{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "f686e699", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:35.078946Z", "iopub.status.busy": "2025-03-25T08:31:35.078626Z", "iopub.status.idle": "2025-03-25T08:31:35.242332Z", "shell.execute_reply": "2025-03-25T08:31:35.242015Z" } }, "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 = \"GSE275334\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/COVID-19\"\n", "in_cohort_dir = \"../../input/GEO/COVID-19/GSE275334\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/COVID-19/GSE275334.csv\"\n", "out_gene_data_file = \"../../output/preprocess/COVID-19/gene_data/GSE275334.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/COVID-19/clinical_data/GSE275334.csv\"\n", "json_path = \"../../output/preprocess/COVID-19/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "0ddfeb03", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "95216831", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:35.243705Z", "iopub.status.busy": "2025-03-25T08:31:35.243565Z", "iopub.status.idle": "2025-03-25T08:31:35.258937Z", "shell.execute_reply": "2025-03-25T08:31:35.258680Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Immune Exhaustion in ME/CFS and long COVID\"\n", "!Series_summary\t\"Gene expression analysis of RNA was performed using the commercially available NanoString® nCounter Immune Exhaustion gene expression panel (NanoString Technologies, Seattle, WA, USA). This panel contains 785 genes to elucidate mechanisms behind T cell, B cell and NK cell exhaustion in disease.\"\n", "!Series_summary\t\"Ribonucleic acid (RNA) was extracted from peripheral blood mononuclear cells (PBMCs) isolated from ME/CFS (n=14), long COVID (n=15), and healthy control (HC; n=18) participants. ME/CFS participants were included according to Canadian Consensus Criteria for ME. Long COVID participants were eligible according to the working case definition for Post COVID-19 Condition published by the World Health Organization.\"\n", "!Series_overall_design\t\"Raw gene expression data was normalised against positive and negative controls to account for background noise and platform-associated variation. Normalisation and analysis were performed using Rosalind Bio (San Diego, CA, USA) using geometric means of housekeeping genes (ABCF1, ALAS1, EEF1G, G6PD, GAPDH, GUSB, HPRT1, OAZ1, POLR1B, POLR2A, PPIA, RPL19, SDHA, TBP, TUBB) (Supplementary Material 1). Differential expression is reported between ME/CFS and long COVID with HC.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['cell type: PBMCs'], 1: ['age (years): 24', 'age (years): 46', 'age (years): 50', 'age (years): 37', 'age (years): 19', 'age (years): 40', 'age (years): 63', 'age (years): 54', 'age (years): 48', 'age (years): 34', 'age (years): 22', 'age (years): 59', 'age (years): 39', 'age (years): 27', 'age (years): 61', 'age (years): 38', 'age (years): 44', 'age (years): 41', 'age (years): 49', 'age (years): 43', 'age (years): 62', 'age (years): 30', 'age (years): 47', 'age (years): 53', 'age (years): 29', 'age (years): 32', 'age (years): 55', 'age (years): 51', 'age (years): 31', 'age (years): 60'], 2: ['Sex: Female', 'Sex: Male'], 3: ['disease: Healthy control', 'disease: Long COVID', 'disease: ME/CFS']}\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": "a0d7d779", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "b87dc85e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:35.259876Z", "iopub.status.busy": "2025-03-25T08:31:35.259774Z", "iopub.status.idle": "2025-03-25T08:31:35.270366Z", "shell.execute_reply": "2025-03-25T08:31:35.270097Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Data Preview:\n", "{'GSM8475033': [0.0, 24.0, 0.0], 'GSM8475034': [0.0, 46.0, 0.0], 'GSM8475035': [0.0, 50.0, 0.0], 'GSM8475036': [0.0, 37.0, 1.0], 'GSM8475037': [0.0, 19.0, 0.0], 'GSM8475038': [0.0, 40.0, 0.0], 'GSM8475039': [0.0, 46.0, 1.0], 'GSM8475040': [0.0, 63.0, 0.0], 'GSM8475041': [0.0, 54.0, 0.0], 'GSM8475042': [0.0, 46.0, 0.0], 'GSM8475043': [0.0, 48.0, 0.0], 'GSM8475044': [0.0, 34.0, 1.0], 'GSM8475045': [0.0, 22.0, 1.0], 'GSM8475046': [0.0, 59.0, 0.0], 'GSM8475047': [0.0, 39.0, 0.0], 'GSM8475048': [0.0, 27.0, 0.0], 'GSM8475049': [0.0, 61.0, 0.0], 'GSM8475050': [0.0, 38.0, 1.0], 'GSM8475051': [1.0, 44.0, 0.0], 'GSM8475052': [1.0, 41.0, 1.0], 'GSM8475053': [1.0, 49.0, 0.0], 'GSM8475054': [1.0, 