{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "73691dc0", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:31.346254Z", "iopub.status.busy": "2025-03-25T08:31:31.346017Z", "iopub.status.idle": "2025-03-25T08:31:31.516536Z", "shell.execute_reply": "2025-03-25T08:31:31.516141Z" } }, "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 = \"COVID-19\"\n", "cohort = \"GSE227080\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/COVID-19\"\n", "in_cohort_dir = \"../../input/GEO/COVID-19/GSE227080\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/COVID-19/GSE227080.csv\"\n", "out_gene_data_file = \"../../output/preprocess/COVID-19/gene_data/GSE227080.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/COVID-19/clinical_data/GSE227080.csv\"\n", "json_path = \"../../output/preprocess/COVID-19/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "1e1ef1bf", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "f223171d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:31.518009Z", "iopub.status.busy": "2025-03-25T08:31:31.517847Z", "iopub.status.idle": "2025-03-25T08:31:31.544659Z", "shell.execute_reply": "2025-03-25T08:31:31.544327Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Early differentially expressed immunological genes in mild and severe COVID-19\"\n", "!Series_summary\t\"We retrospectively analysed the expression of 579 immunological genes in 60 COVID-19 subjects (SARS +ve) and 59 COVID-negative (SARS -ve) subjects using the NanoString nCounter (Immunology panel), a technology based on multiplexed single-molecule counting. Biobanked Human peripheral blood mononuclear cells (PBMCs) samples underwent Nucleic Acid extraction and digital detection of mRNA to evaluate changes in antiviral gene expression between SARS -ve controls and patients with mild (SARS +ve Mild) and moderate/severe (SARS +ve Mod/Sev) disease.\"\n", "!Series_overall_design\t\"119 samples (60 SARS-CoV-2 positive / 59 SARS-CoV-2 negative)\"\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: F', 'gender: M'], 1: ['age: 38', 'age: 66', 'age: 21', 'age: 29', 'age: 73', 'age: 35', 'age: 48', 'age: 70', 'age: 69', 'age: 31', 'age: 72', 'age: 41', 'age: 85', 'age: 79', 'age: 46', 'age: 57', 'age: 87', 'age: 52', 'age: 36', 'age: 77', 'age: 82', 'age: 89', 'age: 94', 'age: 54', 'age: 23', 'age: 61', 'age: 75', 'age: 25', 'age: 43', 'age: 24'], 2: ['severity: MILD', 'severity: MOD_SEV', 'severity: NEG']}\n" ] } ], "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": "79392c3f", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "800842fc", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:31.545953Z", "iopub.status.busy": "2025-03-25T08:31:31.545836Z", "iopub.status.idle": "2025-03-25T08:31:31.551564Z", "shell.execute_reply": "2025-03-25T08:31:31.551236Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing clinical data with sample characteristics\n", "Trait row: 2, Age row: 1, Gender row: 0\n", "Clinical data is available for processing. Trait data (2): ['severity: MILD', 'severity: MOD_SEV', 'severity: NEG']\n", "Age data (1): ['age: 38', 'age: 66', 'age: 21', 'age: 29', 'age: 73']...\n", "Gender data (0): ['gender: F', 'gender: M']\n", "Clinical feature extraction will be completed when the full dataset is available.\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n", "import json\n", "from typing import Optional, Callable, Dict, Any, List\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains expression data of 579 immunological genes\n", "# This is gene expression data, not miRNA or methylation data\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "trait_row = 2 # \"severity\" indicates COVID-19 severity status\n", "age_row = 1 # Age information is available\n", "gender_row = 0 # Gender information is available\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value: str) -> int:\n", " \"\"\"Convert COVID-19 severity trait to binary (0 for negative/mild, 1 for moderate/severe)\"\"\"\n", " if not value or ':' not in value:\n", " return None\n", " severity = value.split(':', 1)[1].strip().upper()\n", " if severity == 'NEG': # COVID-negative\n", " return 0\n", " elif severity == 'MILD': # Mild COVID\n", " return 0\n", " elif severity == 'MOD_SEV': # Moderate/Severe COVID\n", " return 1\n", " return None\n", "\n", "def convert_age(value: str) -> float:\n", " \"\"\"Convert age string to float\"\"\"\n", " if not value or ':' not in value:\n", " return None\n", " age_str = value.split(':', 1)[1].strip()\n", " try:\n", " return float(age_str)\n", " except ValueError:\n", " return None\n", "\n", "def convert_gender(value: str) -> int:\n", " \"\"\"Convert gender string to binary (0 for female, 1 for male)\"\"\"\n", " if not value or ':' not in value:\n", " return None\n", " gender = value.split(':', 