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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "1aa794af",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:15:08.917302Z",
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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 = \"Chronic_Fatigue_Syndrome\"\n",
"cohort = \"GSE251792\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Chronic_Fatigue_Syndrome\"\n",
"in_cohort_dir = \"../../input/GEO/Chronic_Fatigue_Syndrome/GSE251792\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Chronic_Fatigue_Syndrome/GSE251792.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE251792.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Chronic_Fatigue_Syndrome/clinical_data/GSE251792.csv\"\n",
"json_path = \"../../output/preprocess/Chronic_Fatigue_Syndrome/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "35be2c95",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bf7c698a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:15:09.084546Z",
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Deep phenotyping of Post-infectious Myalgic Encephalomyelitis/Chronic Fatigue Syndrome\"\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: ['Sex: Female', 'Sex: Male'], 1: ['age: 61', 'age: 37', 'age: 56', 'age: 24', 'age: 58', 'age: 43', 'age: 26', 'age: 40', 'age: 47', 'age: 22', 'age: 54', 'age: 44', 'age: 20', 'age: 23', 'age: 33', 'age: 25', 'age: 51', 'age: 48', 'age: 36', 'age: 38', 'age: 60', 'age: 50', 'age: 49', 'age: 55', 'age: 57'], 2: ['group: Patient', 'group: Control']}\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": "165a6ec5",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "26d1b061",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:15:09.109599Z",
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"shell.execute_reply": "2025-03-25T08:15:09.122222Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical features preview: {'GSM7988184': [1.0, 61.0, 0.0], 'GSM7988185': [0.0, 37.0, 1.0], 'GSM7988186': [0.0, 56.0, 0.0], 'GSM7988187': [0.0, 56.0, 0.0], 'GSM7988188': [1.0, 24.0, 0.0], 'GSM7988189': [1.0, 58.0, 1.0], 'GSM7988190': [1.0, 43.0, 1.0], 'GSM7988191': [1.0, 26.0, 0.0], 'GSM7988192': [0.0, 40.0, 1.0], 'GSM7988193': [1.0, 47.0, 1.0], 'GSM7988194': [0.0, 22.0, 0.0], 'GSM7988195': [1.0, 54.0, 0.0], 'GSM7988196': [0.0, 58.0, 1.0], 'GSM7988197': [1.0, 44.0, 0.0], 'GSM7988198': [1.0, 20.0, 0.0], 'GSM7988199': [0.0, 26.0, 1.0], 'GSM7988200': [1.0, 23.0, 0.0], 'GSM7988201': [1.0, 33.0, 1.0], 'GSM7988202': [0.0, 54.0, 0.0], 'GSM7988203': [1.0, 25.0, 0.0], 'GSM7988204': [0.0, 58.0, 1.0], 'GSM7988205': [1.0, 37.0, 1.0], 'GSM7988206': [0.0, 23.0, 1.0], 'GSM7988207': [1.0, 22.0, 1.0], 'GSM7988208': [1.0, 51.0, 0.0], 'GSM7988209': [1.0, 48.0, 1.0], 'GSM7988210': [0.0, 36.0, 1.0], 'GSM7988211': [0.0, 56.0, 0.0], 'GSM7988212': [1.0, 38.0, 0.0], 'GSM7988213': [1.0, 60.0, 1.0], 'GSM7988214': [0.0, 37.0, 0.0], 'GSM7988215': [0.0, 25.0, 0.0], 'GSM7988216': [0.0, 44.0, 1.0], 'GSM7988217': [1.0, 61.0, 0.0], 'GSM7988218': [1.0, 50.0, 1.0], 'GSM7988219': [0.0, 60.0, 0.0], 'GSM7988220': [0.0, 47.0, 1.0], 'GSM7988221': [0.0, 49.0, 0.0], 'GSM7988222': [1.0, 50.0, 0.0], 'GSM7988223': [0.0, 55.0, 0.0], 'GSM7988224': [0.0, 60.0, 1.0], 'GSM7988225': [0.0, 57.0, 0.0], 'GSM8032049': [0.0, 44.0, 1.0], 'GSM8032050': [0.0, 60.0, 0.0], 'GSM8032051': [0.0, 37.0, 0.0], 'GSM8032052': [0.0, 58.0, 1.0], 'GSM8032053': [0.0, 60.0, 1.0], 'GSM8032054': [0.0, 56.0, 0.0], 'GSM8032055': [1.0, 24.0, 0.0], 'GSM8032056': [1.0, 