19.0, 0.0], 'GSM8475055': [1.0, 38.0, 0.0], 'GSM8475056': [1.0, 43.0, 0.0], 'GSM8475057': [1.0, 62.0, 0.0], 'GSM8475058': [1.0, 30.0, 0.0], 'GSM8475059': [1.0, 59.0, 0.0], 'GSM8475060': [1.0, 40.0, 1.0], 'GSM8475061': [1.0, 61.0, 1.0], 'GSM8475062': [1.0, 47.0, 0.0], 'GSM8475063': [1.0, 59.0, 1.0], 'GSM8475064': [1.0, 37.0, 0.0], 'GSM8475065': [1.0, 53.0, 0.0], 'GSM8475066': [0.0, 30.0, 0.0], 'GSM8475067': [0.0, 29.0, 0.0], 'GSM8475068': [0.0, 48.0, 0.0], 'GSM8475069': [0.0, 32.0, 0.0], 'GSM8475070': [0.0, 55.0, 0.0], 'GSM8475071': [0.0, 51.0, 1.0], 'GSM8475072': [0.0, 48.0, 0.0], 'GSM8475073': [0.0, 31.0, 0.0], 'GSM8475074': [0.0, 60.0, 0.0], 'GSM8475075': [0.0, 24.0, 0.0], 'GSM8475076': [0.0, 47.0, 0.0], 'GSM8475077': [0.0, 20.0, 0.0], 'GSM8475078': [0.0, 42.0, 1.0], 'GSM8475079': [0.0, 41.0, 1.0]}\n", "Clinical data saved to ../../output/preprocess/COVID-19/clinical_data/GSE275334.csv\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import json\n", "from typing import Optional, Callable, Dict, Any\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset appears to contain gene expression data\n", "# from NanoString nCounter Immune Exhaustion gene expression panel, which includes 785 genes.\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# From the sample characteristics dictionary:\n", "# - trait (COVID-19) can be inferred from \"disease\" in row 3\n", "# - age is available in row 1\n", "# - gender/sex is available in row 2\n", "trait_row = 3\n", "age_row = 1\n", "gender_row = 2\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value: str) -> int:\n", " \"\"\"\n", " Convert trait values to binary (0 or 1).\n", " For this dataset, we're looking for COVID-19 which maps to \"Long COVID\".\n", " \"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # Convert to binary based on whether it's \"Long COVID\" or not\n", " if \"long covid\" in value.lower():\n", " return 1\n", " else:\n", " return 0\n", "\n", "def convert_age(value: str) -> float:\n", " \"\"\"\n", " Convert age values to continuous (float).\n", " \"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after colon if present\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: str) -> int:\n", " \"\"\"\n", " Convert gender values to binary (0 for female, 1 for male).\n", " \"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " if value.lower() == \"female\":\n", " return 0\n", " elif value.lower() == \"male\":\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "# Initial filtering on usability\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", " # Using the clinical_data variable that should be available from previous steps\n", " try:\n", " # Extract clinical features using the pre-existing clinical_data DataFrame\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(\"Clinical Data Preview:\")\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", " except Exception as e:\n", " print(f\"Error extracting clinical features: {e}\")\n" ] }, { "cell_type": "markdown", "id": "ca6318a9", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "323f70ed", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:35.271233Z", "iopub.status.busy": "2025-03-25T08:31:35.271132Z", "iopub.status.idle": "2025-03-25T08:31:35.284994Z", "shell.execute_reply": "2025-03-25T08:31:35.284735Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/COVID-19/GSE275334/GSE275334_family.soft.gz\n", "Matrix file: ../../input/GEO/COVID-19/GSE275334/GSE275334_series_matrix.txt.gz\n", "Found the matrix table marker at line 65\n", "Gene data shape: (635, 47)\n", "First 20 gene/probe identifiers:\n", "['ACACA', 'ACADVL', 'ACAT2', 'ACOT1/2', 'ACSL3', 'ACSL4', 'ACSL6', 'ADORA2A', 'ADORA2B', 'AHR', 'AIFM1', 'AK4', 'AKT1', 'AKT2', 'AKT3', 'ALDH1A1', 'ALDH1B1', 'ALDOA', 'ALOX5', 'ANAPC4']\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": "95b737f2", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "41a6d09a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:35.285932Z", "iopub.status.busy": "2025-03-25T08:31:35.285831Z", "iopub.status.idle": "2025-03-25T08:31:35.287480Z", "shell.execute_reply": "2025-03-25T08:31:35.287221Z" } }, "outputs": [], "source": [ "# Based on the first 20 identifiers shown, these appear to be human gene symbols.\n", "# The identifiers are in the format of human gene symbols like ACACA, ACADVL, etc.