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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ea33696f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:56:07.184862Z",
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"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 = \"Lupus_(Systemic_Lupus_Erythematosus)\"\n",
"cohort = \"GSE193442\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)\"\n",
"in_cohort_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE193442\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE193442.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE193442.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE193442.csv\"\n",
"json_path = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "e5a48629",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cf0623c8",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:56:07.351220Z",
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"shell.execute_reply": "2025-03-25T07:56:07.439436Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Transcriptional profiling of human KIR+ CD8 T cells\"\n",
"!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
"!Series_overall_design\t\"Refer to individual Series\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: PBMC'], 1: ['cell type: KIR+ CD8 T']}\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": "01f09e30",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9527f20a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:56:07.441198Z",
"iopub.status.busy": "2025-03-25T07:56:07.441086Z",
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"shell.execute_reply": "2025-03-25T07:56:07.447324Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Analyze the dataset based on the background information and sample characteristics\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the series title and sample characteristics, this dataset seems to focus on transcriptional profiling\n",
"# of human KIR+ CD8 T cells, which suggests it contains gene expression data.\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# From the sample characteristics, we don't see explicit fields for lupus/SLE status, age, or gender\n",
"# The dataset appears to be focusing only on cell types rather than patient characteristics\n",
"\n",
"# For trait (SLE)\n",
"# No explicit SLE status is provided in the sample characteristics\n",
"trait_row = None # No explicit trait information available\n",
"\n",
"# For age\n",
"# No age information is provided in the sample characteristics\n",
"age_row = None # No age information available\n",
"\n",
"# For gender\n",
"# No gender information is provided in the sample characteristics\n",
"gender_row = None # No gender information available\n",
"\n",
"# 2.2 Data Type Conversion\n",
"# Since we don't have access to these variables, we'll define placeholder conversion functions\n",
"\n",
"def convert_trait(value):\n",
" # Placeholder function since trait data is not available\n",
" if value is None:\n",
" return None\n",
" \n",
" value = value.split(': ')[-1].strip().lower()\n",
" if 'lupus' in value or 'sle' in value:\n",
" return 1\n",
" elif 'control' in value or 'healthy' in value or 'normal' in value:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" # Placeholder function since age data is not available\n",
" if value is None:\n",
" return None\n",
" \n",
" try:\n",
" # Extract value after colon and convert to float\n",
" age_str = value.split(': ')[-1].strip()\n",
" return float(age_str)\n",
" except (ValueError, AttributeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # Placeholder function since gender data is not available\n",
" if value is None:\n",
" return None\n",
" \n",
" value = value.split(': ')[-1].strip().lower()\n",
" if 'female' in value or 'f' == value:\n",
" return 0\n",
" elif 'male' in value or 'm' == value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Check if trait data is available\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save the initial filtering result\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. Since trait_row is None, we skip the clinical feature extraction step\n"
]
},
{
"cell_type": "markdown",
"id": "4f35d3f2",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d51a12ed",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:56:07.448860Z",
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"shell.execute_reply": "2025-03-25T07:56:07.991423Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\n",
"No subseries references found in the first 1000 lines of the SOFT file.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene data extraction result:\n",
"Number of rows: 0\n",
"First 20 gene/probe identifiers:\n",
"Index([], dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. First get the path to the soft and matrix files\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Looking more carefully at the background information\n",
"# This is a SuperSeries which doesn't contain direct gene expression data\n",
"# Need to investigate the soft file to find the subseries\n",
"print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n",
"\n",
"# Open the SOFT file to try to identify subseries\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" subseries_lines = []\n",
" for i, line in enumerate(f):\n",
" if 'Series_relation' in line and 'SuperSeries of' in line:\n",
" subseries_lines.append(line.strip())\n",
" if i > 1000: # Limit search to first 1000 lines\n",
" break\n",
"\n",
"# Display the subseries found\n",
"if subseries_lines:\n",
" print(\"Found potential subseries references:\")\n",
" for line in subseries_lines:\n",
" print(line)\n",
"else:\n",
" print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n",
"\n",
"# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(\"\\nGene data extraction result:\")\n",
" print(\"Number of rows:\", len(gene_data))\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",
" print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")"
]
}
],
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