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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
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"metadata": {
"execution": {
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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 = \"GSE211378\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/COVID-19\"\n",
"in_cohort_dir = \"../../input/GEO/COVID-19/GSE211378\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/COVID-19/GSE211378.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/COVID-19/gene_data/GSE211378.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/COVID-19/clinical_data/GSE211378.csv\"\n",
"json_path = \"../../output/preprocess/COVID-19/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "d7caed5d",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c571c381",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Whole Blood profiling of COVID convalescent and Healthy donors with nCounter\"\n",
"!Series_summary\t\"This study investigated the cellular immune response in people who had recovered from SARS-CoV2 infection (COVID-19).\"\n",
"!Series_overall_design\t\"264 Whole Blood samples from 160 COVID convalescent donors, and 40 from Healthy donors.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['sample.id: host response panel 1 wb', 'sample.id: host response panel 2 wb', 'sample.id: host response panel 3 wb', 'sample.id: host response panel 4 wb', 'sample.id: host response panel plate 25 wb 11292020', 'sample.id: host response panel plate 5 wb 11 19 2020', 'sample.id: host response panel plate 6 wb 11192020', 'sample.id: host response panel plate 7 wb 11 20 2020', 'sample.id: host response panel plate 8 wb 11202020', 'sample.id: host response panel plate 26 wb 11302020', 'sample.id: host response panel plate 9 wb 11212020', 'sample.id: host response panel plate 10 wb 11212020', 'sample.id: host response panel plate 11 wb 11222020', 'sample.id: host response panel plate 12 wb 11222020', 'sample.id: host response panel plate 13 11232020', 'sample.id: host response panel plate 14 wb 11232020', 'sample.id: host response panel plate 15 wb 11242020', 'sample.id: host response panel plate 16 wb 11242020', 'sample.id: host response panel plate 17 wb 11252020', 'sample.id: host response panel plate 18 wb 11252020', 'sample.id: host response panel plate 19 wb 11262020', 'sample.id: host response panel plate 20 wb 11262020', 'sample.id: host response panel plate 21 wb 11272020', 'sample.id: host response panel plate 22 wb 11272020', 'sample.id: host response panel plate 23 11282020', 'sample.id: host response panel plate 24 wb 11292020'], 1: ['date: 20201018', 'date: 20201019', 'date: 20201129', 'date: 20201119', 'date: 20201120', 'date: 20201130', 'date: 20201121', 'date: 20201122', 'date: 20201123', 'date: 20201124', 'date: 20201125', 'date: 20201126', 'date: 20201127', 'date: 20201128'], 2: ['generlf: NS_Hs_HostResponse_v1.0'], 3: ['systemapf: n6_vDV1'], 4: ['lane.number: 1', 'lane.number: 2', 'lane.number: 3', 'lane.number: 4', 'lane.number: 5', 'lane.number: 6', 'lane.number: 7', 'lane.number: 8', 'lane.number: 9', 'lane.number: 10', 'lane.number: 11', 'lane.number: 12'], 5: ['fovcount: 555'], 6: ['fovcounted: 551', 'fovcounted: 549', 'fovcounted: 544', 'fovcounted: 535', 'fovcounted: 546', 'fovcounted: 541', 'fovcounted: 540', 'fovcounted: 538', 'fovcounted: 532', 'fovcounted: 543', 'fovcounted: 536', 'fovcounted: 537', 'fovcounted: 534', 'fovcounted: 542', 'fovcounted: 545', 'fovcounted: 528', 'fovcounted: 547', 'fovcounted: 526', 'fovcounted: 550', 'fovcounted: 554', 'fovcounted: 552', 'fovcounted: 539', 'fovcounted: 530', 'fovcounted: 548', 'fovcounted: 553', 'fovcounted: 555', 'fovcounted: 515', 'fovcounted: 522', 'fovcounted: 521', 'fovcounted: 533'], 7: ['scannerid: 1906C0614'], 8: ['stageposition: 1', 'stageposition: 2'], 9: ['bindingdensity: 0.92', 'bindingdensity: 1.1', 'bindingdensity: 1.52', 'bindingdensity: 1.75', 'bindingdensity: 1.94', 'bindingdensity: 2.49', 'bindingdensity: 1.98', 'bindingdensity: 1.69', 'bindingdensity: 1.44', 'bindingdensity: 2.91', 'bindingdensity: 1.81', 'bindingdensity: 2.18', 'bindingdensity: 1.82', 'bindingdensity: 1.72', 'bindingdensity: 2.09', 'bindingdensity: 1.66', 'bindingdensity: 1.87', 