{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "b381ed7e", "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 = \"Bipolar_disorder\"\n", "cohort = \"GSE92538\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Bipolar_disorder\"\n", "in_cohort_dir = \"../../input/GEO/Bipolar_disorder/GSE92538\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Bipolar_disorder/GSE92538.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Bipolar_disorder/gene_data/GSE92538.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Bipolar_disorder/clinical_data/GSE92538.csv\"\n", "json_path = \"../../output/preprocess/Bipolar_disorder/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "68a8bbdf", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": null, "id": "c7fd9cf2", "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": "6fa7bd63", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "6161e74f", "metadata": {}, "outputs": [], "source": [ "As a domain expert in this biomedical research project, I'll provide the corrected code for this step:\n", "\n", "```python\n", "import pandas as pd\n", "import os\n", "import json\n", "from typing import Optional, Callable, Dict, Any\n", "\n", "# 1. Check for gene expression data availability\n", "# Based on the background information, this dataset contains transcriptomic data\n", "# from human brain (dorsolateral prefrontal cortex) with 11,911 ENTREZ transcripts\n", "is_gene_available = True # Gene expression data is available\n", "\n", "# 2. Identify variable availability and create conversion functions\n", "\n", "# 2.1 Trait (Bipolar disorder)\n", "# From the sample characteristics, trait information is in row 2 (\"diagnosis: ...\")\n", "trait_row = 2\n", "\n", "# 2.2 Age information\n", "# Age is available in row 8\n", "age_row = 8\n", "\n", "# 2.3 Gender information\n", "# Gender is available in row 6\n", "gender_row = 6\n", "\n", "# Conversion functions\n", "def convert_trait(value):\n", " \"\"\"Convert diagnosis information to binary trait indicator for Bipolar disorder.\"\"\"\n", " if value is None or 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()\n", " \n", " # Check if it's Bipolar Disorder (case-insensitive)\n", " if 'bipolar' in value.lower():\n", " return 1\n", " elif value.lower() in ['control', 'major depressive disorder', 'schizophrenia']:\n", " return 0\n", " else:\n", " return None # Unknown or other diagnosis\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age information to continuous numerical value.\"\"\"\n", " if value is None or 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()\n", " \n", " try:\n", " return float(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender information to binary (0=female, 1=male).\"\"\"\n", " if value is None or 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()\n", " \n", " # Convert to binary values\n", " if value.upper() == 'F':\n", " return 0\n", " elif value.upper() == 'M':\n", " return 1\n", " else:\n", " return None # Unknown gender\n", "\n", "# 3. Save metadata through 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. Extract clinical features if available\n", "if trait_row is not None:\n", " # Create a DataFrame from the sample characteristics dictionary\n", " sample_chars = {\n", " 0: ['cohort: Schiz Cohort 1', 'cohort: Dep Cohort 3', 'cohort: Dep Cohort 4', 'cohort: Dep Cohort 2', 'cohort: Dep Cohort 1'], \n", " 1: ['site of processing: UC_Davis', 'site of processing: UC_Irvine', 'site of processing: U_Michigan'], \n", " 2: ['diagnosis: Schizophrenia', 'diagnosis: Control', 'diagnosis: Major Depressive Disorder', 'diagnosis: Bipolar Disorder'], \n", " 3: ['subject id: 101078', 'subject id: 101244', 'subject id: 101283', 'subject id: 101309', 'subject id: 101431', 'subject id: 101488', 'subject id: 101597', 'subject id: 101657', 'subject id: 101674', 'subject id: 101690', 'subject id: 101736', 'subject id: 101739', 'subject id: 101811', 'subject id: 101923', 'subject id: 101956', 'subject id: 102038', 'subject id: 102118', 'subject id: 102212', 'subject id: 102420', 'subject id: 102422', 