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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "6769a68e",
"metadata": {},
"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 = \"Stomach_Cancer\"\n",
"cohort = \"GSE146361\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Stomach_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Stomach_Cancer/GSE146361\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Stomach_Cancer/GSE146361.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Stomach_Cancer/gene_data/GSE146361.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Stomach_Cancer/clinical_data/GSE146361.csv\"\n",
"json_path = \"../../output/preprocess/Stomach_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "5fec3494",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "95949586",
"metadata": {},
"outputs": [],
"source": [
"# 1. Let's first list the directory contents to understand what files are available\n",
"import os\n",
"\n",
"print(\"Files in the cohort directory:\")\n",
"files = os.listdir(in_cohort_dir)\n",
"print(files)\n",
"\n",
"# Adapt file identification to handle different naming patterns\n",
"soft_files = [f for f in files if 'soft' in f.lower() or '.soft' in f.lower() or '_soft' in f.lower()]\n",
"matrix_files = [f for f in files if 'matrix' in f.lower() or '.matrix' in f.lower() or '_matrix' in f.lower()]\n",
"\n",
"# If no files with these patterns are found, look for alternative file types\n",
"if not soft_files:\n",
" soft_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
"if not matrix_files:\n",
" matrix_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
"\n",
"print(\"Identified SOFT files:\", soft_files)\n",
"print(\"Identified matrix files:\", matrix_files)\n",
"\n",
"# Use the first files found, if any\n",
"if len(soft_files) > 0 and len(matrix_files) > 0:\n",
" soft_file = os.path.join(in_cohort_dir, soft_files[0])\n",
" matrix_file = os.path.join(in_cohort_dir, matrix_files[0])\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(\"\\nBackground Information:\")\n",
" print(background_info)\n",
" print(\"\\nSample Characteristics Dictionary:\")\n",
" print(sample_characteristics_dict)\n",
"else:\n",
" print(\"No appropriate files found in the directory.\")\n"
]
},
{
"cell_type": "markdown",
"id": "61199793",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15d7037e",
"metadata": {},
"outputs": [],
"source": [
"# Analyze the output to determine the dataset characteristics\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# From the background information, we can see this dataset contains gene expression data\n",
"# It mentions \"gene expression profile\" and \"HumanHT-12 v3.0 Expression BeadChip array (Illumina)\"\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Data Availability\n",
"# For the trait (Stomach Cancer), we can see all samples have \"disease: Gastric Cancer\" (key 0)\n",
"trait_row = 0\n",
"\n",
"# For age, there's no information available\n",
"age_row = None\n",
"\n",
"# For gender, there's no information available\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value to binary (1 for disease, 0 for control)\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" # Extract value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # All samples have gastric cancer, so all will be 1\n",
" if \"gastric cancer\" in value.lower():\n",
" return 1\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous\"\"\"\n",
" # Not used as age data is unavailable\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
" # Not used as gender data is unavailable\n",
" return None\n",
"\n",
"# 3. Save Metadata\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\n",
"if trait_row is not None:\n",
" # Create a DataFrame from the sample characteristics dictionary\n",
" # Based on the sample characteristics, we know the dataset contains 27 cell lines\n",
" # Each cell line has the same disease status (Gastric Cancer)\n",
" \n",
" # Create the clinical data DataFrame\n",
" sample_chars = {\n",
" 0: ['disease: Gastric Cancer'], \n",
" 1: ['organism part: Stomach'], \n",
" 2: ['cell line: Gastric Cancer Cell line'], \n",
" 3: ['cell line: Hs746T', 'cell line: YCC-16', 'cell line: YCC-2', 'cell line: SNU-16', \n",
" 'cell line: SNU-719', 'cell line: YCC-9', 'cell line: SNU-668', 'cell line: MKN-74', \n",
" 'cell line: SNU-1', 'cell line: SNU-5', 'cell line: MKN-45', 'cell line: SNU-638', \n",
" 'cell line: SNU-216', 'cell line: YCC-6', 'cell line: YCC-7', 'cell line: MKN-1', \n",
" 'cell line: MKN-28', 'cell line: NCI-N87', 'cell line: SNU-484', 'cell line: SNU-601', \n",
" 'cell line: SNU-620', 'cell line: YCC-3', 'cell line: YCC-11', 'cell line: YCC-1', \n",
" 'cell line: AGS', 'cell line: KATOIII', 'cell line: SNU-520']\n",
" }\n",
" \n",
" # Extract cell line names from the sample characteristics\n",
" cell_lines = [line.split(\": \")[1] for line in sample_chars[3]]\n",
" \n",
" # Create a DataFrame with all samples having the same trait value\n",
" clinical_data = pd.DataFrame(index=cell_lines)\n",
" \n",
" # Add sample characteristics as columns\n",
