{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "65ab8bd1", "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 = \"Allergies\"\n", "cohort = \"GSE182740\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Allergies\"\n", "in_cohort_dir = \"../../input/GEO/Allergies/GSE182740\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Allergies/GSE182740.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Allergies/gene_data/GSE182740.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Allergies/clinical_data/GSE182740.csv\"\n", "json_path = \"../../output/preprocess/Allergies/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "7ddcc40f", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": null, "id": "160d6d0c", "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": "ee8297ac", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "2adc0c65", "metadata": {}, "outputs": [], "source": [ "I'll fix the issues with the file reading approach and simplify the clinical data creation using the sample characteristics dictionary directly.\n", "\n", "```python\n", "import pandas as pd\n", "import numpy as np\n", "import os\n", "import gzip\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this is microarray data for gene expression\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "\n", "# For trait (Allergies), we use the 'disease' field since this study compares different skin conditions\n", "trait_row = 1 # The key for disease information\n", "\n", "# No explicit age information is provided in the sample characteristics\n", "age_row = None\n", "\n", "# No gender information is provided in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert disease information to a binary indicator for allergies\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Based on the study design, this dataset focuses on atopic dermatitis (a form of allergy)\n", " # and its overlap with psoriasis\n", " if value.lower() == 'atopic_dermatitis':\n", " return 1 # Allergic condition\n", " elif value.lower() == 'mixed':\n", " return 1 # This represents overlap phenotype which includes allergic component\n", " elif value.lower() == 'psoriasis':\n", " return 0 # Non-allergic skin condition\n", " elif value.lower() == 'normal_skin':\n", " return 0 # No allergic condition\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age information to numeric value\"\"\"\n", " # Not applicable as age data is not available\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender information to binary (0: female, 1: male)\"\"\"\n", " # Not applicable as gender data is not available\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Conduct initial filtering\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_row is not None:\n", " # Create a DataFrame from the sample characteristics dictionary directly\n", " sample_char_dict = {\n", " 0: ['tissue: skin'], \n", " 1: ['disease: Psoriasis', 'disease: Atopic_dermatitis', 'disease: Mixed', 'disease: Normal_skin'],\n", " 2: ['lesional (ls) vs. nonlesional (nl) vs. normal: LS', 'lesional (ls) vs. nonlesional (nl) vs. normal: NL', 'lesional (ls) vs. nonlesional (nl) vs. normal: Normal'],\n", " 3: ['psoriasis area and diseave severity index (pasi): 10.1', 'psoriasis area and diseave severity index (pasi): 7.9', 'psoriasis area and diseave severity index (pasi): 10.4', 'psoriasis area and diseave severity index (pasi): 9', 'psoriasis area and diseave severity index (pasi): 18.4', 'psoriasis area and diseave severity index (pasi): 11.1', 'psoriasis area and diseave severity index (pasi): 8.5', 'psoriasis area and diseave severity index (pasi): 7.1', 'psoriasis area and diseave severity index (pasi): 6.3', 'psoriasis area and diseave severity index (pasi): 10.8', 'psoriasis area and diseave severity index (pasi): 7.4', 'psoriasis area and diseave severity index (pasi): 3.5', 'psoriasis area and diseave severity index (pasi): 4.7', 'psoriasis area and diseave severity index (pasi): 4', 'psoriasis area and diseave severity index (pasi): 25.4', 'psoriasis area and diseave severity index (pasi): 5.8', 'psoriasis area and diseave severity index (pasi): 6', 'psoriasis area and diseave severity index (pasi): 17.2', 'psoriasis area and diseave severity index (pasi): 7.6', 'psoriasis area and diseave severity index (pasi): 3.6', 'psoriasis area and diseave severity index (pasi): 2.4', 'psoriasis area and diseave severity index (pasi): 2.9', 'psoriasis area and diseave severity index (pasi): 