'subject id: 102432', 'subject id: 102440', 'subject id: 102475', 'subject id: 102513', 'subject id: 102539', 'subject id: 102636', 'subject id: 102785', 'subject id: 103037', 'subject id: 103068', 'subject id: 103091'], \n", " 4: ['agonal factor: 0', 'agonal factor: NA', 'agonal factor: 1', 'agonal factor: 2', 'agonal factor: 3'], \n", " 5: ['tissue ph (cerebellum): 6.83', 'tissue ph (cerebellum): 6.97', 'tissue ph (cerebellum): 7.01', 'tissue ph (cerebellum): NA', 'tissue ph (cerebellum): 6.87', 'tissue ph (cerebellum): 7.05', 'tissue ph (cerebellum): 6.38', 'tissue ph (cerebellum): 6.72', 'tissue ph (cerebellum): 6.91', 'tissue ph (cerebellum): 6.06', 'tissue ph (cerebellum): 7', 'tissue ph (cerebellum): 7.02', 'tissue ph (cerebellum): 6.86', 'tissue ph (cerebellum): 6.54', 'tissue ph (cerebellum): 7.21', 'tissue ph (cerebellum): 6.63', 'tissue ph (cerebellum): 6.42', 'tissue ph (cerebellum): 6.89', 'tissue ph (cerebellum): 7.19', 'tissue ph (cerebellum): 6.68', 'tissue ph (cerebellum): 6.62', 'tissue ph (cerebellum): 7.17', 'tissue ph (cerebellum): 6.84', 'tissue ph (cerebellum): 6.76', 'tissue ph (cerebellum): 6.93', 'tissue ph (cerebellum): 6.59', 'tissue ph (cerebellum): 6.61', 'tissue ph (cerebellum): 6.79', 'tissue ph (cerebellum): 7.2', 'tissue ph (cerebellum): 6.81'], \n", " 6: ['gender: M', 'gender: F'], \n", " 7: ['race: Caucasian', 'race: Hispanic', 'race: Asian', 'race: Other', 'race: African American'], \n", " 8: ['age: 39', 'age: 32', 'age: 63', 'age: 70', 'age: 71', 'age: 44', 'age: 66', 'age: 59', 'age: 65', 'age: 69', 'age: 52', 'age: 49', 'age: 58', 'age: 45', 'age: 72', 'age: 73', 'age: 64', 'age: 77', 'age: 55', 'age: 53', 'age: 62', 'age: 47', 'age: 67', 'age: 57', 'age: 35', 'age: 48', 'age: 19', 'age: 54', 'age: 50', 'age: 56'], \n", " 9: ['post-mortem interval: 7.5', 'post-mortem interval: 27.25', 'post-mortem interval: 29.5', 'post-mortem interval: 14.5', 'post-mortem interval: 36.5', 'post-mortem interval: 23', 'post-mortem interval: 32', 'post-mortem interval: 13.25', 'post-mortem interval: 26', 'post-mortem interval: 11.25', 'post-mortem interval:\n" ] }, { "cell_type": "markdown", "id": "cea28f95", "metadata": {}, "source": [ "### Step 3: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "a050473f", "metadata": {}, "outputs": [], "source": [ "I understand the issue now. I need to extract and parse the GEO files, focusing on the .soft file which contains the clinical information.\n", "\n", "```python\n", "import pandas as pd\n", "import numpy as np\n", "import os\n", "import json\n", "import gzip\n", "import re\n", "from typing import Callable, Optional, Dict, Any\n", "\n", "# Function to extract and parse the .soft file\n", "def parse_geo_soft_file(file_path):\n", " # Dictionary to store sample characteristics by sample_id\n", " sample_characteristics = {}\n", " current_sample = None\n", " \n", " # Open the gzipped file and decode it\n", " with gzip.open(file_path, 'rt') as f:\n", " for line in f:\n", " line = line.strip()\n", " \n", " # Identify when we start a new sample\n", " if line.startswith(\"^SAMPLE = \"):\n", " current_sample = line.split(\"^SAMPLE = \")[1]\n", " sample_characteristics[current_sample] = {}\n", " \n", " # Extract sample characteristics\n", " elif current_sample and line.startswith(\"!Sample_characteristics_ch1 = \"):\n", " char_value = line.replace(\"!Sample_characteristics_ch1 = \", \"\").strip()\n", " if \":\" in char_value:\n", " key, value = char_value.split(\":\", 1)\n", " sample_characteristics[current_sample][key.strip()] = value.strip()\n", " else:\n", " # Handle characteristics without a key-value format\n", " sample_characteristics[current_sample][char_value] = True\n", " \n", " # Extract sample title\n", " elif current_sample and line.startswith(\"!Sample_title = \"):\n", " title = line.replace(\"!Sample_title = \", \"\").strip()\n", " sample_characteristics[current_sample][\"title\"] = title\n", " \n", " return sample_characteristics\n", "\n", "# First, check if the soft file exists\n", "soft_file_path = os.path.join(in_cohort_dir, \"GSE92538_family.soft.gz\")\n", "if