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{
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"cell_type": "code",
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"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": {
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"name": "stdout",
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"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": {
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"shell.execute_reply": "2025-03-25T08:31:31.551236Z"
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"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",
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"execution": {
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"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"
]
},
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"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,
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{
"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.\")"
]
}
],
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