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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "34d61bd8",
"metadata": {
"execution": {
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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 = \"Asthma\"\n",
"cohort = \"GSE230164\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Asthma\"\n",
"in_cohort_dir = \"../../input/GEO/Asthma/GSE230164\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Asthma/GSE230164.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Asthma/gene_data/GSE230164.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Asthma/clinical_data/GSE230164.csv\"\n",
"json_path = \"../../output/preprocess/Asthma/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "92bfef84",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "95c1d853",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Gene expression profiling of asthma\"\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: ['gender: female', 'gender: male']}\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": "913b1076",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b39b3aaf",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:41:56.498241Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this is a SuperSeries about gene expression profiling of asthma\n",
"# This indicates it likely contains gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# From the sample characteristics dictionary, we can see gender information is available at index 0\n",
"# There's no explicit trait (asthma) or age information in the sample characteristics\n",
"trait_row = None # Trait information not directly available\n",
"age_row = None # Age information not available\n",
"gender_row = 0 # Gender information is at index 0\n",
"\n",
"# 2.2 Data Type Conversion\n",
"# For trait (unavailable, but defining function for completeness)\n",
"def convert_trait(value):\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().lower()\n",
" else:\n",
" value = value.strip().lower()\n",
" \n",
" # Binary conversion for asthma\n",
" if 'asthma' in value or 'yes' in value or 'positive' in value or 'case' in value:\n",
" return 1\n",
" elif 'control' in value or 'no' in value or 'negative' in value or 'healthy' in value:\n",
" return 0\n",
" return None\n",
"\n",
"# For age (unavailable, but defining function for completeness)\n",
"def convert_age(value):\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",
" else:\n",
" value = value.strip()\n",
" \n",
" # Try to convert to float for continuous age\n",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"# For gender\n",
"def convert_gender(value):\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().lower()\n",
" else:\n",
" value = value.strip().lower()\n",
" \n",
" # Binary conversion: female=0, male=1\n",
" if 'female' in value or 'f' == value:\n",
" return 0\n",
" elif 'male' in value or 'm' == value:\n",
" return 1\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Check if trait data is available (trait_row is not None)\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Conduct initial filtering and save metadata\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",
"# Since trait_row is None, we skip the clinical feature extraction\n"
]
},
{
"cell_type": "markdown",
"id": "e81b59e8",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "fcba5192",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:41:57.001553Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Asthma/GSE230164/GSE230164-GPL10558_series_matrix.txt.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape: (47235, 99)\n",
"First 20 gene/probe identifiers:\n",
"Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n",
" 'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n",
" 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n",
" 'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n",
" 'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Get the SOFT and matrix file paths again \n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"Matrix file found: {matrix_file}\")\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" \n",
" # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "385d8636",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bf8612dd",
"metadata": {
"execution": {
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},
"outputs": [],
"source": [
"# The gene identifiers start with \"ILMN_\" which indicates they are Illumina probe IDs\n",
"# from the Illumina BeadArray platform. These are not human gene symbols but are \n",
"# platform-specific probe IDs that need to be mapped to gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "1bcf6388",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "379708e7",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:41:57.006677Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Species': [nan, nan, nan, nan, nan], 'Source': [nan, nan, nan, nan, nan], 'Search_Key': [nan, nan, nan, nan, nan], 'Transcript': [nan, nan, nan, nan, nan], 'ILMN_Gene': [nan, nan, nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan, nan, nan], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Unigene_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': [nan, nan, nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB'], 'Protein_Product': [nan, nan, nan, nan, 'thrB'], 'Probe_Id': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5090180.0, 6510136.0, 7560739.0, 1450438.0, 1240647.0], 'Probe_Type': [nan, nan, nan, nan, nan], 'Probe_Start': [nan, nan, nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT', 'TCTGTCACTGTCAGGAAAGTGGTAAAACTGCAACTCAATTACTGCAATGC', 'CTTGTGCCTGAGCTGTCAAAAGTAGAGCACGTCGCCGAGATGAAGGGCGC'], 'Chromosome': [nan, nan, nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan, nan, nan], 'Cytoband': [nan, nan, nan, nan, nan], 'Definition': [nan, nan, nan, nan, nan], 'Ontology_Component': [nan, nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan, nan, nan], 'GB_ACC': [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": "fef3f8cb",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "21c2ffae",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:42:06.770198Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping dataframe shape: (44837, 2)\n",
"First few rows of gene mapping:\n",
" ID Gene\n",
"0 ILMN_1343048 phage_lambda_genome\n",
"1 ILMN_1343049 phage_lambda_genome\n",
"2 ILMN_1343050 phage_lambda_genome:low\n",
"3 ILMN_1343052 phage_lambda_genome:low\n",
"4 ILMN_1343059 thrB\n",
"Gene expression data shape after mapping: (21440, 99)\n",
"First few gene symbols:\n",
"Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2',\n",
" 'A4GALT', 'A4GNT'],\n",
" dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. Identify the key columns from the gene annotation dictionary\n",
"# The gene identifiers in the gene expression data are \"ILMN_\" IDs which map to the \"ID\" column in gene_annotation\n",
"# The gene symbols are stored in the \"Symbol\" column in gene_annotation\n",
"\n",
"# 2. Get gene mapping dataframe by extracting the two identified columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col=\"ID\", gene_col=\"Symbol\")\n",
"print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
"print(\"First few rows of gene mapping:\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
"print(\"First few gene symbols:\")\n",
"print(gene_data.index[:10])\n"
]
},
{
"cell_type": "markdown",
"id": "cc8557d0",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "fdf42161",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:42:08.167323Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape after normalization: (20238, 99)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Asthma/gene_data/GSE230164.csv\n",
"No trait information available in this dataset.\n",
"Dataset cannot be used for analysis because trait information is missing.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
"\n",
"# Create directory and save the normalized gene 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\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# Since trait_row was determined to be None in step 2, we cannot extract clinical features\n",
"# This means the dataset lacks the necessary trait information for our analysis\n",
"print(\"No trait information available in this dataset.\")\n",
"\n",
"# Use is_final=False for validation since we lack trait information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=False # No trait information available\n",
")\n",
"\n",
"print(\"Dataset cannot be used for analysis because trait information is missing.\")"
]
}
],
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|