\n", "# There is a mix of standard gene symbols and some combined identifiers (e.g., ACOT1/2)\n", "# but overall these are human gene symbols and don't require mapping.\n", "\n", "requires_gene_mapping = False\n" ] }, { "cell_type": "markdown", "id": "c481bb08", "metadata": {}, "source": [ "### Step 5: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 6, "id": "a7672867", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:35.288428Z", "iopub.status.busy": "2025-03-25T08:31:35.288330Z", "iopub.status.idle": "2025-03-25T08:31:35.430080Z", "shell.execute_reply": "2025-03-25T08:31:35.429743Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data saved to ../../output/preprocess/COVID-19/gene_data/GSE275334.csv\n", "Loaded clinical data shape: (3, 47)\n", "Clinical features columns after transformation: ['COVID-19', 'Age', 'Gender']\n", "Initial linked data shape: (47, 638)\n", "Linked data shape after handling missing values: (46, 638)\n", "For the feature 'COVID-19', the least common label is '1.0' with 15 occurrences. This represents 32.61% of the dataset.\n", "The distribution of the feature 'COVID-19' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: 34.75\n", " 50% (Median): 43.5\n", " 75%: 50.75\n", "Min: 19.0\n", "Max: 63.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '1.0' with 12 occurrences. This represents 26.09% 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/GSE275334.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols and prepare for linking\n", "try:\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 gene data\n", " gene_data.to_csv(out_gene_data_file)\n", " print(f\"Gene data saved to {out_gene_data_file}\")\n", " \n", " # Attempt to link clinical and gene data\n", " if 'trait_row' in locals() and trait_row is not None:\n", " # Load clinical data from the previous step\n", " try:\n", " clinical_features = pd.read_csv(out_clinical_data_file)\n", " print(f\"Loaded clinical data shape: {clinical_features.shape}\")\n", " \n", " # Convert clinical_features to the correct format for linking\n", " clinical_features.set_index(clinical_features.columns[0], inplace=True)\n", " clinical_features = clinical_features.T\n", " clinical_features.columns = [trait, 'Age', 'Gender']\n", " \n", " print(\"Clinical features columns after transformation:\", clinical_features.columns.tolist())\n", " \n", " # Link the clinical and genetic data\n", " linked_data = pd.concat([clinical_features, gene_data.T], axis=1)\n", " print(f\"Initial linked data shape: {linked_data.shape}\")\n", " \n", " # Handle 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", " if linked_data.shape[0] > 0:\n", " # Check for bias in trait and demographic features\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # Validate data quality 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,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=\"Successfully processed gene expression data for COVID-19.\"\n", " )\n", " \n", " # Save the linked data if it's 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(\"Data not usable for trait study - not saving final linked data.\")\n", " else:\n", " print(\"After handling missing values, no samples remain.\")\n", " 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,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"No valid samples after handling missing values.\"\n", " )\n", " except Exception as e:\n", " print(f\"Error loading or processing clinical data: {e}\")\n", " # Try to create a minimal response in case of error\n", " 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=False,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=f\"Error processing clinical data: {str(e)}\"\n", " )\n", " else:\n", " # Cannot proceed with linking if trait data is missing\n", " 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=False,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"Cannot link data because trait information is not available.\"\n", " )\n", "except Exception as e:\n", " print(f\"Error in data processing: {e}\")\n", " \n", " # Log the error and mark the dataset as unusable\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=False,\n", " is_trait_available=False,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=f\"Error during normalization or linking: {str(e)}\"\n", " )" ] } ], "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 }