1)[1].strip().upper()\n", " if gender == 'F':\n", " return 0\n", " elif gender == 'M':\n", " return 1\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Initial filtering and saving 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", "# 4. Clinical Feature Extraction\n", "# Since trait_row is not None, we need to extract clinical features\n", "if trait_row is not None:\n", " # Create a DataFrame from the sample characteristics dictionary\n", " # This simulates clinical data based on the provided sample characteristics\n", " sample_characteristics = {\n", " 0: ['gender: F', 'gender: M'], \n", " 1: ['age: 38', 'age: 66', 'age: 21', 'age: 29', 'age: 73', 'age: 35', 'age: 48', 'age: 70', 'age: 69', 'age: 31', 'age: 72', 'age: 41', 'age: 85', 'age: 79', 'age: 46', 'age: 57', 'age: 87', 'age: 52', 'age: 36', 'age: 77', 'age: 82', 'age: 89', 'age: 94', 'age: 54', 'age: 23', 'age: 61', 'age: 75', 'age: 25', 'age: 43', 'age: 24'], \n", " 2: ['severity: MILD', 'severity: MOD_SEV', 'severity: NEG']\n", " }\n", " \n", " # We don't have the actual samples yet, so we'll create placeholder sample IDs\n", " # The actual clinical_data processing will happen later when we have the full data\n", " print(\"Processing clinical data with sample characteristics\")\n", " print(f\"Trait row: {trait_row}, Age row: {age_row}, Gender row: {gender_row}\")\n", " \n", " # Since we're just doing initial validation at this point, we'll note that clinical data\n", " # is available but requires further processing in subsequent steps\n", " print(f\"Clinical data is available for processing. Trait data ({trait_row}): {sample_characteristics[trait_row]}\")\n", " print(f\"Age data ({age_row}): {sample_characteristics[age_row][:5]}...\")\n", " print(f\"Gender data ({gender_row}): {sample_characteristics[gender_row]}\")\n", " \n", " # Note: We need the actual GEO data matrix with sample IDs to properly extract clinical features\n", " # This will be done in a later step when we have access to the complete dataset\n", " print(\"Clinical feature extraction will be completed when the full dataset is available.\")\n" ] }, { "cell_type": "markdown", "id": "dbfaa3fa", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "4c05bce2", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:31.552806Z", "iopub.status.busy": "2025-03-25T08:31:31.552693Z", "iopub.status.idle": "2025-03-25T08:31:31.572345Z", "shell.execute_reply": "2025-03-25T08:31:31.572010Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/COVID-19/GSE227080/GSE227080_family.soft.gz\n", "Matrix file: ../../input/GEO/COVID-19/GSE227080/GSE227080_series_matrix.txt.gz\n", "Found the matrix table marker at line 63\n", "Gene data shape: (579, 119)\n", "First 20 gene/probe identifiers:\n", "['ABCB1', 'ABL1', 'ADA', 'AHR', 'AICDA', 'AIRE', 'APP', 'ARG1', 'ARG2', 'ARHGDIB', 'ATG10', 'ATG12', 'ATG16L1', 'ATG5', 'ATG7', 'ATM', 'B2M', 'B3GAT1', 'BATF', 'BATF3']\n" ] } ], "source": [ "# 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", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# Set gene availability flag\n", "is_gene_available = True # Initially assume gene data is available\n", "\n", "# First check if the matrix file contains the expected marker\n", "found_marker = False\n", "marker_row = None\n", "try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for i, line in enumerate(file):\n", " if \"!series_matrix_table_begin\" in line:\n", " found_marker = True\n", " marker_row = i\n", " print(f\"Found the matrix table marker at line {i}\")\n", " break\n", " \n", " if not found_marker:\n", " print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n", " is_gene_available = False\n", " \n", " # If marker was found, try to extract gene data\n", " if is_gene_available:\n", " try:\n", " # Try using the library function\n", " gene_data = get_genetic_data(matrix_file)\n", " \n", " if gene_data.shape[0] == 0:\n", " print(\"Warning: Extracted gene data has 0 rows.\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " # Print the first 20 gene/probe identifiers\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20].tolist())\n", " except Exception as e:\n", " print(f\"Error extracting gene data with get_genetic_data(): {e}\")\n", " is_gene_available = False\n", " \n", " # If gene data extraction failed, examine file content to diagnose\n", " if not is_gene_available:\n", " print(\"Examining file content to diagnose the issue:\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Print lines around the marker if found\n", " if marker_row is not None:\n", " for i, line in enumerate(file):\n", " if i >= marker_row - 2 and i <= marker_row + 10:\n", " print(f\"Line {i}: {line.strip()[:100]}...\")\n", " if i > marker_row + 10:\n", " break\n", " else:\n", " # If marker not found, print first 10 lines\n", " for i, line in enumerate(file):\n", " if i < 10:\n", " print(f\"Line {i}: {line.strip()[:100]}...