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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "028f1fa0",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:25:41.452199Z",
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"iopub.status.idle": "2025-03-25T06:25:41.622792Z",
"shell.execute_reply": "2025-03-25T06:25:41.622359Z"
}
},
"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 = \"Alzheimers_Disease\"\n",
"cohort = \"GSE122063\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Alzheimers_Disease\"\n",
"in_cohort_dir = \"../../input/GEO/Alzheimers_Disease/GSE122063\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Alzheimers_Disease/GSE122063.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Alzheimers_Disease/gene_data/GSE122063.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Alzheimers_Disease/clinical_data/GSE122063.csv\"\n",
"json_path = \"../../output/preprocess/Alzheimers_Disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "7a0ea38b",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "97c07b6e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:25:41.624145Z",
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"iopub.status.idle": "2025-03-25T06:25:42.120255Z",
"shell.execute_reply": "2025-03-25T06:25:42.119661Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Dementia Comparison: VaD vs. AD vs. Controls\"\n",
"!Series_summary\t\"Gene expression profiling was performed on frontal and temporal cortex from vascular dementia (VaD), Alzheimer's disease (AD), and non-demented controls (Control) obtained from the University of Michigan Brain Bank. Controls and AD cases had no infarcts in the autopsied hemisphere. Vascular dementia cases had low Braak staging.\"\n",
"!Series_overall_design\t\"Each sample (VaD=8), (AD=12), (Controls=11) was run, at a minimum, in duplicate with a dye swap (Cy3/Cy5) on Agilent Human 8x60k v2 microarrays.\"\n",
"!Series_overall_design\t\"\"\n",
"!Series_overall_design\t\"These are dual channel arrays, but have been processed as a single channel analysis. Normalized log2 signal is provided for each sample. Raw files are included in a tar archive on the series record. Please see 'Description' field for the name of the raw file for each sample.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['patient diagnosis: Vascular dementia', \"patient diagnosis: Alzheimer's disease\", 'patient diagnosis: Control'], 1: ['tissue: Brain'], 2: ['brain region: frontal cortex', 'brain region: temporal cortex'], 3: ['subject id: 381', 'subject id: 444', 'subject id: 488', 'subject id: 745', 'subject id: 981', 'subject id: 1063', 'subject id: 1370', 'subject id: 1396', 'subject id: 279', 'subject id: 326', 'subject id: 413', 'subject id: 418', 'subject id: 544', 'subject id: 754', 'subject id: 765', 'subject id: 850', 'subject id: 895', 'subject id: 958', 'subject id: 1181', 'subject id: 1337', 'subject id: 57', 'subject id: 90', 'subject id: 100', 'subject id: 110', 'subject id: 382', 'subject id: 566', 'subject id: 729', 'subject id: 732', 'subject id: 915', 'subject id: 978'], 4: ['pmi: 17', 'pmi: 15', 'pmi: 12', 'pmi: 4', 'pmi: 7', 'pmi: 6', 'pmi: 9', 'pmi: 5', 'pmi: 14', 'pmi: 8', 'pmi: 10'], 5: ['Sex: Male', 'Sex: Female'], 6: ['age: 75', 'age: 90', 'age: 78', 'age: 82', 'age: 96', 'age: 77', 'age: 93', 'age: 62', 'age: 89', 'age: 79', 'age: 81', 'age: 91', 'age: 83', 'age: 63', 'age: 88', 'age: 74', 'age: 73', 'age: 87', 'age: 60']}\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": "a7b31f08",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b3131b3d",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:25:42.122126Z",
