{ "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 }