'bindingdensity: 1.51', 'bindingdensity: 2.27', 'bindingdensity: 2.51', 'bindingdensity: 1.88', 'bindingdensity: 2.15', 'bindingdensity: 2.1', 'bindingdensity: 1.54', 'bindingdensity: 1.33', 'bindingdensity: 1.04', 'bindingdensity: 1.45', 'bindingdensity: 1.63', 'bindingdensity: 1.7', 'bindingdensity: 3.1'], 10: ['cartridgeid: host response panel wb 1', 'cartridgeid: host response panel 2 wb', 'cartridgeid: host response panel 3 wb', 'cartridgeid: host response panel 4 wb', 'cartridgeid: host response panel plate 25 wb 11292020', 'cartridgeid: host response panel plate 5 wb 11 19 2020', 'cartridgeid: host response panel plate 6 wb 11 19 2020', 'cartridgeid: host response panel plate 7 wb 11 20 2020', 'cartridgeid: host response panel plate 8 wb 11202020', 'cartridgeid: host response panel plate 26 wb 11302020', 'cartridgeid: host response panel plate 9 wb 11212020', 'cartridgeid: host response panel plate 10 wb 11212020', 'cartridgeid: host response panel plate 11 wb 11222020', 'cartridgeid: host response panel plate 12 wb 11222020', 'cartridgeid: host response panel plate 13 wb 11232020', 'cartridgeid: host response panel plate 14 wb 11232020', 'cartridgeid: host response panel plate 15 wb 11242020', 'cartridgeid: host response panel plate 16 wb 11242020', 'cartridgeid: host response panel plate 17 wb 11252020', 'cartridgeid: host response panel plate 18 wb 11252020', 'cartridgeid: host response panel plate 19 wb 11262020', 'cartridgeid: host response panel plate 20 wb 11262020', 'cartridgeid: host response panel plate 21 wb 11272020', 'cartridgeid: host response panel plate 22 wb 11272020', 'cartridgeid: host response panel plate 23 wb 11282020', 'cartridgeid: host response panel plate 24 wb 11292020'], 11: ['cartridgebarcode: NA'], 12: ['nanostring_id: 12590', 'nanostring_id: 12591_51', 'nanostring_id: 12645_21', 'nanostring_id: 12650', 'nanostring_id: 12672', 'nanostring_id: 12688_41', 'nanostring_id: 12693_21', 'nanostring_id: 12694_21', 'nanostring_id: 12700_21', 'nanostring_id: 12707_31', 'nanostring_id: 12708 _51', 'nanostring_id: 12709_21', 'nanostring_id: 12721', 'nanostring_id: 12726_21', 'nanostring_id: 12727', 'nanostring_id: 12733', 'nanostring_id: 12736', 'nanostring_id: 12745', 'nanostring_id: 12751_41', 'nanostring_id: 12766', 'nanostring_id: 12772_31', 'nanostring_id: 12774', 'nanostring_id: 12781', 'nanostring_id: 12786_21', 'nanostring_id: 12792_21', 'nanostring_id: 12812_31', 'nanostring_id: 12830_51', 'nanostring_id: 12862_21', 'nanostring_id: 12889_31', 'nanostring_id: 12896_52']}\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": "b3bdde7f",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "56c3816e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:30:17.463571Z",
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"shell.execute_reply": "2025-03-25T08:30:17.486220Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"is_gene_available = True # The background suggests this is gene expression profiling data\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Data Availability\n",
"# There are no clear clinical data columns in the sample characteristics\n",
"# From the background information: \"264 Whole Blood samples from 160 COVID convalescent donors, \n",
"# and 40 from Healthy donors.\" implies there is COVID-19 status information\n",
"# However, it's not available in the sample characteristics dictionary\n",
"trait_row = None # No clear trait information in characteristics\n",
"age_row = None # No age information found\n",
"gender_row = None # No gender information found\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" # Function to convert COVID-19 status, though not used in this dataset\n",
" if value is None:\n",
" return None\n",
" \n",
" value = value.lower().split(': ')[-1].strip()\n",
" if 'covid' in value or 'convalescent' in value or 'infected' in value or 'positive' in value:\n",
" return 1\n",
" elif 'healthy' in value or 'control' in value or 'negative' in value:\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" # Function to convert age to continuous values, though not used in this dataset\n",
" if value is None:\n",
" return None\n",
" \n",
" try:\n",
" # Extract the value after the colon and convert to float\n",
" age_value = value.split(': ')[-1].strip()\n",