17.9', 'psoriasis area and diseave severity index (pasi): 1.4', 'psoriasis area and diseave severity index (pasi): 18', 'psoriasis area and diseave severity index (pasi): 10.6', 'psoriasis area and diseave severity index (pasi): 11.8', 'psoriasis area and diseave severity index (pasi): 6.6', 'psoriasis area and diseave severity index (pasi): 20.4', 'psoriasis area and diseave severity index (pasi): 17.7'],\n", " 4: ['scoring atopic dermatitis (scorad): 19.97', 'scoring atopic dermatitis (scorad): 41.94', 'scoring atopic dermatitis (scorad): 46.98', 'scoring atopic dermatitis (scorad): 36.38', 'scoring atopic dermatitis (scorad): 81.92', 'scoring atopic dermatitis (scorad): 39.24', 'scoring atopic dermatitis (scorad): 51.74', 'scoring atopic dermatitis (scorad): 17.03', 'scoring atopic dermatitis (scorad): 35.2', 'scoring atopic dermatitis (scorad): 29.64', 'scoring atopic dermatitis (scorad): 43.3', 'scoring atopic dermatitis (scorad): 42.97', 'scoring atopic dermatitis (scorad): 13.22', 'scoring atopic dermatitis (scorad): 13.87', 'scoring atopic dermatitis (scorad): 14.29', 'scoring atopic dermatitis (scorad): 36.44', 'scoring atopic dermatitis (scorad): 21.94', 'scoring atopic dermatitis (scorad): 18.62', 'scoring atopic dermatitis (scorad): 30.2', 'scoring atopic dermatitis (scorad): 17.14', 'scoring atopic dermatitis (scorad): 16.99', 'scoring atopic dermatitis (scorad): 14.51', 'scoring atopic dermatitis (scorad): 12.64', 'scoring atopic dermatitis (scorad): 16.33', 'scoring atopic dermatitis (scorad): 32.31', 'scoring atopic dermatitis (scorad): 14.52', 'scoring atopic dermatitis (scorad): 30.49', 'scoring atopic dermatitis (scorad): 29.03', 'scoring atopic dermatitis (scorad): 33.96', 'scoring atopic dermatitis (scorad): 12.76'],\n", " 5: ['eczema area and severity index (easi): 9.4', 'eczema area and severity index (easi): 22.65', 'eczema area and severity index (easi): 25.55', 'eczema area and severity index (easi): 25.5', 'eczema area and severity index (easi): 47.65', 'eczema area and severity index (easi): 18.9', 'eczema area and severity index (easi): 28.65', 'eczema area and severity index (easi): 9.6', 'eczema area and severity index (easi): 20.95', 'eczema area and severity index (easi): 23.5', 'eczema area and severity index (easi): 29.6', 'eczema area an\n" ] }, { "cell_type": "markdown", "id": "9307e8c3", "metadata": {}, "source": [ "### Step 3: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "749d0443", "metadata": {}, "outputs": [], "source": [ "# Load the input files to explore the dataset\n", "import os\n", "import pandas as pd\n", "import re\n", "import numpy as np\n", "\n", "# Define possible paths for clinical data\n", "clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n", "sample_info_path = os.path.join(in_cohort_dir, \"sample_info.csv\")\n", "gene_data_path = os.path.join(in_cohort_dir, \"gene_data.csv\")\n", "\n", "# Check if gene expression data is available\n", "is_gene_available = os.path.exists(gene_data_path)\n", "\n", "# Initialize variables\n", "clinical_data = None\n", "background_info = \"\"\n", "sample_chars = {}\n", "\n", "# Try to load clinical data from different possible files\n", "if os.path.exists(clinical_data_path):\n", " clinical_data = pd.read_csv(clinical_data_path)\n", " print(\"Clinical data loaded from clinical_data.csv\")\n", " # Display sample characteristics to identify relevant rows\n", " print(\"Sample characteristics preview:\")\n", " for i, row in clinical_data.iterrows():\n", " unique_values = clinical_data.iloc[i, 1:].unique()\n", " sample_chars[i] = list(unique_values)\n", " print(sample_chars)\n", "elif os.path.exists(sample_info_path):\n", " clinical_data = pd.read_csv(sample_info_path)\n", " print(\"Clinical data loaded from sample_info.csv\")\n", " # Display sample characteristics to identify relevant rows\n", " print(\"Sample characteristics preview:\")\n", " for i, row in clinical_data.iterrows():\n", " unique_values = clinical_data.iloc[i, 1:].unique()\n", " sample_chars[i] = list(unique_values)\n", " print(sample_chars)\n", "else:\n", " print(\"Clinical data not available in standard files\")\n", "\n", "# Check for background information\n", "background_path = os.path.join(in_cohort_dir, \"background.txt\")\n", "if os.path.exists(background_path):\n", " with open(background_path, 'r') as f:\n", " background_info = f.read()\n", " print(\"Background information preview:\")\n", " print(background_info[:500] + \"...