\")\n", " else:\n", " break\n", " except Exception as e2:\n", " print(f\"Error examining file: {e2}\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing file: {e}\")\n", " is_gene_available = False\n", "\n", "# Update validation information if gene data extraction failed\n", "if not is_gene_available:\n", " print(\"Gene expression data could not be successfully extracted from this dataset.\")\n", " # Update the validation record since gene data isn't available\n", " is_trait_available = False # We already determined trait data isn't available in step 2\n", " validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n", " is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n" ] }, { "cell_type": "markdown", "id": "dcc914f9", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "0ca9df9e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:31.573521Z", "iopub.status.busy": "2025-03-25T08:31:31.573407Z", "iopub.status.idle": "2025-03-25T08:31:31.575226Z", "shell.execute_reply": "2025-03-25T08:31:31.574906Z" } }, "outputs": [], "source": [ "# The gene identifiers appear to be standard human gene symbols (like ABCB1, ABL1, ADA, etc.)\n", "# These are official gene symbols that don't require mapping to other identifiers\n", "\n", "requires_gene_mapping = False\n" ] }, { "cell_type": "markdown", "id": "22fede8b", "metadata": {}, "source": [ "### Step 5: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 6, "id": "21361c86", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:31.576404Z", "iopub.status.busy": "2025-03-25T08:31:31.576291Z", "iopub.status.idle": "2025-03-25T08:31:31.839969Z", "shell.execute_reply": "2025-03-25T08:31:31.839607Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (561, 119)\n", "Normalized gene data saved to ../../output/preprocess/COVID-19/gene_data/GSE227080.csv\n", "Clinical features saved to ../../output/preprocess/COVID-19/clinical_data/GSE227080.csv\n", "Clinical features preview:\n", "{'COVID-19': [0.0, 1.0, 1.0, 0.0, 1.0], 'Age': [38.0, 66.0, 21.0, 29.0, 73.0], 'Gender': [0.0, 1.0, 1.0, 1.0, 1.0]}\n", "Linked data shape: (119, 564)\n", "Linked data shape after handling missing values: (119, 564)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "For the feature 'COVID-19', the least common label is '1.0' with 34 occurrences. This represents 28.57% of the dataset.\n", "The distribution of the feature 'COVID-19' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Quartiles for 'Age':\n", " 25%: 47.0\n", " 50% (Median): 63.0\n", " 75%: 75.0\n", "Min: 21.0\n", "Max: 94.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '0.0' with 44 occurrences. This represents 36.97% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "Linked data saved to ../../output/preprocess/COVID-19/GSE227080.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\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", "# Create output directory if it doesn't exist\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "\n", "# Save the normalized gene data\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. Extract clinical features using the previously identified feature rows\n", "# Use the clinical data from Step 1 and the row identifiers from Step 2\n", "clinical_features = geo_select_clinical_features(\n", " 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", "# Create directory for clinical data output\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "\n", "# Save the clinical features\n", "clinical_features.to_csv(out_clinical_data_file)\n", "print(f\"Clinical features saved to {out_clinical_data_file}\")\n", "\n", "# Preview the clinical features\n", "clinical_features_preview = preview_df(clinical_features.T)\n", "print(\"Clinical features preview:\")\n", "print(clinical_features_preview)\n", "\n", "# 3. Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# 4. Handle missing values in the linked data\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", "\n", "# 5. Determine if trait and demographic features are biased\n", "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 6. 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=is_gene_available,\n", " is_trait_available=True, # We have trait data as identified in Step 2\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=\"Dataset contains gene expression data for COVID-19 severity analysis.\"\n", ")\n", "\n", "# 7. Save the linked data if it's usable\n", "if is_usable:\n", " # Create output directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " \n", " # Save the linked data\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Linked data not saved due to quality issues.\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }