{ "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 }