"iopub.status.busy": "2025-03-25T06:25:42.121969Z",
"iopub.status.idle": "2025-03-25T06:25:42.157171Z",
"shell.execute_reply": "2025-03-25T06:25:42.156697Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical Data Preview:\n",
"{'GSM3454053': [nan, 75.0, 1.0], 'GSM3454054': [nan, 75.0, 1.0], 'GSM3454055': [nan, 75.0, 1.0], 'GSM3454056': [nan, 75.0, 1.0], 'GSM3454057': [nan, 75.0, 1.0], 'GSM3454058': [nan, 75.0, 1.0], 'GSM3454059': [nan, 75.0, 1.0], 'GSM3454060': [nan, 75.0, 1.0], 'GSM3454061': [nan, 90.0, 0.0], 'GSM3454062': [nan, 90.0, 0.0], 'GSM3454063': [nan, 90.0, 0.0], 'GSM3454064': [nan, 90.0, 0.0], 'GSM3454065': [nan, 78.0, 1.0], 'GSM3454066': [nan, 78.0, 1.0], 'GSM3454067': [nan, 78.0, 1.0], 'GSM3454068': [nan, 78.0, 1.0], 'GSM3454069': [nan, 82.0, 0.0], 'GSM3454070': [nan, 82.0, 0.0], 'GSM3454071': [nan, 82.0, 0.0], 'GSM3454072': [nan, 82.0, 0.0], 'GSM3454073': [nan, 96.0, 0.0], 'GSM3454074': [nan, 96.0, 0.0], 'GSM3454075': [nan, 96.0, 0.0], 'GSM3454076': [nan, 96.0, 0.0], 'GSM3454077': [nan, 77.0, 1.0], 'GSM3454078': [nan, 77.0, 1.0], 'GSM3454079': [nan, 77.0, 1.0], 'GSM3454080': [nan, 77.0, 1.0], 'GSM3454081': [nan, 93.0, 0.0], 'GSM3454082': [nan, 93.0, 0.0], 'GSM3454083': [nan, 93.0, 0.0], 'GSM3454084': [nan, 93.0, 0.0], 'GSM3454085': [nan, 62.0, 1.0], 'GSM3454086': [nan, 62.0, 1.0], 'GSM3454087': [nan, 62.0, 1.0], 'GSM3454088': [nan, 62.0, 1.0], 'GSM3454089': [1.0, 82.0, 0.0], 'GSM3454090': [1.0, 82.0, 0.0], 'GSM3454091': [1.0, 82.0, 0.0], 'GSM3454092': [1.0, 82.0, 0.0], 'GSM3454093': [1.0, 82.0, 0.0], 'GSM3454094': [1.0, 82.0, 0.0], 'GSM3454095': [1.0, 82.0, 0.0], 'GSM3454096': [1.0, 82.0, 0.0], 'GSM3454097': [1.0, 89.0, 0.0], 'GSM3454098': [1.0, 89.0, 0.0], 'GSM3454099': [1.0, 89.0, 0.0], 'GSM3454100': [1.0, 89.0, 0.0], 'GSM3454101': [1.0, 82.0, 0.0], 'GSM3454102': [1.0, 82.0, 0.0], 'GSM3454103': [1.0, 82.0, 0.0], 'GSM3454104': [1.0, 82.0, 0.0], 'GSM3454105': [1.0, 77.0, 0.0], 'GSM3454106': [1.0, 77.0, 0.0], 'GSM3454107': [1.0, 77.0, 0.0], 'GSM3454108': [1.0, 77.0, 0.0], 'GSM3454109': [1.0, 79.0, 1.0], 'GSM3454110': [1.0, 79.0, 1.0], 'GSM3454111': [1.0, 79.0, 1.0], 'GSM3454112': [1.0, 79.0, 1.0], 'GSM3454113': [1.0, 81.0, 0.0], 'GSM3454114': [1.0, 81.0, 0.0], 'GSM3454115': [1.0, 81.0, 0.0], 'GSM3454116': [1.0, 81.0, 0.0], 'GSM3454117': [1.0, 81.0, 0.0], 'GSM3454118': [1.0, 81.0, 0.0], 'GSM3454119': [1.0, 81.0, 0.0], 'GSM3454120': [1.0, 81.0, 0.0], 'GSM3454121': [1.0, 75.0, 0.0], 'GSM3454122': [1.0, 75.0, 0.0], 'GSM3454123': [1.0, 75.0, 0.0], 'GSM3454124': [1.0, 75.0, 0.0], 'GSM3454125': [1.0, 81.0, 1.0], 'GSM3454126': [1.0, 81.0, 1.0], 'GSM3454127': [1.0, 81.0, 1.0], 'GSM3454128': [1.0, 81.0, 1.0], 'GSM3454129': [1.0, 91.0, 1.0], 'GSM3454130': [1.0, 91.0, 1.0], 'GSM3454131': [1.0, 91.0, 1.0], 'GSM3454132': [1.0, 91.0, 1.0], 'GSM3454133': [1.0, 83.0, 0.0], 'GSM3454134': [1.0, 83.0, 0.0], 'GSM3454135': [1.0, 83.0, 0.0], 'GSM3454136': [1.0, 83.0, 0.0], 'GSM3454137': [1.0, 63.0, 0.0], 'GSM3454138': [1.0, 63.0, 0.0], 'GSM3454139': [1.0, 63.0, 0.0], 'GSM3454140': [1.0, 63.0, 0.0], 'GSM3454141': [1.0, 88.0, 0.0], 'GSM3454142': [1.0, 88.0, 0.0], 'GSM3454143': [1.0, 88.0, 0.0], 'GSM3454144': [1.0, 88.0, 0.0], 'GSM3454145': [0.0, 74.0, 0.0], 'GSM3454146': [0.0, 74.0, 0.0], 'GSM3454147': [0.0, 74.0, 0.0], 'GSM3454148': [0.0, 74.0, 0.0], 'GSM3454149': [0.0, 73.0, 1.0], 'GSM3454150': [0.0, 73.0, 1.0], 'GSM3454151': [0.0, 73.0, 1.0], 'GSM3454152': [0.0, 73.0, 1.0], 'GSM3454153': [0.0, 87.0, 0.0], 'GSM3454154': [0.0, 87.0, 0.0], 'GSM3454155': [0.0, 87.0, 0.0], 'GSM3454156': [0.0, 87.0, 0.0], 'GSM3454157': [0.0, 73.0, 0.0], 'GSM3454158': [0.0, 73.0, 0.0], 'GSM3454159': [0.0, 73.0, 0.0], 'GSM3454160': [0.0, 73.0, 0.0], 'GSM3454161': [0.0, 81.0, 1.0], 'GSM3454162': [0.0, 81.0, 1.0], 'GSM3454163': [0.0, 81.0, 1.0], 'GSM3454164': [0.0, 81.0, 1.0], 'GSM3454165': [0.0, 81.0, 0.0], 'GSM3454166': [0.0, 81.0, 0.0], 'GSM3454167': [0.0, 81.0, 0.0], 'GSM3454168': [0.0, 81.0, 0.0], 'GSM3454169': [0.0, 60.0, 1.0], 'GSM3454170': [0.0, 60.0, 1.0], 'GSM3454171': [0.0, 60.0, 1.0], 'GSM3454172': [0.0, 60.0, 1.0], 'GSM3454173': [0.0, 91.0, 1.0], 'GSM3454174': [0.0, 91.0, 1.0], 'GSM3454175': [0.0, 91.0, 1.0], 'GSM3454176': [0.0, 91.0, 1.0], 'GSM3454177': [0.0, 81.0, 0.0], 'GSM3454178': [0.0, 81.0, 0.0], 'GSM3454179': [0.0, 81.0, 0.0], 'GSM3454180': [0.0, 81.0, 0.0], 'GSM3454181': [0.0, 77.0, 0.0], 'GSM3454182': [0.0, 77.0, 0.0], 'GSM3454183': [0.0, 77.0, 0.0], 'GSM3454184': [0.0, 77.0, 0.0], 'GSM3454185': [0.0, 89.0, 1.0], 'GSM3454186': [0.0, 89.0, 1.0], 'GSM3454187': [0.0, 89.0, 1.0], 'GSM3454188': [0.0, 89.0, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Alzheimers_Disease/clinical_data/GSE122063.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the Series_title, Series_summary, and Series_overall_design, this dataset contains gene expression data\n",
"# from frontal and temporal cortex samples using Agilent Human 8x60k v2 microarrays.\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# Trait (Alzheimer's Disease) - available in key 0 \"patient diagnosis\"\n",
"trait_row = 0\n",
"\n",
"# Age - available in key 6 \"age\"\n",
"age_row = 6\n",
"\n",
"# Gender - available in key 5 \"Sex\"\n",
"gender_row = 5\n",
"\n",
"# 2.2 Data Type Conversion\n",
"\n",
"def convert_trait(val):\n",
" \"\"\"Convert trait values to binary (0: Control, 1: Alzheimer's disease)\"\"\"\n",
" if not isinstance(val, str):\n",
" return None\n",
" \n",
" if \":\" in val:\n",
" val = val.split(\":\", 1)[1].strip()\n",
" \n",
" if \"Alzheimer's disease\" in val or \"AD\" in val:\n",
" return 1\n",
" elif \"Control\" in val:\n",
" return 0\n",
" else: # \"Vascular dementia\" or other values\n",
" return None # We only want AD vs Control\n",
"\n",
"def convert_age(val):\n",
" \"\"\"Convert age values to continuous numeric values\"\"\"\n",
" if not isinstance(val, str):\n",
" return None\n",
" \n",
" if \":\" in val:\n",
" val = val.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return float(val)\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(val):\n",
" \"\"\"Convert gender values to binary (0: Female, 1: Male)\"\"\"\n",
" if not isinstance(val, str):\n",
" return None\n",
" \n",
" if \":\" in val:\n",
" val = val.split(\":\", 1)[1].strip().lower()\n",
" else:\n",
" val = val.lower()\n",
" \n",
" if \"female\" in val:\n",
" return 0\n",
" elif \"male\" in val:\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",
"\n",
"# Save initial filtering 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. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" # Extract clinical features\n",
" 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 extracted clinical data\n",
" preview = preview_df(clinical_df)\n",
" print(\"Clinical Data Preview:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "01fb46c8",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1ac31201",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:25:42.158879Z",
"iopub.status.busy": "2025-03-25T06:25:42.158740Z",
"iopub.status.idle": "2025-03-25T06:25:42.944411Z",
"shell.execute_reply": "2025-03-25T06:25:42.943751Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 20 gene/probe identifiers:\n",
"Index(['4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16',\n",
" '17', '18', '19', '20', '21', '22', '23'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. First get the file paths again to access the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data from the matrix_file\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n",
"print(\"First 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "8340e0f3",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "029c3d4e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:25:42.946406Z",
"iopub.status.busy": "2025-03-25T06:25:42.946245Z",
"iopub.status.idle": "2025-03-25T06:25:42.948832Z",
"shell.execute_reply": "2025-03-25T06:25:42.948464Z"
}
},
"outputs": [],
"source": [
"# This is not a code execution step but an assessment of gene identifiers\n",
"# Looking at the provided indices which are numeric values like '4', '5', '6', etc.\n",
"# These are not standard human gene symbols (which would be alphanumeric like 'APOE', 'PSEN1', etc.)\n",
"# These appear to be probe IDs or some other numerical identifiers that would need mapping to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "11d40dc1",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9051a6c7",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:25:42.950061Z",
"iopub.status.busy": "2025-03-25T06:25:42.949949Z",
"iopub.status.idle": "2025-03-25T06:25:53.411831Z",
"shell.execute_reply": "2025-03-25T06:25:53.411185Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['1', '2', '3', '4', '5'], 'COL': ['192', '192', '192', '192', '192'], 'ROW': [328.0, 326.0, 324.0, 322.0, 320.0], 'NAME': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'A_23_P117082', 'A_33_P3246448'], 'SPOT_ID': ['CONTROL', 'CONTROL', 'CONTROL', 'A_23_P117082', 'A_33_P3246448'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'GB_ACC': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'LOCUSLINK_ID': [nan, nan, nan, 50865.0, 23704.0], 'GENE_SYMBOL': [nan, nan, nan, 'HEBP1', 'KCNE4'], 'GENE_NAME': [nan, nan, nan, 'heme binding protein 1', 'potassium voltage-gated channel, Isk-related family, member 4'], 'UNIGENE_ID': [nan, nan, nan, 'Hs.642618', 'Hs.348522'], 'ENSEMBL_ID': [nan, nan, nan, 'ENST00000014930', 'ENST00000281830'], 'ACCESSION_STRING': [nan, nan, nan, 'ref|NM_015987|ens|ENST00000014930|gb|AF117615|gb|BC016277', 'ref|NM_080671|ens|ENST00000281830|tc|THC2655788'], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, 'chr12:13127906-13127847', 'chr2:223920197-223920256'], 'CYTOBAND': [nan, nan, nan, 'hs|12p13.1', 'hs|2q36.1'], 'DESCRIPTION': [nan, nan, nan, 'Homo sapiens heme binding protein 1 (HEBP1), mRNA [NM_015987]', 'Homo sapiens potassium voltage-gated channel, Isk-related family, member 4 (KCNE4), mRNA [NM_080671]'], 'GO_ID': [nan, nan, nan, 'GO:0005488(binding)|GO:0005576(extracellular region)|GO:0005737(cytoplasm)|GO:0005739(mitochondrion)|GO:0005829(cytosol)|GO:0007623(circadian rhythm)|GO:0020037(heme binding)', 'GO:0005244(voltage-gated ion channel activity)|GO:0005249(voltage-gated potassium channel activity)|GO:0006811(ion transport)|GO:0006813(potassium ion transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0016324(apical plasma membrane)'], 'SEQUENCE': [nan, nan, nan, 'AAGGGGGAAAATGTGATTTGTGCCTGATCTTTCATCTGTGATTCTTATAAGAGCTTTGTC', 'GCAAGTCTCTCTGCACCTATTAAAAAGTGATGTATATACTTCCTTCTTATTCTGTTGAGT']}\n"
]
}
],
"source": [
"# 1. First get the file paths using geo_get_relevant_filepaths function\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. 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",
"# 3. 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": "399268e0",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0995315e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:25:53.413594Z",
"iopub.status.busy": "2025-03-25T06:25:53.413473Z",
"iopub.status.idle": "2025-03-25T06:25:53.990324Z",
"shell.execute_reply": "2025-03-25T06:25:53.989786Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mapping data shape: (54295, 2)\n",
"First 5 rows of mapping data:\n",
" ID Gene\n",
"3 4 HEBP1\n",
"4 5 KCNE4\n",
"5 6 BPIFA3\n",
"6 7 LOC100129869\n",
"7 8 IRG1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data shape after mapping: (20353, 136)\n",
"First 5 gene symbols after mapping:\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2LD1', 'A2M'], dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. Observe the gene identifiers and decide on the mapping columns\n",
"# From the preview, we can see that 'ID' in gene_annotation corresponds to row identifiers in gene_data\n",
"# And 'GENE_SYMBOL' contains the human gene symbols we need\n",
"\n",
"# 2. Get a gene mapping dataframe with the ID and GENE_SYMBOL columns\n",
"mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')\n",
"print(f\"Mapping data shape: {mapping_data.shape}\")\n",
"print(\"First 5 rows of mapping data:\")\n",
"print(mapping_data.head())\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)\n",
"print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
"print(\"First 5 gene symbols after mapping:\")\n",
"print(gene_data.index[:5])\n"
]
},
{
"cell_type": "markdown",
"id": "35e410ec",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2c4b4692",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:25:53.991850Z",
"iopub.status.busy": "2025-03-25T06:25:53.991724Z",
"iopub.status.idle": "2025-03-25T06:26:08.028453Z",
"shell.execute_reply": "2025-03-25T06:26:08.027765Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalizing gene symbols...\n",
"Gene data shape after normalization: (19847, 136)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Alzheimers_Disease/gene_data/GSE122063.csv\n",
"Loading the original clinical data...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting clinical features...\n",
"Clinical data preview:\n",
"{'GSM3454053': [nan, 75.0, 1.0], 'GSM3454054': [nan, 75.0, 1.0], 'GSM3454055': [nan, 75.0, 1.0], 'GSM3454056': [nan, 75.0, 1.0], 'GSM3454057': [nan, 75.0, 1.0], 'GSM3454058': [nan, 75.0, 1.0], 'GSM3454059': [nan, 75.0, 1.0], 'GSM3454060': [nan, 75.0, 1.0], 'GSM3454061': [nan, 90.0, 0.0], 'GSM3454062': [nan, 90.0, 0.0], 'GSM3454063': [nan, 90.0, 0.0], 'GSM3454064': [nan, 90.0, 0.0], 'GSM3454065': [nan, 78.0, 1.0], 'GSM3454066': [nan, 78.0, 1.0], 'GSM3454067': [nan, 78.0, 1.0], 'GSM3454068': [nan, 78.0, 1.0], 'GSM3454069': [nan, 82.0, 0.0], 'GSM3454070': [nan, 82.0, 0.0], 'GSM3454071': [nan, 82.0, 0.0], 'GSM3454072': [nan, 82.0, 0.0], 'GSM3454073': [nan, 96.0, 0.0], 'GSM3454074': [nan, 96.0, 0.0], 'GSM3454075': [nan, 96.0, 0.0], 'GSM3454076': [nan, 96.0, 0.0], 'GSM3454077': [nan, 77.0, 1.0], 'GSM3454078': [nan, 77.0, 1.0], 'GSM3454079': [nan, 77.0, 1.0], 'GSM3454080': [nan, 77.0, 1.0], 'GSM3454081': [nan, 93.0, 0.0], 'GSM3454082': [nan, 93.0, 0.0], 'GSM3454083': [nan, 93.0, 0.0], 'GSM3454084': [nan, 93.0, 0.0], 'GSM3454085': [nan, 62.0, 1.0], 'GSM3454086': [nan, 62.0, 1.0], 'GSM3454087': [nan, 62.0, 1.0], 'GSM3454088': [nan, 62.0, 1.0], 'GSM3454089': [1.0, 82.0, 0.0], 'GSM3454090': [1.0, 82.0, 0.0], 'GSM3454091': [1.0, 82.0, 0.0], 'GSM3454092': [1.0, 82.0, 0.0], 'GSM3454093': [1.0, 82.0, 0.0], 'GSM3454094': [1.0, 82.0, 0.0], 'GSM3454095': [1.0, 82.0, 0.0], 'GSM3454096': [1.0, 82.0, 0.0], 'GSM3454097': [1.0, 89.0, 0.0], 'GSM3454098': [1.0, 89.0, 0.0], 'GSM3454099': [1.0, 89.0, 0.0], 'GSM3454100': [1.0, 89.0, 0.0], 'GSM3454101': [1.0, 82.0, 0.0], 'GSM3454102': [1.0, 82.0, 0.0], 'GSM3454103': [1.0, 82.0, 0.0], 'GSM3454104': [1.0, 82.0, 0.0], 'GSM3454105': [1.0, 77.0, 0.0], 'GSM3454106': [1.0, 77.0, 0.0], 'GSM3454107': [1.0, 77.0, 0.0], 'GSM3454108': [1.0, 77.0, 0.0], 'GSM3454109': [1.0, 79.0, 1.0], 'GSM3454110': [1.0, 79.0, 1.0], 'GSM3454111': [1.0, 79.0, 1.0], 'GSM3454112': [1.0, 79.0, 1.0], 'GSM3454113': [1.0, 81.0, 0.0], 'GSM3454114': [1.0, 81.0, 0.0], 'GSM3454115': [1.0, 81.0, 0.0], 'GSM3454116': [1.0, 81.0, 0.0], 'GSM3454117': [1.0, 81.0, 0.0], 'GSM3454118': [1.0, 81.0, 0.0], 'GSM3454119': [1.0, 81.0, 0.0], 'GSM3454120': [1.0, 81.0, 0.0], 'GSM3454121': [1.0, 75.0, 0.0], 'GSM3454122': [1.0, 75.0, 0.0], 'GSM3454123': [1.0, 75.0, 0.0], 'GSM3454124': [1.0, 75.0, 0.0], 'GSM3454125': [1.0, 81.0, 1.0], 'GSM3454126': [1.0, 81.0, 1.0], 'GSM3454127': [1.0, 81.0, 1.0], 'GSM3454128': [1.0, 81.0, 1.0], 'GSM3454129': [1.0, 91.0, 1.0], 'GSM3454130': [1.0, 91.0, 1.0], 'GSM3454131': [1.0, 91.0, 1.0], 'GSM3454132': [1.0, 91.0, 1.0], 'GSM3454133': [1.0, 83.0, 0.0], 'GSM3454134': [1.0, 83.0, 0.0], 'GSM3454135': [1.0, 83.0, 0.0], 'GSM3454136': [1.0, 83.0, 0.0], 'GSM3454137': [1.0, 63.0, 0.0], 'GSM3454138': [1.0, 63.0, 0.0], 'GSM3454139': [1.0, 63.0, 0.0], 'GSM3454140': [1.0, 63.0, 0.0], 'GSM3454141': [1.0, 88.0, 0.0], 'GSM3454142': [1.0, 88.0, 0.0], 'GSM3454143': [1.0, 88.0, 0.0], 'GSM3454144': [1.0, 88.0, 0.0], 'GSM3454145': [0.0, 74.0, 0.0], 'GSM3454146': [0.0, 74.0, 0.0], 'GSM3454147': [0.0, 74.0, 0.0], 'GSM3454148': [0.0, 74.0, 0.0], 'GSM3454149': [0.0, 73.0, 1.0], 'GSM3454150': [0.0, 73.0, 1.0], 'GSM3454151': [0.0, 73.0, 1.0], 'GSM3454152': [0.0, 73.0, 1.0], 'GSM3454153': [0.0, 87.0, 0.0], 'GSM3454154': [0.0, 87.0, 0.0], 'GSM3454155': [0.0, 87.0, 0.0], 'GSM3454156': [0.0, 87.0, 0.0], 'GSM3454157': [0.0, 73.0, 0.0], 'GSM3454158': [0.0, 73.0, 0.0], 'GSM3454159': [0.0, 73.0, 0.0], 'GSM3454160': [0.0, 73.0, 0.0], 'GSM3454161': [0.0, 81.0, 1.0], 'GSM3454162': [0.0, 81.0, 1.0], 'GSM3454163': [0.0, 81.0, 1.0], 'GSM3454164': [0.0, 81.0, 1.0], 'GSM3454165': [0.0, 81.0, 0.0], 'GSM3454166': [0.0, 81.0, 0.0], 'GSM3454167': [0.0, 81.0, 0.0], 'GSM3454168': [0.0, 81.0, 0.0], 'GSM3454169': [0.0, 60.0, 1.0], 'GSM3454170': [0.0, 60.0, 