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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ca0ecf26",
"metadata": {
"execution": {
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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 = \"Crohns_Disease\"\n",
"cohort = \"GSE83448\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Crohns_Disease\"\n",
"in_cohort_dir = \"../../input/GEO/Crohns_Disease/GSE83448\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Crohns_Disease/GSE83448.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Crohns_Disease/gene_data/GSE83448.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Crohns_Disease/clinical_data/GSE83448.csv\"\n",
"json_path = \"../../output/preprocess/Crohns_Disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "7dbf9eed",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1bf33921",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Genome-wide transcriptional analysis in intestinal biopsies from Crohn's disease (CD) patients.\"\n",
"!Series_summary\t\"Differential gene expression analysis between CD patients and controls to identify the transcriptional signature that defines the inflamed intestinal mucosa in CD.\"\n",
"!Series_overall_design\t\"Intestinal biopsy samples were obtained from CD patients and healthy controls. RNA was subsequently extracted from each sample. Gene expression intensities were measured using GE Healthcare/Amersham Biosciences CodeLink Human Whole Genome Bioarray. After performing the gene expression quality control analysis, we characterized the transcriptional profile of the inflamed intestinal mucosa in CD.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: intestinal mucosa'], 1: ['inflammation: Control', 'inflammation: Inflamed margin', 'inflammation: Non-inflamed margin']}\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": "8ff34959",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a6c98889",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:34:40.300522Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data preview:\n",
"{'GSM2203115': [0.0], 'GSM2203116': [0.0], 'GSM2203117': [0.0], 'GSM2203118': [1.0], 'GSM2203119': [1.0], 'GSM2203120': [1.0], 'GSM2203121': [0.0], 'GSM2203122': [1.0], 'GSM2203123': [1.0], 'GSM2203124': [1.0], 'GSM2203125': [0.0], 'GSM2203126': [1.0], 'GSM2203127': [1.0], 'GSM2203128': [1.0], 'GSM2203129': [0.0], 'GSM2203130': [1.0], 'GSM2203131': [1.0], 'GSM2203132': [0.0], 'GSM2203133': [1.0], 'GSM2203134': [1.0], 'GSM2203135': [1.0], 'GSM2203136': [0.0], 'GSM2203137': [1.0], 'GSM2203138': [1.0], 'GSM2203139': [1.0], 'GSM2203140': [0.0], 'GSM2203141': [0.0], 'GSM2203142': [1.0], 'GSM2203143': [1.0], 'GSM2203144': [0.0], 'GSM2203145': [0.0], 'GSM2203146': [0.0], 'GSM2203147': [0.0], 'GSM2203148': [1.0], 'GSM2203149': [1.0], 'GSM2203150': [1.0], 'GSM2203151': [1.0], 'GSM2203152': [1.0], 'GSM2203153': [1.0], 'GSM2203154': [1.0], 'GSM2203155': [1.0], 'GSM2203156': [1.0], 'GSM2203157': [1.0], 'GSM2203158': [1.0], 'GSM2203159': [1.0], 'GSM2203160': [1.0], 'GSM2203161': [1.0], 'GSM2203162': [1.0], 'GSM2203163': [1.0], 'GSM2203164': [1.0], 'GSM2203165': [1.0], 'GSM2203166': [1.0], 'GSM2203167': [1.0]}\n",
"Clinical data saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE83448.csv\n"
]
}
],
"source": [
"# 1. Analyze gene expression data availability\n",
"# From the background info, we can see this is a study with gene expression data from GE Healthcare/Amersham Biosciences CodeLink Human Whole Genome Bioarray\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# Looking at the dictionary, we can see that key 1 has inflammation status\n",
"# We can use this to infer Crohn's Disease status (inflamed = CD patient, control = healthy control)\n",
"trait_row = 1\n",
"# Age data is not available in the dictionary\n",
"age_row = None\n",
"# Gender data is not available in the dictionary\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"# Define conversion functions for each variable\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert inflammation status to binary Crohn's Disease indicator.\n",
" 0 = No CD (Control), 1 = CD (Inflamed margin or Non-inflamed margin)\n",
" \"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if value == \"Control\":\n",
" return 0\n",
" elif value in [\"Inflamed margin\", \"Non-inflamed margin\"]:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# No age data, but define the function as required\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous value.\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"# No gender data, but define the function as required\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary value (0=female, 1=male).\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" if value in ['female', 'f']:\n",
" return 0\n",
" elif value in ['male', 'm']:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save metadata - initial filtering check\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. Extract clinical features if trait data is available\n",
"if trait_row is not None:\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 clinical data\n",
" preview = preview_df(clinical_df)\n",
" print(\"Clinical data preview:\")\n",
" print(preview)\n",
" \n",
" # Save clinical data to CSV\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": "4665e22e",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c06530fb",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:34:40.301772Z",
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"shell.execute_reply": "2025-03-25T08:34:40.438237Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"First 20 gene/probe identifiers:\n",
"Index(['GE469557', 'GE469567', 'GE469590', 'GE469632', 'GE469690', 'GE469802',\n",
" 'GE469817', 'GE469849', 'GE469866', 'GE469875', 'GE469953', 'GE470103',\n",
" 'GE470130', 'GE470157', 'GE470169', 'GE470208', 'GE470218', 'GE470249',\n",
" 'GE470296', 'GE470328'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene data dimensions: 20902 genes × 53 samples\n"
]
}
],
"source": [
"# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract the gene expression 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)\n",
"print(\"\\nFirst 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n",
"\n",
"# 4. Print the dimensions of the gene expression data\n",
"print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
"is_gene_available = True\n"
]
},
{
"cell_type": "markdown",
"id": "091d77b6",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "dfa92f1c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:34:40.439902Z",
"iopub.status.busy": "2025-03-25T08:34:40.439793Z",
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"shell.execute_reply": "2025-03-25T08:34:40.441381Z"
}
},
"outputs": [],
"source": [
"# Examine the gene identifiers\n",
"# These identifiers (like GE469557) are not standard human gene symbols\n",
"# Standard human gene symbols would be like BRCA1, TP53, etc.\n",
"# These look like custom probes/identifiers specific to a microarray platform\n",
"# They would need to be mapped to standard gene symbols for biological interpretation\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "f8a29bd4",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ffa0f3a0",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:34:41.775955Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['GE469530', 'GE469548', 'GE469549', 'GE469555', 'GE469557'], 'GB_ACC': ['AI650595.1', 'BU686968.1', 'BU623208.1', 'BE045962.1', 'AY077696.1'], 'Probe_Name': ['GE469530', 'GE469548', 'GE469549', 'GE469555', 'GE469557'], 'Probe_Type': ['DISCOVERY', 'DISCOVERY', 'DISCOVERY', 'DISCOVERY', 'DISCOVERY'], 'DESCRIPTION': [\"wa92h11x1 NCI_CGAP_GC6 cDNA clone IMAGE:2303685 3'\", \"UI-CF-DU1-ado-i-08-0-UIs1 UI-CF-DU1 cDNA clone UI-CF-DU1-ado-i-08-0-UI 3'\", \"UI-H-FL1-bgd-j-14-0-UI.s1 NCI_CGAP_FL1 cDNA clone UI-H-FL1-bgd-j-14-0-UI 3', mRNA sequence\", \"hd90g04x4 NCI_CGAP_GC6 cDNA clone IMAGE:2916822 3'\", 'clone qd65g07 PRED16 protein (PRED16) mRNA'], 'SPOT_ID': [nan, nan, nan, nan, nan]}\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": "f830b849",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b2ecca5f",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:34:44.225820Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Checking for additional columns in annotation data:\n",
"Column 'ID' sample: ['GE469530' 'GE469548' 'GE469549']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'GB_ACC' sample: ['AI650595.1' 'BU686968.1' 'BU623208.1']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'Probe_Name' sample: ['GE469530' 'GE469548' 'GE469549']\n",
"Column 'Probe_Type' sample: ['DISCOVERY']\n",
"Column 'DESCRIPTION' sample: [\"wa92h11x1 NCI_CGAP_GC6 cDNA clone IMAGE:2303685 3'\"\n",
" \"UI-CF-DU1-ado-i-08-0-UIs1 UI-CF-DU1 cDNA clone UI-CF-DU1-ado-i-08-0-UI 3'\"\n",
" \"UI-H-FL1-bgd-j-14-0-UI.s1 NCI_CGAP_FL1 cDNA clone UI-H-FL1-bgd-j-14-0-UI 3', mRNA sequence\"]\n",
"Column 'SPOT_ID' sample: ['INCYTE UNIQUE']\n",
"\n",
"Using GenBank accessions as gene identifiers.\n",
"\n",
"Gene mapping dataframe shape: (1156663, 2)\n",
"Sample of gene mapping:\n",
"{'ID': ['GE469530', 'GE469548', 'GE469549', 'GE469555', 'GE469557'], 'Gene': ['AI650595.1', 'BU686968.1', 'BU623208.1', 'BE045962.1', 'AY077696.1']}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"After mapping - Gene data dimensions: (5353, 53)\n",
"\n",
"First few gene identifiers after mapping:\n",
"Index(['AA010870', 'AA021186', 'AA029225', 'AA057423', 'AA058586', 'AA127601',\n",
" 'AA149620', 'AA150617', 'AA166934', 'AA187037'],\n",
" dtype='object', name='Gene')\n",
"\n",
"Note: The dataset is using GenBank accessions rather than standard gene symbols.\n",
"This may affect downstream analysis that relies on gene symbol annotations.\n",
"\n",
"Gene expression data saved to ../../output/preprocess/Crohns_Disease/gene_data/GSE83448.csv\n"
]
}
],
"source": [
"# 1. Look for alternative gene symbol columns in the annotation data\n",
"# First, check if there are any hidden/unprefixed columns that might contain gene symbols\n",
"print(\"Checking for additional columns in annotation data:\")\n",
"for col in gene_annotation.columns:\n",
" unique_values = gene_annotation[col].dropna().unique()\n",
" if len(unique_values) > 0:\n",
" print(f\"Column '{col}' sample: {unique_values[:3]}\")\n",
"\n",
"# Since we don't see standard gene symbols, we'll use GB_ACC (GenBank accessions)\n",
"# as identifiers for the gene expression data\n",
"print(\"\\nUsing GenBank accessions as gene identifiers.\")\n",
"gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'GB_ACC')\n",
"\n",
"# Check the mapping dataframe\n",
"print(f\"\\nGene mapping dataframe shape: {gene_mapping.shape}\")\n",
"print(\"Sample of gene mapping:\")\n",
"print(preview_df(gene_mapping))\n",
"\n",
"# 3. Convert probe-level measurements to gene expression\n",
"# Note: We're working with accession numbers, not gene symbols\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Preview the results\n",
"print(\"\\nAfter mapping - Gene data dimensions:\", gene_data.shape)\n",
"print(\"\\nFirst few gene identifiers after mapping:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Skip normalization since these are not standard gene symbols\n",
"# We'll keep the accession numbers as identifiers\n",
"print(\"\\nNote: The dataset is using GenBank accessions rather than standard gene symbols.\")\n",
"print(\"This may affect downstream analysis that relies on gene symbol annotations.\")\n",
"\n",
"# Save the 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\"\\nGene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "f9445cb6",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c9256f02",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:34:44.227605Z",
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"shell.execute_reply": "2025-03-25T08:34:45.640822Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing gene expression data...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Crohns_Disease/gene_data/GSE83448.csv\n",
"Gene data shape: 5353 genes × 53 samples\n",
"Extracting clinical features from original clinical data...\n",
"Clinical features saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE83448.csv\n",
"Clinical features preview:\n",
"{'GSM2203115': [0.0], 'GSM2203116': [0.0], 'GSM2203117': [0.0], 'GSM2203118': [1.0], 'GSM2203119': [1.0], 'GSM2203120': [1.0], 'GSM2203121': [0.0], 'GSM2203122': [1.0], 'GSM2203123': [1.0], 'GSM2203124': [1.0], 'GSM2203125': [0.0], 'GSM2203126': [1.0], 'GSM2203127': [1.0], 'GSM2203128': [1.0], 'GSM2203129': [0.0], 'GSM2203130': [1.0], 'GSM2203131': [1.0], 'GSM2203132': [0.0], 'GSM2203133': [1.0], 'GSM2203134': [1.0], 'GSM2203135': [1.0], 'GSM2203136': [0.0], 'GSM2203137': [1.0], 'GSM2203138': [1.0], 'GSM2203139': [1.0], 'GSM2203140': [0.0], 'GSM2203141': [0.0], 'GSM2203142': [1.0], 'GSM2203143': [1.0], 'GSM2203144': [0.0], 'GSM2203145': [0.0], 'GSM2203146': [0.0], 'GSM2203147': [0.0], 'GSM2203148': [1.0], 'GSM2203149': [1.0], 'GSM2203150': [1.0], 'GSM2203151': [1.0], 'GSM2203152': [1.0], 'GSM2203153': [1.0], 'GSM2203154': [1.0], 'GSM2203155': [1.0], 'GSM2203156': [1.0], 'GSM2203157': [1.0], 'GSM2203158': [1.0], 'GSM2203159': [1.0], 'GSM2203160': [1.0], 'GSM2203161': [1.0], 'GSM2203162': [1.0], 'GSM2203163': [1.0], 'GSM2203164': [1.0], 'GSM2203165': [1.0], 'GSM2203166': [1.0], 'GSM2203167': [1.0]}\n",
"Linking clinical and genetic data...\n",
"Linked data shape: (53, 5354)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (53, 5354)\n",
"\n",
"Checking for bias in feature variables:\n",
"For the feature 'Crohns_Disease', the least common label is '0.0' with 14 occurrences. This represents 26.42% of the dataset.\n",
"The distribution of the feature 'Crohns_Disease' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Crohns_Disease/GSE83448.csv\n"
]
}
],
"source": [
"# 1. Skip gene symbol normalization and use the accession numbers directly\n",
"print(\"Processing gene expression data...\")\n",
"# Don't normalize - these are GenBank accessions, not gene symbols\n",
"gene_data_normalized = gene_data # Use the original gene data with accession numbers\n",
"\n",
"# Save the gene data (without normalization)\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",
"print(f\"Gene data shape: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# 2. Extract clinical features from scratch\n",
"print(\"Extracting clinical features from original clinical data...\")\n",
"clinical_features = geo_select_clinical_features(\n",
" clinical_data, \n",
" trait, \n",
" trait_row,\n",
" convert_trait,\n",
" age_row,\n",
" convert_age,\n",
" gender_row,\n",
" convert_gender\n",
")\n",
"\n",
"# Save the extracted clinical features\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_features.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
"\n",
"print(\"Clinical features preview:\")\n",
"print(preview_df(clinical_features))\n",
"\n",
"# Check if clinical features were successfully extracted\n",
"if clinical_features.empty:\n",
" print(\"Failed to extract clinical features. Dataset cannot be processed further.\")\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=False,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"Clinical features could not be extracted from the dataset.\"\n",
" )\n",
" print(\"Dataset deemed not usable due to lack of clinical features.\")\n",
"else:\n",
" # 2. Link clinical and genetic data\n",
" print(\"Linking clinical and genetic data...\")\n",
" linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
" # Check if the linked data has gene features\n",
" if linked_data.shape[1] <= 1:\n",
" print(\"Error: Linked data has no gene features. Dataset cannot be processed further.\")\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=False,\n",
" is_trait_available=True,\n",
" is_biased=True,\n",
" df=linked_data,\n",
" note=\"Failed to link gene expression data with clinical features.\"\n",
" )\n",
" else:\n",
" # 3. Handle missing values systematically\n",
" linked_data = handle_missing_values(linked_data, trait_col=trait)\n",
" print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" # Check if there are still samples after missing value handling\n",
" if linked_data.shape[0] == 0:\n",
" print(\"Error: No samples remain after handling missing values.\")\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=True,\n",
" df=pd.DataFrame(),\n",
" note=\"All samples were removed during missing value handling.\"\n",
" )\n",
" else:\n",
" # 4. Check if the dataset is biased\n",
" print(\"\\nChecking for bias in feature variables:\")\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
" # 5. Conduct final quality validation\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_biased,\n",
" df=linked_data,\n",
" note=\"Dataset contains gene expression data for Crohn's Disease patients and healthy controls.\"\n",
" )\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 deemed not usable for trait association studies, linked data not saved.\")"
]
}
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|