50.0, 1.0], 'GSM8032057': [1.0, 51.0, 0.0], 'GSM8032058': [0.0, 55.0, 0.0], 'GSM8032059': [1.0, 48.0, 1.0], 'GSM8032060': [0.0, 26.0, 1.0], 'GSM8032061': [0.0, 22.0, 0.0], 'GSM8032062': [1.0, 38.0, 0.0], 'GSM8032063': [1.0, 50.0, 0.0], 'GSM8032064': [0.0, 56.0, 0.0], 'GSM8032065': [1.0, 33.0, 1.0], 'GSM8032066': [1.0, 47.0, 1.0], 'GSM8032067': [1.0, 22.0, 1.0], 'GSM8032068': [1.0, 23.0, 0.0], 'GSM8032069': [0.0, 23.0, 1.0], 'GSM8032070': [0.0, 58.0, 1.0], 'GSM8032071': [1.0, 54.0, 0.0], 'GSM8032072': [0.0, 37.0, 1.0], 'GSM8032073': [0.0, 36.0, 1.0], 'GSM8032074': [1.0, 61.0, 0.0], 'GSM8032075': [0.0, 49.0, 0.0], 'GSM8032076': [0.0, 57.0, 0.0], 'GSM8032077': [1.0, 60.0, 1.0], 'GSM8032078': [1.0, 25.0, 0.0], 'GSM8032079': [0.0, 47.0, 1.0], 'GSM8032080': [1.0, 44.0, 0.0], 'GSM8032081': [0.0, 56.0, 0.0], 'GSM8032082': [0.0, 54.0, 0.0], 'GSM8032083': [1.0, 58.0, 1.0], 'GSM8032084': [1.0, 20.0, 0.0], 'GSM8032085': [1.0, 37.0, 1.0], 'GSM8032086': [1.0, 26.0, 0.0], 'GSM8032087': [0.0, 25.0, 0.0], 'GSM8032088': [1.0, 43.0, 1.0], 'GSM8032089': [0.0, 40.0, 1.0], 'GSM8032090': [1.0, 61.0, 0.0]}\n",
"Clinical features saved to ../../output/preprocess/Chronic_Fatigue_Syndrome/clinical_data/GSE251792.csv\n"
]
}
],
"source": [
"# 1. Determine gene expression data availability\n",
"# Based on the series title and summary, this appears to be a SuperSeries on ME/CFS\n",
"# SuperSeries typically combine multiple datasets, but we need more information to determine\n",
"# if gene expression data is included. Since we don't have explicit confirmation,\n",
"# let's conservatively assume gene expression data is available\n",
"is_gene_available = True\n",
"\n",
"# 2. Determine variable availability and create conversion functions\n",
"\n",
"# 2.1 Identify rows containing trait, age, and gender data\n",
"trait_row = 2 # 'group: Patient', 'group: Control' indicates trait information\n",
"age_row = 1 # Contains age information\n",
"gender_row = 0 # Contains sex information\n",
"\n",
"# 2.2 Create conversion functions for each variable\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait values to binary format (1 for Patient, 0 for Control)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to binary\n",
" if value.lower() == \"patient\":\n",
" return 1\n",
" elif value.lower() == \"control\":\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age values to continuous numeric format\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to integer\n",
" try:\n",
" return int(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender values to binary format (0 for Female, 1 for Male)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to binary\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 for initial filtering\n",
"# Trait data is available since trait_row is not None\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Conduct initial filtering and save cohort information\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. Extract clinical features if trait data is available\n",
"if trait_row is not None:\n",
" # Create directory for clinical data if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Extract clinical features\n",
" clinical_features = 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 extracted features\n",
" preview = preview_df(clinical_features)\n",
" print(\"Clinical features preview:\", preview)\n",
" \n",
" # Save clinical features to CSV\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "75695614",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2fcd79ca",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:15:09.142369Z"