os.path.exists(soft_file_path):\n", " print(f\"Parsing SOFT file: {soft_file_path}\")\n", " sample_data = parse_geo_soft_file(soft_file_path)\n", " \n", " # Convert to DataFrame for easier analysis\n", " samples_list = []\n", " for sample_id, characteristics in sample_data.items():\n", " sample_dict = {'sample_id': sample_id}\n", " sample_dict.update(characteristics)\n", " samples_list.append(sample_dict)\n", " \n", " if samples_list:\n", " clinical_df = pd.DataFrame(samples_list)\n", " print(\"Clinical data structure:\")\n", " print(clinical_df.head())\n", " print(f\"Number of samples: {len(clinical_df)}\")\n", " print(f\"Columns: {clinical_df.columns.tolist()}\")\n", " \n", " # Check unique values in relevant columns to identify trait, age, and gender\n", " for col in clinical_df.columns:\n", " if col != 'sample_id':\n", " unique_vals = clinical_df[col].unique()\n", " print(f\"Column '{col}' unique values: {unique_vals}\")\n", " \n", " # Look for columns containing relevant information\n", " col_lower = col.lower()\n", " if 'disease' in col_lower or 'diagnosis' in col_lower or 'status' in col_lower or 'group' in col_lower:\n", " print(f\"Potential trait column: {col}\")\n", " elif 'age' in col_lower:\n", " print(f\"Potential age column: {col}\")\n", " elif 'gender' in col_lower or 'sex' in col_lower:\n", " print(f\"Potential gender column: {col}\")\n", " \n", " # Check if gene expression data is available by looking for matrix files\n", " is_gene_available = any(f.endswith('_series_matrix.txt.gz') for f in os.listdir(in_cohort_dir))\n", " \n", " # Now, based on the column analysis, define trait_row, age_row, and gender_row\n", " # These will be set based on the analysis output\n", " trait_row = None\n", " age_row = None\n", " gender_row = None\n", " \n", " # For demonstration, let's create a transposed clinical data for geo_select_clinical_features\n", " # In a real scenario, we would identify the specific rows from the DataFrame\n", " data_dict = {}\n", " for i, col in enumerate(clinical_df.columns):\n", " if col != 'sample_id':\n", " data_dict[i] = clinical_df[col].tolist()\n", " \n", " transposed_clinical_df = pd.DataFrame(data_dict, index=clinical_df['sample_id'])\n", " \n", " # Check the title column for trait information\n", " if 'title' in clinical_df.columns:\n", " title_unique = clinical_df['title'].unique()\n", " print(f\"Sample titles: {title_unique}\")\n", " \n", " # Check if titles contain bipolar/control information\n", " has_bipolar_info = any('bipolar' in str(title).lower() or 'control' in str(title).lower() for title in title_unique)\n", " if has_bipolar_info:\n", " trait_row = list(clinical_df.columns).index('title') - 1 # Adjust for sample_id column\n", " print(f\"Found trait information in 'title' column, row: {trait_row}\")\n", " \n", " # Conversion functions based on the data analysis\n", " def convert_trait(value):\n", " if pd.isna(value):\n", " return None\n", " value_str = str(value).lower()\n", " \n", " if 'control' in value_str or 'healthy' in value_str or 'normal' in value_str:\n", " return 0\n", " elif 'bipolar' in value_str or 'bd' in value_str or 'bp' in value_str or 'patient' in value_str:\n", " return 1\n", " return None\n", " \n", " def convert_age(value):\n", " if pd.isna(value):\n", " return None\n", " try:\n", " # Extract numbers from text\n", " nums = re.findall(r'\\d+\\.?\\d*', str(value))\n", " if nums:\n", " return float(nums[0])\n", " return None\n", " except:\n", " return None\n", " \n", " def convert_gender(value):\n", " if pd.isna(value):\n", " return None\n", " value_str = str(value).lower()\n", " \n", " if 'female' in value_str or 'f' == value_str:\n", " return 0\n", " elif 'male' in value_str or 'm' == value_str:\n", " return 1\n", " return None\n", " \n", " # For this dataset, determine trait availability based on analysis\n", " is_trait_available = trait_row is not None\n", " \n", " # Save metadata\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 is available\n", " if is_trait_available:\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=transposed_clinical_df,\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 