\" if len(background_info) > 500 else background_info)\n", "else:\n", " print(\"Background information not available\")\n", "\n", "# Set trait_row, age_row, and gender_row based on available data\n", "# Initially set all to None (indicating data not available)\n", "trait_row = None\n", "age_row = None\n", "gender_row = None\n", "\n", "# Function to convert trait values (for allergies)\n", "def convert_trait(value):\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if isinstance(value, str) and ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary (1 for allergic/positive, 0 for control/negative)\n", " if isinstance(value, str):\n", " value_lower = value.lower()\n", " if any(term in value_lower for term in ['allergic', 'allergy', 'positive', 'yes', 'disease']):\n", " return 1\n", " elif any(term in value_lower for term in ['control', 'healthy', 'negative', 'no', 'normal']):\n", " return 0\n", " \n", " return None\n", "\n", "# Function to convert age values\n", "def convert_age(value):\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if isinstance(value, str) and ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Try to extract numeric age\n", " if isinstance(value, str):\n", " # Try to find numbers in the string\n", " numbers = re.findall(r'\\d+(?:\\.\\d+)?', value)\n", " if numbers:\n", " try:\n", " return float(numbers[0])\n", " except:\n", " pass\n", " elif isinstance(value, (int, float)):\n", " return float(value)\n", " \n", " return None\n", "\n", "# Function to convert gender values\n", "def convert_gender(value):\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if isinstance(value, str) and ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary (0 for female, 1 for male)\n", " if isinstance(value, str):\n", " value_lower = value.lower()\n", " if any(term in value_lower for term in ['female', 'f', 'woman', 'girl']):\n", " return 0\n", " elif any(term in value_lower for term in ['male', 'm', 'man', 'boy']):\n", " return 1\n", " \n", " return None\n", "\n", "# Determine if trait data is available\n", "# This should be False if clinical_data is None or we couldn't identify a trait_row\n", "is_trait_available = (clinical_data is not None) and (trait_row is not None)\n", "\n", "# Save metadata 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 and save 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,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the selected clinical features\n", " print(\"Selected clinical features preview:\")\n", " print(preview_df(selected_clinical_df))\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, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "else:\n", " print(f\"Clinical data processing skipped: trait data available: {is_trait_available}\")\n" ] }, { "cell_type": "markdown", "id": "943e869c", "metadata": {}, "source": [ "### Step 4: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "cd3c2ecf", "metadata": {}, "outputs": [], "source": [ "# 1. First get the file paths again to access the matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Use the get_genetic_data function from the library to get the gene_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) for future observation\n", "print(\"First 20 gene/probe identifiers:\")\n", "print(gene_data.index[:20])\n" ] }, { "cell_type": "markdown", "id": "a56a8108", "metadata": {}, "source": [ "### Step 5: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": null, "id": "4c6360e2", "metadata": {}, "outputs": [], "source": [ "# Looking at the gene identifiers, I can see they're in the format of Affymetrix probe IDs (e.g., \"1007_s_at\")\n", "# These are not human gene symbols but probe identifiers from an Affymetrix microarray platform\n", "# They need to be mapped to standard gene symbols for consistent analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "bf5bbf7e", "metadata": {}, "source": [ "### Step 6: Gene Annotation" ] }, { "cell_type": "code", "execution_count": null, "id": "a9672e6a", "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": "cab963b2", "metadata": {}, "source": [ "### Step 7: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": null, "id": "59d54a44", "metadata": {}, "outputs": [], "source": [ "# 1. Identify the relevant columns in the gene annotation dataframe\n", "# From previous outputs, I can see:\n", "# - The gene identifiers in the expression data are Affymetrix probe IDs (e.g., \"1007_s_at\")\n", "# - In the annotation