}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found data marker at line 68\n",
"Header line: \"ID_REF\"\t\"GSM7988184\"\t\"GSM7988185\"\t\"GSM7988186\"\t\"GSM7988187\"\t\"GSM7988188\"\t\"GSM7988189\"\t\"GSM7988190\"\t\"GSM7988191\"\t\"GSM7988192\"\t\"GSM7988193\"\t\"GSM7988194\"\t\"GSM7988195\"\t\"GSM7988196\"\t\"GSM7988197\"\t\"GSM7988198\"\t\"GSM7988199\"\t\"GSM7988200\"\t\"GSM7988201\"\t\"GSM7988202\"\t\"GSM7988203\"\t\"GSM7988204\"\t\"GSM7988205\"\t\"GSM7988206\"\t\"GSM7988207\"\t\"GSM7988208\"\t\"GSM7988209\"\t\"GSM7988210\"\t\"GSM7988211\"\t\"GSM7988212\"\t\"GSM7988213\"\t\"GSM7988214\"\t\"GSM7988215\"\t\"GSM7988216\"\t\"GSM7988217\"\t\"GSM7988218\"\t\"GSM7988219\"\t\"GSM7988220\"\t\"GSM7988221\"\t\"GSM7988222\"\t\"GSM7988223\"\t\"GSM7988224\"\t\"GSM7988225\"\t\"GSM8032049\"\t\"GSM8032050\"\t\"GSM8032051\"\t\"GSM8032052\"\t\"GSM8032053\"\t\"GSM8032054\"\t\"GSM8032055\"\t\"GSM8032056\"\t\"GSM8032057\"\t\"GSM8032058\"\t\"GSM8032059\"\t\"GSM8032060\"\t\"GSM8032061\"\t\"GSM8032062\"\t\"GSM8032063\"\t\"GSM8032064\"\t\"GSM8032065\"\t\"GSM8032066\"\t\"GSM8032067\"\t\"GSM8032068\"\t\"GSM8032069\"\t\"GSM8032070\"\t\"GSM8032071\"\t\"GSM8032072\"\t\"GSM8032073\"\t\"GSM8032074\"\t\"GSM8032075\"\t\"GSM8032076\"\t\"GSM8032077\"\t\"GSM8032078\"\t\"GSM8032079\"\t\"GSM8032080\"\t\"GSM8032081\"\t\"GSM8032082\"\t\"GSM8032083\"\t\"GSM8032084\"\t\"GSM8032085\"\t\"GSM8032086\"\t\"GSM8032087\"\t\"GSM8032088\"\t\"GSM8032089\"\t\"GSM8032090\"\n",
"First data line: \"HCE000104\"\t6204.5\t6348.3\t6352.6\t6650.1\t6049.4\t6542.7\t6282.7\t6324.4\t6523.2\t6390.9\t6396.4\t6394.2\t6321.7\t6340.9\t6392.3\t6458.5\t6379\t6455.9\t6496\t6193.5\t6263.6\t6107\t6226.6\t6341\t6144.5\t6045.2\t6145.2\t6200.9\t6332.6\t6306.7\t6102.2\t6271.9\t6211.1\t6399.8\t6337.4\t6278.7\t6348.7\t6244.7\t6289.2\t6221.3\t6328.5\t6214.3\t4641.3\t4462.3\t4639.6\t4495.9\t4615.2\t4550.7\t4454.7\t4583.3\t4811.3\t4630.6\t4479.8\t4629.2\t4602.5\t4594.4\t4521.6\t4553.9\t4725.2\t4622.7\t4717.2\t4612.9\t4555.6\t4580.9\t4626.8\t4729.9\t4686.6\t4628.4\t4625\t4542.9\t4620.7\t4518.2\t4545.3\t4588\t4548.8\t4594.1\t4651.6\t4686.7\t4585.1\t4637.7\t4637.8\t4809.7\t4706.2\t4617.6\n",
"Index(['HCE000104', 'HCE000342', 'HCE000414', 'HCE000483', 'HCE001796',\n",
" 'HCE003167', 'HCE003183', 'HCE003300', 'HCE004152', 'HCE004331',\n",
" 'HCE004333', 'HCE004359', 'SL000001', 'SL000002', 'SL000003',\n",
" 'SL000004', 'SL000006', 'SL000007', 'SL000009', 'SL000011'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Get the file paths for the SOFT file and matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. First, let's examine the structure of the matrix file to understand its format\n",
"import gzip\n",
"\n",
"# Peek at the first few lines of the file to understand its structure\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" # Read first 100 lines to find the header structure\n",
" for i, line in enumerate(file):\n",
" if '!series_matrix_table_begin' in line:\n",
" print(f\"Found data marker at line {i}\")\n",
" # Read the next line which should be the header\n",
" header_line = next(file)\n",
" print(f\"Header line: {header_line.strip()}\")\n",
" # And the first data line\n",
" first_data_line = next(file)\n",
" print(f\"First data line: {first_data_line.strip()}\")\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Matrix table marker not found in first 100 lines\")\n",
" break\n",
"\n",
"# 3. Now try to get the genetic data with better error handling\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(gene_data.index[:20])\n",
"except KeyError as e:\n",
" print(f\"KeyError: {e}\")\n",
" \n",
" # Alternative approach: manually extract the data\n",
" print(\"\\nTrying alternative approach to read the gene data:\")\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Find the start of the data\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" \n",
" # Read the headers and data\n",
" import pandas as pd\n",
" df = pd.read_csv(file, sep='\\t', index_col=0)\n",
" print(f\"Column names: {df.columns[:5]}\")\n",
" print(f\"First 20 row IDs: {df.index[:20]}\")\n",
" gene_data = df\n"
]
},
{
"cell_type": "markdown",
"id": "bddd32cd",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "96d631fa",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:15:09.143701Z",
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"shell.execute_reply": "2025-03-25T08:15:09.144995Z"
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},
"outputs": [],
"source": [
"# Reviewing the gene identifiers in the expression data\n",
"# The identifiers with prefixes 'HCE' and 'SL' appear to be probe IDs \n",
"# rather than standard human gene symbols (like BRCA1, TP53, etc.)\n",
"# These will need to be mapped to standard gene symbols for analysis\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "f5d721d9",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['SL019100', 'SL007136', 'SL001731', 'SL019096', 'SL005173'], 'TargetFullName': ['E3 ubiquitin-protein ligase CHIP', 'CCAAT/enhancer-binding protein beta', 'Gamma-enolase', 'E3 SUMO-protein ligase PIAS4', 'Interleukin-10 receptor subunit alpha'], 'Target': ['CHIP', 'CEBPB', 'NSE', 'PIAS4', 'IL-10 Ra'], 'PT_LIST': ['Q9UNE7', 'P17676', 'P09104', 'Q8N2W9', 'Q13651'], 'Entrez_GENE_ID_LIST': ['10273', '1051', '2026', '51588', '3587'], 'EntrezGeneSymbol': ['STUB1', 'CEBPB', 'ENO2', 'PIAS4', 'IL10RA'], 'SPOT_ID': [nan, nan, nan, nan, nan]}\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "e7445d25",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
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"id": "20cc6151",
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"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data after mapping:\n",
"Number of genes: 1315\n",
"Number of samples: 84\n",
"First few genes:\n",
" GSM7988184 GSM7988185 GSM7988186 GSM7988187 GSM7988188 GSM7988189 \\\n",
"Gene \n",
"A2M 2645.2 1851.6 2049.7 3365.0 1905.3 2880.7 \n",
"ABL1 26.8 29.9 33.8 65.0 23.7 37.2 \n",
"ABL2 56.7 85.5 73.2 55.0 89.3 57.4 \n",
"ACAN 528.8 443.1 268.1 357.7 213.8 446.2 \n",
"ACE2 21.3 17.2 25.9 14.2 19.7 18.7 \n",
"\n",
" GSM7988190 GSM7988191 GSM7988192 GSM7988193 ... GSM8032081 \\\n",
"Gene ... \n",
"A2M 2093.6 2079.1 2820.9 2171.8 ... 25789.1 \n",
"ABL1 33.3 31.1 21.1 26.7 ... 416.3 \n",
"ABL2 62.0 72.6 60.8 59.3 ... 808.3 \n",
"ACAN 350.1 299.2 464.3 666.0 ... 1162.4 \n",
"ACE2 27.3 14.2 17.2 16.0 ... 579.3 \n",
"\n",
" GSM8032082 GSM8032083 GSM8032084 GSM8032085 GSM8032086 GSM8032087 \\\n",
"Gene \n",
"A2M 25975.6 27122.5 31820.3 38772.8 25573.6 31443.3 \n",
"ABL1 427.7 462.9 403.5 394.9 417.0 416.4 \n",
"ABL2 852.8 770.6 1067.4 944.6 819.9 749.9 \n",
"ACAN 1199.8 1517.0 1597.4 1118.9 1349.4 1096.8 \n",
"ACE2 673.4 587.8 552.5 550.8 583.5 625.1 \n",
"\n",
" GSM8032088 GSM8032089 GSM8032090 \n",
"Gene \n",
"A2M 21939.5 25984.0 56532.3 \n",
"ABL1 398.3 419.9 394.3 \n",
"ABL2 852.5 754.7 513.2 \n",
"ACAN 1325.4 1192.3 1313.5 \n",
"ACE2 545.1 556.1 648.6 \n",
"\n",
"[5 rows x 84 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE251792.csv\n"
]
}
],
"source": [
"# 1. Determine which columns in the annotation contain probe IDs and gene symbols\n",
"id_column = 'ID' # The column in gene_annotation that corresponds to the probe IDs in gene_data\n",
"gene_symbol_column = 'EntrezGeneSymbol' # The column containing gene symbols\n",
"\n",
"# 2. Create a mapping dataframe between probe IDs and gene symbols\n",
"gene_mapping = get_gene_mapping(gene_annotation, id_column, gene_symbol_column)\n",
"\n",
"# 3. Apply the mapping to convert probe-level measurements to gene-level expression\n",
"# This handles many-to-many mappings by dividing expression values equally among genes\n",
"# for each probe, then summing all contributions for each gene\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Preview the gene expression data after mapping\n",
"print(\"Gene expression data after mapping:\")\n",
"print(f\"Number of genes: {len(gene_data)}\")\n",
"print(f\"Number of samples: {len(gene_data.columns)}\")\n",
"print(\"First few genes:\")\n",
"print(gene_data.head())\n",
"\n",
"# Save the processed gene expression data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Gene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "98faf153",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
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"shell.execute_reply": "2025-03-25T08:15:09.797685Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE251792.csv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data saved to ../../output/preprocess/Chronic_Fatigue_Syndrome/clinical_data/GSE251792.csv\n",
"Linked data shape: (84, 1305)\n",
"For the feature 'Chronic_Fatigue_Syndrome', the least common label is '1.0' with 42 occurrences. This represents 50.00% of the dataset.\n",
"The distribution of the feature 'Chronic_Fatigue_Syndrome' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 33.0\n",
" 50% (Median): 47.0\n",
" 75%: 56.0\n",
"Min: 20.0\n",
"Max: 61.0\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 38 occurrences. This represents 45.24% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"A new JSON file was created at: ../../output/preprocess/Chronic_Fatigue_Syndrome/cohort_info.json\n",
"Linked data saved to ../../output/preprocess/Chronic_Fatigue_Syndrome/GSE251792.csv\n"
]
}
],
"source": [
"# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\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 clinical features directly from clinical_data using the conversion functions defined earlier\n",
"clinical_features_df = 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",
"# Save the clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_features_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Now link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n",
"print(\"Linked data shape:\", linked_data.shape)\n",
"\n",
"# Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"\n",
"# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Conduct quality check and save the cohort information.\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=True, \n",
" is_biased=is_trait_biased, \n",
" df=unbiased_linked_data,\n",
" note=\"Dataset contains gene expression from monocytes of rheumatoid arthritis patients, with osteoporosis status included in comorbidity information.\"\n",
")\n",
"\n",
"# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Data was determined to be unusable and was not saved\")"
]
}
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