the selected clinical features\n", " print(\"Preview of selected clinical features:\")\n", " preview = preview_df(selected_clinical_df)\n", " print(preview)\n", " \n", " # Save the selected clinical features 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, index=False)\n", " else:\n", " print(\"Clinical feature extraction skipped: Trait data not available\")\n", " else:\n", " print(\"No sample data found in the SOFT file\")\n", " is_gene_available = any(f.endswith('_series_matrix.txt.gz') for f in os.listdir(in_cohort_dir))\n", " validate_and_save_cohort_info(\n", " is_final=False,\n" ] }, { "cell_type": "markdown", "id": "dad6f9ee", "metadata": {}, "source": [ "### Step 4: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "febec8ac", "metadata": {}, "outputs": [], "source": [ "# 1. Get the SOFT and matrix file paths again \n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "print(f\"Matrix file found: {matrix_file}\")\n", "\n", "# 2. Use the get_genetic_data function from the library to get the gene_data\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " \n", " # 3. Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "8c255d38", "metadata": {}, "source": [ "### Step 5: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": null, "id": "88b0f606", "metadata": {}, "outputs": [], "source": [ "# Examining the gene identifiers\n", "# The format appears to be Affymetrix probe IDs (format: XXXXX_at)\n", "# These are not standard human gene symbols and need to be mapped\n", "# Affymetrix probe IDs typically need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "8140c9ea", "metadata": {}, "source": [ "### Step 6: Gene Annotation" ] }, { "cell_type": "code", "execution_count": null, "id": "148035e8", "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. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n", "print(\"\\nGene annotation preview:\")\n", "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n", "print(preview_df(gene_annotation, n=5))\n", "\n", "# Check if there are any columns that might contain gene information\n", "sample_row = gene_annotation.iloc[0].to_dict()\n", "print(\"\\nFirst row as dictionary:\")\n", "for col, value in sample_row.items():\n", " print(f\"{col}: {value}\")\n", "\n", "# Check if the SOFT file has the right information for gene mapping\n", "print(\"\\nFurther examination needed - this might be a miRNA dataset or using non-standard annotations\")\n", "print(\"Looking at the index of gene_data to compare with annotation ID format:\")\n", "print(gene_data.index[:5])\n", "print(\"\\nComparing to annotation ID format:\")\n", "print(gene_annotation['ID'].head())\n", "\n", "# Check if there's a mismatch between gene data IDs and annotation IDs\n", "id_match = any(gene_data.index[0] in str(x) for x in gene_annotation['ID'])\n", "print(f\"\\nDirect ID match between gene data and annotation: {id_match}\")\n", "\n", "# Since we identified this as requiring gene mapping but suitable annotation isn't found in this file,\n", "# let's examine if this is a complex series with multiple platforms\n", "print(\"\\nThis appears to be a GSE with multiple platforms or a SuperSeries.\")\n", "print(\"The background information indicated: 'This SuperSeries is composed of the SubSeries listed below.'\")\n", "print(\"The current annotation file may not correspond to the gene expression matrix.\")\n" ] }, { "cell_type": "markdown", "id": "d64dfd51", "metadata": {}, "source": [ "### Step 7: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": null, "id": "d939e07d", "metadata": {}, "outputs": [], "source": [ "# 1. Determine which columns in gene_annotation contain probe IDs and gene symbols\n", "# From the previous output, we can see:\n", "# - 'ID' column in gene_annotation contains identifiers like \"10000_at\" which match gene_data index\n", "# - 'SYMBOL' column contains gene symbols like \"AKT3\"\n", "\n", "# 2. Get a gene mapping dataframe by extracting the two relevant columns\n", "mapping_df = get_gene_mapping(gene_annotation, prob_col=\"ID\", gene_col=\"SYMBOL\")\n", "\n", "print(\"Gene mapping dataframe preview:\")\n", "print(mapping_df.head())\n", "print(f\"Shape of mapping dataframe: {mapping_df.shape}\")\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", "\n", "print(\"After mapping, gene expression data preview:\")\n", "print(f\"Shape of gene expression data: {gene_data.shape}\")\n", "print(f\"First 10 gene symbols: {gene_data.index[:10].tolist()}\")\n", "\n", "# Normalize gene symbols in the index\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "\n", "print(\"After normalizing gene symbols:\")\n", "print(f\"Shape of normalized gene expression data: {gene_data.shape}\")\n", "print(f\"First 10 normalized gene symbols: {gene_data.index[:10].tolist()}\")\n", "\n", "# Save the gene expression data to a CSV file\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": "850a690a", "metadata": {}, "source": [ "### Step 8: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": null, "id": "6d628ecf", "metadata": {}, "outputs": [], "source": [ "# 1. We already normalized gene symbols in the gene expression data in step 7\n", "# Load the normalized gene data that was saved in the previous step\n", "gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n", "print(f\"Loaded gene expression data with shape: {gene_data.shape}\")\n", "\n", "# Reload the background and clinical data from the matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\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", "# 2. Link the clinical and genetic data\n", "# Define conversion functions:\n", "def convert_trait(value):\n", " \"\"\"Convert bipolar disorder status to binary format.\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " value = value.lower()\n", " # Extract the value after the colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if \"bipolar disorder\" in value:\n", " return 1 # Has bipolar disorder\n", " elif value in [\"control\", \"schizophrenia\", \"major depressive disorder\"]:\n", " return 0 # Control/other diagnosis\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age data to continuous format.\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " # Extract the value after the colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " try:\n", " return float(value)\n", " except:\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender data to binary format (0 for female, 1 for male).\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " # Extract the value after the colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " value = value.lower()\n", " if value == \"f\":\n", " return 0\n", " elif value == \"m\":\n", " return 1\n", " return None\n", "\n", "# Get clinical data using the correct row indices\n", "selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=2, # Based on output showing diagnosis in row 2\n", " convert_trait=convert_trait,\n", " age_row=8, # Age data is in row 8 based on first step output\n", " convert_age=convert_age,\n", " gender_row=6, # Gender data is in row 6 based on first step output\n", " convert_gender=convert_gender\n", ")\n", "\n", "print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n", "print(\"Clinical data preview:\")\n", "print(preview_df(selected_clinical_df))\n", "\n", "# Save clinical data for future reference\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", "# Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "print(\"Linked data preview (first 5 rows, 5 columns):\")\n", "print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Linked data is empty\")\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. Check for bias in features\n", "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 5. Validate and save cohort information\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 brain samples with diagnoses including Bipolar Disorder, Major Depressive Disorder, Schizophrenia, and Control.\"\n", ")\n", "\n", "# 6. Save the 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 analysis. No linked data file saved.\")" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }