASD)', 'diagnosis: Williams Syndrome (WS)'],\n",
" 1: ['tissue: whole blood'],\n",
" 2: ['age: 22y', 'age: 23y', 'age: 24y', 'age: 33y', 'age: 21y', 'age: 20y', 'age: 28y', 'age: 25y', 'age: 32y', \n",
" 'age: 36y', 'age: 30y', 'age: 27y', 'age: 31y', 'age: 35y', 'age: 10y', 'age: 16y', 'age: 11y', 'age: 12y', \n",
" 'age: 38y', 'age: 34y', 'age: 29y', 'age: 19y', 'age: 13y', 'age: 15y', 'age: 43y', 'age: 14y', 'age: 17y', \n",
" 'age: 39y', 'age: 26y'],\n",
" 3: ['gender: female', 'gender: male']\n",
" }\n",
" \n",
" # Create a proper DataFrame for geo_select_clinical_features\n",
" # The function expects rows to represent features\n",
" clinical_data = pd.DataFrame.from_dict(sample_characteristics_dict, orient='index')\n",
" \n",
" # 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 dataframe\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save to CSV\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ae32e6ac",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d68a0e98",
"metadata": {},
"outputs": [],
"source": [
"I'll provide clean code for this step without any markdown or narrative text:\n",
"\n",
"```python\n",
"# Review the output data and determine gene expression availability, trait, age, and gender data availability\n",
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import re\n",
"import json\n",
"\n",
"# Load main files from this cohort\n",
"expression_files = [f for f in os.listdir(in_cohort_dir) if f.endswith('.txt') or f.endswith('.csv')]\n",
"if len(expression_files) > 0:\n",
" main_file = os.path.join(in_cohort_dir, expression_files[0])\n",
" # Check if file exists and try to peek at its content\n",
" if os.path.exists(main_file):\n",
" with open(main_file, 'r') as f:\n",
" header = [next(f) for _ in range(10)]\n",
" # Look for gene expression indicators\n",
" gene_expr_indicators = ['gene', 'expression', 'RNA', 'transcript']\n",
" is_gene_available = any(any(indicator.lower() in line.lower() for indicator in gene_expr_indicators) for line in header)\n",
" else:\n",
" is_gene_available = False\n",
"else:\n",
" is_gene_available = False\n",
"\n",
"# Load clinical data or characteristic information\n",
"clinical_file = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
"if os.path.exists(clinical_file):\n",
" clinical_data = pd.read_csv(clinical_file)\n",
" print(\"Clinical data preview:\")\n",
" print(clinical_data.head())\n",
" \n",
" # Search for trait information - ASD related terms\n",
" trait_search_terms = ['autism', 'asd', 'diagnosis', 'condition', 'disease', 'control', 'case', 'patient', 'status']\n",
" trait_row = None\n",
" \n",
" # Search for age information\n",
" age_search_terms = ['age', 'years', 'year old']\n",
" age_row = None\n",
" \n",
" # Search for gender information\n",
" gender_search_terms = ['gender', 'sex', 'male', 'female']\n",
" gender_row = None\n",
" \n",
" # Check each row for trait, age, and gender information\n",
" for i in range(len(clinical_data)):\n",
" row_values = list(clinical_data.iloc[i])\n",
" row_text = ' '.join([str(val).lower() for val in row_values if pd.notna(val)])\n",
" \n",
" # Check for trait information\n",
" if trait_row is None and any(term in row_text for term in trait_search_terms):\n",
" # Verify it's not constant across all samples\n",
" values = [val for val in row_values if pd.notna(val) and val != clinical_data.columns[0]]\n",
" unique_values = set(values)\n",
" if len(unique_values) > 1: # More than one unique value\n",
" trait_row = i\n",
" \n",
" # Check for age information\n",
" if age_row is None and any(term in row_text for term in age_search_terms):\n",
" # Verify it's not constant across all samples\n",
" values = [val for val in row_values if pd.notna(val) and val != clinical_data.columns[0]]\n",
" unique_values = set(values)\n",
" if len(unique_values) > 1: # More than one unique value\n",
" age_row = i\n",
" \n",
" # Check for gender information\n",
" if gender_row is None and any(term in row_text for term in gender_search_terms):\n",
" # Verify it's not constant across all samples\n",
" values = [val for val in row_values if pd.notna(val) and val != clinical_data.columns[0]]\n",
" unique_values = set(values)\n",
" if len(unique_values) > 1: # More than one unique value\n",
" gender_row = i\n",
" \n",
" # If trait information not found, check for study design clues\n",
" if trait_row is None:\n",
" metadata_file = os.path.join(in_cohort_dir, \"metadata.json\")\n",
" if os.path.exists(metadata_file):\n",
" with open(metadata_file, 'r') as f:\n",
" metadata = json.load(f)\n",
" if 'summary' in metadata:\n",
" summary = metadata['summary'].lower()\n",
" if 'autism' in summary or 'asd' in summary:\n",
" # Look for sample groups in clinical data again with different approach\n",
" for i in range(len(clinical_data)):\n",
" row_text = ' '.join([str(val).lower() for val in clinical_data.iloc[i] if pd.notna(val)])\n",
" if 'group' in row_text or 'subject' in row_text or 'sample' in row_text:\n",
" values = [val for val in clinical_data.iloc[i] if pd.notna(val) and val != clinical_data.columns[0]]\n",
" unique_values = set(values)\n",
" if len(unique_values) > 1:\n",
" trait_row = i\n",
" break\n",
" \n",
" # Define conversion functions\n",
" def convert_trait(value):\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value_str = str(value).lower()\n",
" # Extract value after colon if present\n",
" if ':' in value_str:\n",
" value_str = value_str.split(':', 1)[1].strip()\n",
" \n",
" # Convert ASD/autism/case to 1, control/normal/healthy to 0\n",
" if any(term in value_str for term in ['asd', 'autism', 'case', 'patient', 'positive']):\n",
" return 1\n",
" elif any(term in value_str for term in ['control', 'normal', 'healthy', 'negative', 'non-asd']):\n",
" return 0\n",
" return None\n",
" \n",
" def convert_age(value):\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value_str = str(value).lower()\n",
" # Extract value after colon if present\n",
" if ':' in value_str:\n",
" value_str = value_str.split(':', 1)[1].strip()\n",
" \n",
" # Extract numerical age using regex\n",
" age_match = re.search(r'(\\d+\\.?\\d*)', value_str)\n",
" if age_match:\n",
" try:\n",
" return float(age_match.group(1))\n",
" except:\n",
" return None\n",
" return None\n",
" \n",
" def convert_gender(value):\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value_str = str(value).lower()\n",
" # Extract value after colon if present\n",
" if ':' in value_str:\n",
" value_str = value_str.split(':', 1)[1].strip()\n",
" \n",
" # Convert gender: female=0, male=1\n",
" if any(term in value_str for term in ['female', 'f', 'woman', 'girl']):\n",
" return 0\n",
" elif any(term in value_str for term in ['male', 'm', 'man', 'boy']):\n",
" return 1\n",
" return None\n",
" \n",
" # Determine if trait data is available\n",
" is_trait_available = trait_row is not None\n",
" \n",
" # Save initial validation information\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 trait data is available\n",
" if is_trait_available:\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 if age_row is not None else None,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender if gender_row is not None else None\n",
" )\n",
" \n",
" # Preview and save clinical data\n",
" print(\"Selected clinical features preview:\")\n",
" preview = preview_df(selected_clinical_df)\n",
" print(preview)\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.\n"
]
},
{
"cell_type": "markdown",
"id": "58327c84",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "641e92c1",
"metadata": {},
"outputs": [],
"source": [
"# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "1b9ed811",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d40b9bfa",
"metadata": {},
"outputs": [],
"source": [
"# Analyzing the gene identifiers\n",
"# The identifiers shown ('1', '2', '3'...) are numeric values, not standard gene symbols\n",
"# Human gene symbols would typically be alphanumeric like \"BRCA1\", \"TP53\", \"GAPDH\", etc.\n",
"# These appear to be numeric identifiers that would need to be mapped to actual gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "160d3e00",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a6cd5bb",
"metadata": {},
"outputs": [],
"source": [
"# 1. 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",
"# 2. 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": "1da362ff",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "231cb3f0",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify columns for gene identifiers and gene symbols in the annotation data\n",
"# Based on the preview, 'ID' contains identifiers that match the gene expression data\n",
"# 'GENE_SYMBOL' contains the corresponding gene symbols\n",
"probe_col = 'ID'\n",
"gene_symbol_col = 'GENE_SYMBOL'\n",
"\n",
"# 2. Get the gene mapping dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_symbol_col)\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print a preview of the gene expression data after mapping\n",
"print(\"Gene expression data preview after mapping:\")\n",
"print(gene_data.shape)\n",
"print(gene_data.index[:10]) # Show first 10 gene symbols\n"
]
},
{
"cell_type": "markdown",
"id": "c568cd2a",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4dea5344",
"metadata": {},
"outputs": [],
"source": [
"# 1. First, we need to re-establish the file paths and extract data again\n",
"# Get file paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Extract the sample characteristics from the matrix file\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",
"# Define trait, age, and gender rows based on the sample characteristics dictionary\n",
"trait_row = 0 # Diagnosis information is in row 0\n",
"age_row = 2 # Age information is in row 2 \n",
"gender_row = 3 # Gender information is in row 3\n",
"\n",
"# Define conversion functions for clinical data\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait values to binary (0 for control, 1 for ASD)\"\"\"\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip().lower()\n",
" else:\n",
" return None\n",
" \n",
" if \"autism\" in value or \"asd\" in value:\n",
" return 1 # ASD is present\n",
" elif \"control\" in value:\n",
" return 0 # Control\n",
" # Williams Syndrome is not our trait of interest\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age values to continuous numeric values\"\"\"\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip().lower()\n",
" else:\n",
" return None\n",
" \n",
" # Extract numeric age from strings like \"age: 22y\"\n",
" if 'y' in value:\n",
" try:\n",
" age = int(value.replace('y', ''))\n",
" return age\n",
" except ValueError:\n",
" return None\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender values to binary (0 for female, 1 for male)\"\"\"\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip().lower()\n",
" else:\n",
" return None\n",
" \n",
" if value == \"female\":\n",
" return 0\n",
" elif value == \"male\":\n",
" return 1\n",
" return None\n",
"\n",
"# 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",
"# Get gene expression data again\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# Get gene annotation and mapping\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Now we continue with the normalization and linking process\n",
"# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"# Create directory if it doesn't exist\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 the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
"print(f\"Clinical and genetic data linked: {linked_data.shape}\")\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"After handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Conduct quality check and save the cohort information.\n",
"note = \"Dataset contains ASD, control, and Williams Syndrome samples. Only ASD and control samples are used.\"\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_trait_biased, \n",
" df=unbiased_linked_data,\n",
" note=note\n",
")\n",
"\n",
"# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked and processed data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset was determined to be unusable for trait-gene association studies.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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