1.0], 'GSM3454171': [0.0, 60.0, 1.0], 'GSM3454172': [0.0, 60.0, 1.0], 'GSM3454173': [0.0, 91.0, 1.0], 'GSM3454174': [0.0, 91.0, 1.0], 'GSM3454175': [0.0, 91.0, 1.0], 'GSM3454176': [0.0, 91.0, 1.0], 'GSM3454177': [0.0, 81.0, 0.0], 'GSM3454178': [0.0, 81.0, 0.0], 'GSM3454179': [0.0, 81.0, 0.0], 'GSM3454180': [0.0, 81.0, 0.0], 'GSM3454181': [0.0, 77.0, 0.0], 'GSM3454182': [0.0, 77.0, 0.0], 'GSM3454183': [0.0, 77.0, 0.0], 'GSM3454184': [0.0, 77.0, 0.0], 'GSM3454185': [0.0, 89.0, 1.0], 'GSM3454186': [0.0, 89.0, 1.0], 'GSM3454187': [0.0, 89.0, 1.0], 'GSM3454188': [0.0, 89.0, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Alzheimers_Disease/clinical_data/GSE122063.csv\n",
"Linking clinical and genetic data...\n",
"Linked data shape: (136, 19850)\n",
"Handling missing values...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (100, 19850)\n",
"Checking for bias in trait distribution...\n",
"For the feature 'Alzheimers_Disease', the least common label is '0.0' with 44 occurrences. This represents 44.00% of the dataset.\n",
"The distribution of the feature 'Alzheimers_Disease' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 77.0\n",
" 50% (Median): 81.0\n",
" 75%: 83.0\n",
"Min: 60.0\n",
"Max: 91.0\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 32 occurrences. This represents 32.00% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"Dataset usability: True\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Alzheimers_Disease/GSE122063.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"print(\"Normalizing gene symbols...\")\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",
"# Save the normalized gene data to a CSV file\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",
"# 2. Link the clinical and genetic data\n",
"print(\"Loading the original clinical data...\")\n",
"# Get the matrix file again to ensure we have the proper data\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
"\n",
"print(\"Extracting clinical features...\")\n",
"# Use the clinical_data obtained directly from the matrix file\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",
"print(\"Clinical data preview:\")\n",
"print(preview_df(selected_clinical_df))\n",
"\n",
"# Save the clinical data to a CSV file\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Link clinical and genetic data using the normalized gene data\n",
"print(\"Linking clinical and genetic data...\")\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"print(\"Handling 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",
"# 4. Check if trait is biased\n",
"print(\"Checking for bias in trait distribution...\")\n",
"is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Final validation\n",
"note = \"Dataset contains gene expression data from bronchial brushings from control individuals and patients with asthma after rhinovirus infection in vivo, as described in the study 'Rhinovirus-induced epithelial RIG-I inflammasome suppresses antiviral immunity and promotes inflammation in asthma and COVID-19'.\"\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=linked_data,\n",
" note=note\n",
")\n",
"\n",
"print(f\"Dataset usability: {is_usable}\")\n",
"\n",
"# 6. Save linked data if 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(\"Dataset is not usable for trait-gene association studies due to bias or other issues.\")"
]
}
],
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