" return float(age_value)\n",
" except (ValueError, AttributeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # Function to convert gender to binary, though not used in this dataset\n",
" if value is None:\n",
" return None\n",
" \n",
" value = value.lower().split(': ')[-1].strip()\n",
" if value in ['female', 'f', 'woman']:\n",
" return 0\n",
" elif value in ['male', 'm', 'man']:\n",
" return 1\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Initial validation - check if the dataset has both gene and trait data\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=(trait_row is not None)\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"# Since trait_row is None, we skip the clinical feature extraction step\n"
]
},
{
"cell_type": "markdown",
"id": "b7805f8e",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d21ce447",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:30:17.487587Z",
"iopub.status.busy": "2025-03-25T08:30:17.487482Z",
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"shell.execute_reply": "2025-03-25T08:30:17.537762Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOFT file: ../../input/GEO/COVID-19/GSE211378/GSE211378_family.soft.gz\n",
"Matrix file: ../../input/GEO/COVID-19/GSE211378/GSE211378_series_matrix.txt.gz\n",
"Found the matrix table marker at line 84\n",
"Gene data shape: (773, 304)\n",
"First 20 gene/probe identifiers:\n",
"['ACE', 'ACKR2', 'ACKR3', 'ACKR4', 'ACOX1', 'ACSL1', 'ACSL3', 'ACSL4', 'ACVR1', 'ADAR', 'ADGRE5', 'ADGRG3', 'ADORA2A', 'AGT', 'AHR', 'AIF1', 'AIM2', 'AKT1', 'AKT2', 'AKT3']\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": "e6399076",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c3f4cf66",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:30:17.539166Z",
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"shell.execute_reply": "2025-03-25T08:30:17.540516Z"
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},
"outputs": [],
"source": [
"# Review the gene identifiers based on biomedical knowledge\n",
"# The identifiers appear to be standard human gene symbols (ACE, ACKR2, AKT1, etc.)\n",
"# These are official HGNC gene symbols and do not need to be mapped\n",
"\n",
"requires_gene_mapping = False\n"
]
},
{
"cell_type": "markdown",
"id": "b167cfa2",
"metadata": {},
"source": [
"### Step 5: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8759dfa4",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:30:17.541818Z",
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"shell.execute_reply": "2025-03-25T08:30:17.772451Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape after normalization: (758, 304)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/COVID-19/gene_data/GSE211378.csv\n",
"Empty clinical features saved to ../../output/preprocess/COVID-19/clinical_data/GSE211378.csv\n",
"Abnormality detected in the cohort: GSE211378. Preprocessing failed.\n",
"Linked data not created or saved due to missing trait information.\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",
"# Create empty clinical data for record-keeping purposes\n",
"empty_clinical_df = pd.DataFrame()\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 empty clinical features\n",
"empty_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Empty clinical features saved to {out_clinical_data_file}\")\n",
"\n",
"# Since we don't have trait data, we can't perform:\n",
"# - linking with gene data\n",
"# - handling missing values\n",
"# - determining trait bias\n",
"# - creating usable linked data\n",
"\n",
"# For validation purposes, mark the dataset as unusable due to lack of trait data\n",
"is_trait_available = False # No trait data available as determined in Step 2\n",
"is_biased = True # Mark as biased (unusable) since we can't analyze trait-related bias\n",
"\n",
"# Validate and save cohort info - mark as unusable due to missing trait data\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=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=empty_clinical_df, # Use empty DataFrame as placeholder\n",
" note=\"Dataset contains gene expression data but lacks necessary trait information for COVID-19 analysis.\"\n",
")\n",
"\n",
"print(\"Linked data not created or saved due to missing trait information.\")"
]
}
],
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