dataframe, these are stored in the 'ID' column\n", "# - Gene symbols are stored in the 'Gene Symbol' column\n", "\n", "# 2. Create the gene mapping dataframe\n", "mapping_df = get_gene_mapping(gene_annotation, \"ID\", \"Gene Symbol\")\n", "print(f\"Created mapping dataframe with {len(mapping_df)} probe-to-gene mappings\")\n", "\n", "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "print(f\"Converted probe-level data to gene expression data with {len(gene_data)} genes\")\n", "\n", "# Preview the first few genes in the mapped data\n", "print(\"\\nFirst 5 genes in the gene expression data:\")\n", "print(gene_data.index[:5])\n", "\n", "# Check the shape of the resulting gene expression dataframe\n", "print(f\"\\nShape of gene expression data: {gene_data.shape}\")\n" ] }, { "cell_type": "markdown", "id": "8de5dd9b", "metadata": {}, "source": [ "### Step 8: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": null, "id": "36b048b0", "metadata": {}, "outputs": [], "source": [ "# 1. First get the file paths again\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Re-extract the gene expression data\n", "gene_data_raw = get_genetic_data(matrix_file)\n", "\n", "# 3. Re-extract gene annotation and create mapping\n", "gene_annotation = get_gene_annotation(soft_file)\n", "mapping_df = get_gene_mapping(gene_annotation, \"ID\", \"Gene Symbol\")\n", "gene_data = apply_gene_mapping(gene_data_raw, mapping_df)\n", "\n", "# 4. Normalize gene symbols in the gene expression data\n", "print(\"Normalizing gene symbols...\")\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 a CSV 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", "# 5. Define the trait row based on the sample characteristics dictionary observed in Step 1\n", "trait_row = 1 # The disease information is in row 1\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert disease information to a binary indicator for allergies\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if isinstance(value, str) and ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Based on the study design, this dataset focuses on atopic dermatitis (a form of allergy)\n", " # and its overlap with psoriasis\n", " if isinstance(value, str):\n", " value_lower = value.lower()\n", " if 'atopic_dermatitis' in value_lower:\n", " return 1 # Allergic condition\n", " elif 'mixed' in value_lower:\n", " return 1 # This represents overlap phenotype which includes allergic component\n", " elif 'psoriasis' in value_lower:\n", " return 0 # Non-allergic skin condition\n", " elif 'normal_skin' in value_lower:\n", " return 0 # No allergic condition\n", " return None\n", "\n", "# Age and gender are not available\n", "age_row = None\n", "gender_row = None\n", "convert_age = None\n", "convert_gender = None\n", "\n", "# Reconfirm trait availability\n", "is_trait_available = trait_row is not None\n", "is_gene_available = True\n", "\n", "# 6. Get the clinical data again and extract features\n", "print(\"Loading the original clinical data...\")\n", "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n", "\n", "print(\"Extracting 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=None,\n", " gender_row=gender_row,\n", " convert_gender=None\n", ")\n", "\n", "print(\"Clinical data preview:\")\n", "print(preview_df(selected_clinical_df))\n", "\n", "# Save the clinical data to a CSV file\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", "\n", "# 7. Link clinical and genetic data\n", "print(\"Linking clinical and genetic data...\")\n", "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# 8. Handle missing values in the linked data\n", "print(\"Handling missing values...\")\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", "\n", "# 9. Check if trait is biased\n", "print(\"Checking for bias in trait distribution...\")\n", "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 10. Final validation\n", "note = \"Dataset contains gene expression data from skin biopsies comparing psoriasis, atopic dermatitis, and an overlap phenotype. For allergy analysis, atopic dermatitis and the mixed phenotype are considered as cases (1), while psoriasis and normal skin are controls (0).\"\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", "print(f\"Dataset usability: {is_usable}\")\n", "\n", "# 11. 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 is not usable for trait-gene association studies due to bias or other issues.\")" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }