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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "b399e2f5",
"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 = \"Asthma\"\n",
"cohort = \"GSE188424\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Asthma\"\n",
"in_cohort_dir = \"../../input/GEO/Asthma/GSE188424\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Asthma/GSE188424.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Asthma/gene_data/GSE188424.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Asthma/clinical_data/GSE188424.csv\"\n",
"json_path = \"../../output/preprocess/Asthma/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "0dd7ac45",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3f796413",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:41:38.494465Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Gene expression profiling of peripheral blood from uncontrolled and controlled asthma\"\n",
"!Series_summary\t\"We analyzed the transcriptomes of children with controlled and uncontrolled asthma in Taiwanese Consortium of Childhood Asthma Study (TCCAS). Hierarchical clustering, differentially expressed gene (DEG), weighted gene co-expression network analysis (WGCNA) and pathway enrichment methods were performed, to investigate important genes between two groups.\"\n",
"!Series_overall_design\t\"Analysis of gene expression obtained from human whole blood comparing uncontrolled and controlled asthma.\"\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": "d6284a22",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6de9ed63",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:41:38.790897Z",
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"shell.execute_reply": "2025-03-25T06:41:38.796891Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Based on the provided information, let's analyze this dataset:\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# The series summary mentions \"transcriptomes\" and \"gene expression profiling\"\n",
"# which strongly indicates gene expression data is available\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Data Availability\n",
"# Trait (Asthma control status) is mentioned in the background information\n",
"# However, we cannot locate it in the sample characteristics dictionary\n",
"trait_row = None # Cannot find in sample characteristics\n",
"is_trait_available = False # Since trait_row is None\n",
"\n",
"# Age data is not available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# Gender data is available at key 0\n",
"gender_row = 0\n",
"\n",
"# 2.2 Data Type Conversion\n",
"# For trait (when we locate it):\n",
"def convert_trait(val):\n",
" if val is None:\n",
" return None\n",
" val = val.lower().split(': ')[-1].strip()\n",
" if 'uncontrolled' in val:\n",
" return 1\n",
" elif 'controlled' in val:\n",
" return 0\n",
" return None\n",
"\n",
"# For age (if we found it, which we didn't):\n",
"def convert_age(val):\n",
" if val is None:\n",
" return None\n",
" try:\n",
" # Extract the value after the colon and convert to float\n",
" return float(val.split(': ')[-1].strip())\n",
" except:\n",
" return None\n",
"\n",
"# For gender:\n",
"def convert_gender(val):\n",
" if val is None:\n",
" return None\n",
" val = val.lower().split(': ')[-1].strip()\n",
" if 'female' in val:\n",
" return 0\n",
" elif 'male' in val:\n",
" return 1\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Trait data is not available in the sample characteristics\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",
"# Skip this substep since trait_row is None\n"
]
},
{
"cell_type": "markdown",
"id": "73a8faf5",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "181ccba0",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:41:38.798444Z",
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"shell.execute_reply": "2025-03-25T06:41:39.306739Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Asthma/GSE188424/GSE188424_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": "acb57858",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f484fbb0",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:41:39.308589Z",
"iopub.status.busy": "2025-03-25T06:41:39.308462Z",
"iopub.status.idle": "2025-03-25T06:41:39.310567Z",
"shell.execute_reply": "2025-03-25T06:41:39.310241Z"
}
},
"outputs": [],
"source": [
"# The identifiers starting with ILMN_ are Illumina probe IDs, not human gene symbols\n",
"# These are specific to Illumina microarray platforms and need to be mapped to human gene symbols\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "1b0775a3",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ec2b68cd",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:41:39.311678Z",
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"shell.execute_reply": "2025-03-25T06:41:48.651510Z"
}
},
"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": "014ebb6b",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ea26d19c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:41:48.653254Z",
"iopub.status.busy": "2025-03-25T06:41:48.653125Z",
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"shell.execute_reply": "2025-03-25T06:41:50.435236Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mapping dataframe shape: (44837, 2)\n",
"First few rows of mapping dataframe:\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-level expression data shape: (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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data shape: (20238, 99)\n",
"First few normalized gene symbols:\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT',\n",
" 'A4GNT', 'AAA1', 'AAAS'],\n",
" dtype='object', name='Gene')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved gene expression data to ../../output/preprocess/Asthma/gene_data/GSE188424.csv\n"
]
}
],
"source": [
"# 1. Identify the relevant columns for gene mapping\n",
"# From examining the preview, we can see:\n",
"# - 'ID' column contains identifiers matching those in the gene expression data (ILMN_*)\n",
"# - 'Symbol' column contains gene symbols we want to map to\n",
"\n",
"# 2. Get the gene mapping dataframe by extracting the identifier and symbol columns\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n",
"print(\"First few rows of mapping dataframe:\")\n",
"print(mapping_df.head())\n",
"\n",
"# 3. Apply gene mapping to convert probe-level data to gene-level expression\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"print(f\"Gene-level expression data shape: {gene_data.shape}\")\n",
"print(\"First few gene symbols:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# 4. Normalize gene symbols to ensure consistency (optional but recommended)\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene expression data shape: {gene_data.shape}\")\n",
"print(\"First few normalized gene symbols:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# 5. 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\"Saved gene expression data to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "30c21032",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "ade5f9e8",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:41:53.975020Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data shape: (1, 100)\n",
"Clinical data column names: ['!Sample_geo_accession', 'GSM5681954', 'GSM5681955', 'GSM5681956', 'GSM5681957', 'GSM5681958', 'GSM5681959', 'GSM5681960', 'GSM5681961', 'GSM5681962', 'GSM5681963', 'GSM5681964', 'GSM5681965', 'GSM5681966', 'GSM5681967', 'GSM5681968', 'GSM5681969', 'GSM5681970', 'GSM5681971', 'GSM5681972', 'GSM5681973', 'GSM5681974', 'GSM5681975', 'GSM5681976', 'GSM5681977', 'GSM5681978', 'GSM5681979', 'GSM5681980', 'GSM5681981', 'GSM5681982', 'GSM5681983', 'GSM5681984', 'GSM5681985', 'GSM5681986', 'GSM5681987', 'GSM5681988', 'GSM5681989', 'GSM5681990', 'GSM5681991', 'GSM5681992', 'GSM5681993', 'GSM5681994', 'GSM5681995', 'GSM5681996', 'GSM5681997', 'GSM5681998', 'GSM5681999', 'GSM5682000', 'GSM5682001', 'GSM5682002', 'GSM5682003', 'GSM5682004', 'GSM5682005', 'GSM5682006', 'GSM5682007', 'GSM5682008', 'GSM5682009', 'GSM5682010', 'GSM5682011', 'GSM5682012', 'GSM5682013', 'GSM5682014', 'GSM5682015', 'GSM5682016', 'GSM5682017', 'GSM5682018', 'GSM5682019', 'GSM5682020', 'GSM5682021', 'GSM5682022', 'GSM5682023', 'GSM5682024', 'GSM5682025', 'GSM5682026', 'GSM5682027', 'GSM5682028', 'GSM5682029', 'GSM5682030', 'GSM5682031', 'GSM5682032', 'GSM5682033', 'GSM5682034', 'GSM5682035', 'GSM5682036', 'GSM5682037', 'GSM5682038', 'GSM5682039', 'GSM5682040', 'GSM5682041', 'GSM5682042', 'GSM5682043', 'GSM5682044', 'GSM5682045', 'GSM5682046', 'GSM5682047', 'GSM5682048', 'GSM5682049', 'GSM5682050', 'GSM5682051', 'GSM5682052']\n",
"Sample characteristics: {0: ['gender: female', 'gender: male']}\n",
"Gene data shape: (47235, 99)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data saved to ../../output/preprocess/Asthma/gene_data/GSE188424.csv\n",
"Dataset usability status: False\n",
"No linked data file saved since trait data is unavailable.\n"
]
}
],
"source": [
"# First, re-extract the necessary files from the cohort directory\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Get the gene data again \n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# Read background information and clinical data again\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",
"# Examine the clinical data structure to see what's actually available\n",
"print(\"Clinical data shape:\", clinical_data.shape)\n",
"print(\"Clinical data column names:\", clinical_data.columns.tolist())\n",
"sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
"print(\"Sample characteristics:\", sample_characteristics_dict)\n",
"\n",
"# Since we previously determined trait data is not available (trait_row = None),\n",
"# we can't create proper clinical data for this dataset\n",
"is_trait_available = False\n",
"\n",
"# The gene data has already been normalized and saved in previous steps\n",
"print(f\"Gene data shape: {gene_data.shape}\")\n",
"\n",
"# 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\"Gene data saved to {out_gene_data_file}\")\n",
"\n",
"# Since trait data is not available, use is_final=False in validate_and_save_cohort_info\n",
"# This bypasses the need for the is_biased parameter\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=is_trait_available\n",
")\n",
"\n",
"print(f\"Dataset usability status: {is_usable}\")\n",
"print(\"No linked data file saved since trait data is unavailable.\")"
]
}
],
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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