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{
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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 = \"Alzheimers_Disease\"\n",
"cohort = \"GSE185909\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Alzheimers_Disease\"\n",
"in_cohort_dir = \"../../input/GEO/Alzheimers_Disease/GSE185909\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Alzheimers_Disease/GSE185909.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Alzheimers_Disease/gene_data/GSE185909.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Alzheimers_Disease/clinical_data/GSE185909.csv\"\n",
"json_path = \"../../output/preprocess/Alzheimers_Disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "27a6537a",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6171e204",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Co-expression network analysis of frontal cortex during the progression of Alzheimer’s disease \"\n",
"!Series_summary\t\"Using WGCNA and enrichment analyses to identify pathway level differences between individuals with no cognitive impairment, mild cognitive impairment, and Alzheimer’s disease.\"\n",
"!Series_summary\t\"Frozen frontal cortex (BA10) tissue from NCI, MCI, and mild/moderate AD cases (n = 12/group) representing both genders was acquired postmortem from participants in the Rush Religious Orders Study, a longitudinal clinical pathologic study of aging and AD in elderly Catholic clergy\"\n",
"!Series_overall_design\t\"Nimblegen expression array human frontal cortex - NCI (No Cognative Impairment) vs. MCI (Mild Cognative Impairment) vs. AD (alzheimers disease); Labeled cDNA was digested and hybridized to NimbleGen 12 x 135K human arrays for 18 hrs at 42°C and analyzed on a GenePix 4200A scanner (Molecular Devices). Probe intensity levels were quantified with RMA preprocessing (NimbleScan v2.5, Roche)\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['diagnosis: AD', 'diagnosis: MCI', 'diagnosis: NCI'], 1: ['Sex: Male', 'Sex: Female'], 2: ['age_death: 83.8110882957', 'age_death: 80.5338809035', 'age_death: 85.1635865845', 'age_death: 83.3976728268', 'age_death: 76.3093771389', 'age_death: 80.3230663929', 'age_death: 92.1916495551', 'age_death: 85.6399726215', 'age_death: 86.2477754962', 'age_death: 87.3839835729', 'age_death: 82.9349760438', 'age_death: 89.2156057495', 'age_death: 88.0465434634', 'age_death: 90.0314852841', 'age_death: 72.7063655031'], 3: ['post_mortem_interval: 12', 'post_mortem_interval: 4.5833333333', 'post_mortem_interval: 3.25', 'post_mortem_interval: 7.5833333333', 'post_mortem_interval: 2.5', 'post_mortem_interval: 2.6666666667', 'post_mortem_interval: 3.0833333333', 'post_mortem_interval: 3.6666666667', 'post_mortem_interval: 4.5', 'post_mortem_interval: 3.1666666667', 'post_mortem_interval: 13.4166666667', 'post_mortem_interval: 3.9166666667', 'post_mortem_interval: 2.75', 'post_mortem_interval: 7.75'], 4: ['years_education: 18', 'years_education: 21', 'years_education: 16', 'years_education: 15', 'years_education: 25', 'years_education: 8', 'years_education: 20', 'years_education: 22'], 5: ['brain_weight: 1160', 'brain_weight: 1480', 'brain_weight: 1060', 'brain_weight: 1320', 'brain_weight: 1340', 'brain_weight: 1260', 'brain_weight: 1100', 'brain_weight: 1050', 'brain_weight: 1150', 'brain_weight: 1310', 'brain_weight: 1570', 'brain_weight: 1240', 'brain_weight: 1090', 'brain_weight: 1380'], 6: ['cogdx: 4', 'cogdx: 2', 'cogdx: 1', 'cogdx: 3'], 7: ['scmmse30_last_valid: 15', 'scmmse30_last_valid: 27', 'scmmse30_last_valid: 28', 'scmmse30_last_valid: 22', 'scmmse30_last_valid: 20', 'scmmse30_last_valid: 29', 'scmmse30_last_valid: 14', 'scmmse30_last_valid: 18', 'scmmse30_last_valid: 24', 'scmmse30_last_valid: 26', 'scmmse30_last_valid: 30'], 8: ['globcog: -2.1816786639', 'globcog: -0.4065097289', 'globcog: -0.185109059', 'globcog: -1.2008515629', 'globcog: 0.2545211363', 'globcog: -1.1819698561', 'globcog: -0.5206275244', 'globcog: -2.8522349533', 'globcog: -1.4945772374', 'globcog: -1.2390838727', 'globcog: -0.1242229923', 'globcog: -0.263746112', 'globcog: -0.7849454354', 'globcog: -0.8658969549', 'globcog: -0.4237302883'], 9: ['cerad: 2', 'cerad: 4', 'cerad: 1'], 10: ['braak: 2', 'braak: 5', 'braak: 3', 'braak: 4'], 11: ['niareagan: 3', 'niareagan: 2', 'niareagan: 1']}\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": "7804665f",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "24d5d839",
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"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of clinical data:\n",
"{'GSM5625602': [1.0, 83.8110882957, 1.0], 'GSM5625603': [1.0, 83.8110882957, 1.0], 'GSM5625604': [0.0, 80.5338809035, 1.0], 'GSM5625605': [0.0, 80.5338809035, 1.0], 'GSM5625606': [0.0, 85.1635865845, 0.0], 'GSM5625607': [0.0, 85.1635865845, 0.0], 'GSM5625608': [1.0, 83.3976728268, 1.0], 'GSM5625609': [1.0, 83.3976728268, 1.0], 'GSM5625610': [0.0, 76.3093771389, 1.0], 'GSM5625611': [1.0, 80.3230663929, 1.0], 'GSM5625612': [1.0, 80.3230663929, 1.0], 'GSM5625613': [1.0, 80.3230663929, 1.0], 'GSM5625614': [1.0, 80.3230663929, 1.0], 'GSM5625615': [0.0, 92.1916495551, 0.0], 'GSM5625616': [0.0, 92.1916495551, 0.0], 'GSM5625617': [0.0, 92.1916495551, 0.0], 'GSM5625618': [1.0, 85.6399726215, 0.0], 'GSM5625619': [1.0, 85.6399726215, 0.0], 'GSM5625620': [1.0, 86.2477754962, 1.0], 'GSM5625621': [1.0, 86.2477754962, 1.0], 'GSM5625622': [1.0, 86.2477754962, 1.0], 'GSM5625623': [1.0, 87.3839835729, 1.0], 'GSM5625624': [1.0, 87.3839835729, 1.0], 'GSM5625625': [0.0, 82.9349760438, 0.0], 'GSM5625626': [0.0, 82.9349760438, 0.0], 'GSM5625627': [0.0, 89.2156057495, 1.0], 'GSM5625628': [0.0, 89.2156057495, 1.0], 'GSM5625629': [1.0, 88.0465434634, 1.0], 'GSM5625630': [1.0, 88.0465434634, 1.0], 'GSM5625631': [0.0, 90.0314852841, 0.0], 'GSM5625632': [0.0, 90.0314852841, 0.0], 'GSM5625633': [0.0, 90.0314852841, 0.0], 'GSM5625634': [0.0, 90.0314852841, 0.0], 'GSM5625635': [0.0, 72.7063655031, 1.0], 'GSM5625636': [0.0, 72.7063655031, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Alzheimers_Disease/clinical_data/GSE185909.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# From the background information, this dataset contains gene expression data from NimbleGen expression array\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): Row 0 contains diagnosis information\n",
"trait_row = 0\n",
"\n",
"# Age: Row 2 contains age information\n",
"age_row = 2\n",
"\n",
"# Gender: Row 1 contains gender information\n",
"gender_row = 1\n",
"\n",
"# 2.2 Data Type Conversion\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert diagnosis to binary: 1 for AD, 0 for non-AD (NCI, MCI)\"\"\"\n",
" if not value or \":\" not in value:\n",
" return None\n",
" \n",
" diagnosis = value.split(\": \")[1].strip().upper()\n",
" \n",
" if diagnosis == \"AD\":\n",
" return 1\n",
" elif diagnosis in [\"NCI\", \"MCI\"]:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous numeric value\"\"\"\n",
" if not value or \":\" not in value:\n",
" return None\n",
" \n",
" try:\n",
" age = float(value.split(\": \")[1].strip())\n",
" return age\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary: 0 for Female, 1 for Male\"\"\"\n",
" if not value or \":\" not in value:\n",
" return None\n",
" \n",
" gender = value.split(\": \")[1].strip().lower()\n",
" \n",
" if gender == \"female\":\n",
" return 0\n",
" elif gender == \"male\":\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Trait data is available if trait_row is not None\n",
"is_trait_available = trait_row is not None\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 (if trait_row is not None)\n",
"if trait_row is not None:\n",
" # clinical_data is assumed to be available from a previous step\n",
" clinical_df = geo_select_clinical_features(\n",
" clinical_data, # Assuming this was created in a previous step\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 processed data\n",
" print(\"Preview of clinical data:\")\n",
" print(preview_df(clinical_df))\n",
" \n",
" # Save the processed data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "3cf8bd71",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e709c499",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 20 gene/probe identifiers:\n",
"Index(['AB000409', 'AB000463', 'AB000781', 'AB001328', 'AB002294', 'AB002308',\n",
" 'AB002311', 'AB002313', 'AB002360', 'AB002377', 'AB002381', 'AB002382',\n",
" 'AB002384', 'AB003177', 'AB003333', 'AB006589', 'AB006590', 'AB006621',\n",
" 'AB006625', 'AB007457'],\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": "285babd9",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2b62ce2d",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# Based on my biomedical knowledge, these identifiers appear to be GenBank accession numbers\n",
"# (starting with \"AB\" followed by numbers), not standard human gene symbols.\n",
"# Standard human gene symbols would typically be alphabetical like APOE, BRCA1, etc.\n",
"# These accession numbers need to be mapped to gene symbols for meaningful analysis.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "57682ed9",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f5e4a996",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['AB000409', 'AB000463', 'AB000781', 'AB001328', 'AB002294'], 'GB_ACC': ['AB000409', 'AB000463', 'AB000781', 'AB001328', 'AB002294'], 'DESCRIPTION': ['MAP kinase interacting serine/threonine kinase 1', 'SH3-domain binding protein 2', 'kinase non-catalytic C-lobe domain (KIND) containing 1', 'solute carrier family 15 (oligopeptide transporter), member 1', 'zinc finger protein 646']}\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": "8d8c902c",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a7a1f1e2",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation columns:\n",
"['ID', 'GB_ACC', 'DESCRIPTION']\n",
"\n",
"More detailed view of gene annotation:\n",
" ID GB_ACC DESCRIPTION\n",
"0 AB000409 AB000409 MAP kinase interacting serine/threonine kinase 1\n",
"1 AB000463 AB000463 SH3-domain binding protein 2\n",
"2 AB000781 AB000781 kinase non-catalytic C-lobe domain (KIND) containing 1\n",
"3 AB001328 AB001328 solute carrier family 15 (oligopeptide transporter), member 1\n",
"4 AB002294 AB002294 zinc finger protein 646\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Number of genes after mapping and normalization: 1772\n",
"First 10 genes:\n",
"Index(['A4GALT', 'AAA1', 'AADAC', 'AARD', 'ABCC11', 'ABCC9', 'ABCD1', 'ABCE1',\n",
" 'ABI3', 'ABRAXAS2'],\n",
" dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. Re-examine the gene annotation data to understand available fields\n",
"print(\"Gene annotation columns:\")\n",
"print(gene_annotation.columns.tolist())\n",
"\n",
"# Let's see more rows to better understand the data structure\n",
"print(\"\\nMore detailed view of gene annotation:\")\n",
"print(gene_annotation.head().to_string())\n",
"\n",
"# Based on the DESCRIPTION field containing full gene names, we need to extract gene symbols \n",
"# The 'ID' column contains the identifiers that match with gene expression data\n",
"\n",
"# 2. Create the mapping dataframe using the appropriate columns\n",
"id_col = 'ID'\n",
"gene_col = 'DESCRIPTION' # We'll keep this but properly extract symbols from it\n",
"\n",
"# Create the mapping dataframe\n",
"mapping_df = get_gene_mapping(gene_annotation, id_col, gene_col)\n",
"\n",
"# 3. Apply gene mapping with proper extraction of gene symbols\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Normalize gene symbols to standard format after mapping\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"\n",
"# Print the number of genes and preview first genes\n",
"print(f\"\\nNumber of genes after mapping and normalization: {len(gene_data)}\")\n",
"print(\"First 10 genes:\")\n",
"print(gene_data.index[:10])\n"
]
},
{
"cell_type": "markdown",
"id": "e142d9e4",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "926a4043",
"metadata": {
"execution": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalizing gene symbols...\n",
"Gene data shape after normalization: (1772, 35)\n",
"Normalized gene data saved to ../../output/preprocess/Alzheimers_Disease/gene_data/GSE185909.csv\n",
"Loading the original clinical data...\n",
"Extracting clinical features...\n",
"Clinical data preview:\n",
"{'GSM5625602': [1.0, 83.8110882957, 1.0], 'GSM5625603': [1.0, 83.8110882957, 1.0], 'GSM5625604': [0.0, 80.5338809035, 1.0], 'GSM5625605': [0.0, 80.5338809035, 1.0], 'GSM5625606': [0.0, 85.1635865845, 0.0], 'GSM5625607': [0.0, 85.1635865845, 0.0], 'GSM5625608': [1.0, 83.3976728268, 1.0], 'GSM5625609': [1.0, 83.3976728268, 1.0], 'GSM5625610': [0.0, 76.3093771389, 1.0], 'GSM5625611': [1.0, 80.3230663929, 1.0], 'GSM5625612': [1.0, 80.3230663929, 1.0], 'GSM5625613': [1.0, 80.3230663929, 1.0], 'GSM5625614': [1.0, 80.3230663929, 1.0], 'GSM5625615': [0.0, 92.1916495551, 0.0], 'GSM5625616': [0.0, 92.1916495551, 0.0], 'GSM5625617': [0.0, 92.1916495551, 0.0], 'GSM5625618': [1.0, 85.6399726215, 0.0], 'GSM5625619': [1.0, 85.6399726215, 0.0], 'GSM5625620': [1.0, 86.2477754962, 1.0], 'GSM5625621': [1.0, 86.2477754962, 1.0], 'GSM5625622': [1.0, 86.2477754962, 1.0], 'GSM5625623': [1.0, 87.3839835729, 1.0], 'GSM5625624': [1.0, 87.3839835729, 1.0], 'GSM5625625': [0.0, 82.9349760438, 0.0], 'GSM5625626': [0.0, 82.9349760438, 0.0], 'GSM5625627': [0.0, 89.2156057495, 1.0], 'GSM5625628': [0.0, 89.2156057495, 1.0], 'GSM5625629': [1.0, 88.0465434634, 1.0], 'GSM5625630': [1.0, 88.0465434634, 1.0], 'GSM5625631': [0.0, 90.0314852841, 0.0], 'GSM5625632': [0.0, 90.0314852841, 0.0], 'GSM5625633': [0.0, 90.0314852841, 0.0], 'GSM5625634': [0.0, 90.0314852841, 0.0], 'GSM5625635': [0.0, 72.7063655031, 1.0], 'GSM5625636': [0.0, 72.7063655031, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Alzheimers_Disease/clinical_data/GSE185909.csv\n",
"Linking clinical and genetic data...\n",
"Linked data shape: (35, 1775)\n",
"Handling missing values...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (35, 1775)\n",
"Checking for bias in trait distribution...\n",
"For the feature 'Alzheimers_Disease', the least common label is '1.0' with 17 occurrences. This represents 48.57% of the dataset.\n",
"The distribution of the feature 'Alzheimers_Disease' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 81.73442847365001\n",
" 50% (Median): 85.6399726215\n",
" 75%: 88.63107460645\n",
"Min: 72.7063655031\n",
"Max: 92.1916495551\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 13 occurrences. This represents 37.14% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"Dataset usability: True\n",
"Linked data saved to ../../output/preprocess/Alzheimers_Disease/GSE185909.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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