" for row_idx, values in sample_chars.items():\n",
" # Handle the case where row 3 has multiple values (one per cell line)\n",
" if row_idx == 3:\n",
" continue # Skip as we've already used these to create the index\n",
" \n",
" # For other rows, all cell lines share the same value\n",
" for value in values:\n",
" # Use the part before colon as column name, and after colon as value\n",
" if \":\" in value:\n",
" col_name, val = value.split(\":\", 1)\n",
" clinical_data[col_name.strip()] = val.strip()\n",
" \n",
" # Extract clinical features\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",
" # Preview the data\n",
" print(\"Preview of selected clinical data:\")\n",
" print(preview_df(selected_clinical_df))\n",
" \n",
" # Save the data\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"
]
},
{
"cell_type": "markdown",
"id": "efadfcc7",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc2a6aec",
"metadata": {},
"outputs": [],
"source": [
"# Use the helper function to get the proper file paths\n",
"soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Extract gene expression data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file_path)\n",
" \n",
" # Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
" \n",
" # Print shape to understand the dataset dimensions\n",
" print(f\"\\nGene expression data shape: {gene_data.shape}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "786f9d52",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ed1aacdb",
"metadata": {},
"outputs": [],
"source": [
"# Based on biomedical knowledge, these are Illumina probe IDs (indicated by the \"ILMN_\" prefix)\n",
"# and not human gene symbols. These probe IDs need to be mapped to gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "90a7c414",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "083ffd73",
"metadata": {},
"outputs": [],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"try:\n",
" # Use the correct variable name from previous steps\n",
" gene_annotation = get_gene_annotation(soft_file_path)\n",
" \n",
" # 2. Preview the gene annotation dataframe\n",
" print(\"Gene annotation preview:\")\n",
" print(preview_df(gene_annotation))\n",
" \n",
"except UnicodeDecodeError as e:\n",
" print(f\"Unicode decoding error: {e}\")\n",
" print(\"Trying alternative approach...\")\n",
" \n",
" # Read the file with Latin-1 encoding which is more permissive\n",
" import gzip\n",
" import pandas as pd\n",
" \n",
" # Manually read the file line by line with error handling\n",
" data_lines = []\n",
" with gzip.open(soft_file_path, 'rb') as f:\n",
" for line in f:\n",
" # Skip lines starting with prefixes we want to filter out\n",
" line_str = line.decode('latin-1')\n",
" if not line_str.startswith('^') and not line_str.startswith('!') and not line_str.startswith('#'):\n",
" data_lines.append(line_str)\n",
" \n",
" # Create dataframe from collected lines\n",
" if data_lines:\n",
" gene_data_str = '\\n'.join(data_lines)\n",
" gene_annotation = pd.read_csv(pd.io.common.StringIO(gene_data_str), sep='\\t', low_memory=False)\n",
" print(\"Gene annotation preview (alternative method):\")\n",
" print(preview_df(gene_annotation))\n",
" else:\n",
" print(\"No valid gene annotation data found after filtering.\")\n",
" gene_annotation = pd.DataFrame()\n",
" \n",
"except Exception as e:\n",
" print(f\"Error extracting gene annotation data: {e}\")\n",
" gene_annotation = pd.DataFrame()\n"
]
},
{
"cell_type": "markdown",
"id": "c60de818",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f9b46b6",
"metadata": {},
"outputs": [],
"source": [
"# 1. Determine the appropriate columns for mapping\n",
"# Based on the preview of gene_annotation, we can see:\n",
"# - 'ID' contains the Illumina probe IDs (same as in gene expression data)\n",
"# - 'Symbol' contains the gene symbols we need\n",
"\n",
"# 2. Get the gene mapping dataframe using the specified columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"\n",
"# Print a preview of the mapping\n",
"print(\"Preview of gene mapping:\")\n",
"print(gene_mapping.head())\n",
"print(f\"Gene mapping shape: {gene_mapping.shape}\")\n",
"\n",
"# 3. Convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Preview the resulting gene expression data\n",
"print(\"\\nPreview of mapped gene expression data:\")\n",
"print(gene_data.head())\n",
"print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
"\n",
"# Normalize gene symbols to handle synonyms\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(\"\\nPreview after normalizing gene symbols:\")\n",
"print(gene_data.head())\n",
"print(f\"Gene expression data shape after normalization: {gene_data.shape}\")\n",
"\n",
"# Save the processed 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 expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "2191b622",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eeffbed2",
"metadata": {},
"outputs": [],
"source": [
"# 1. Load the normalized gene data that was saved in the previous step\n",
"try:\n",
" gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
" print(f\"Loaded normalized gene data shape: {gene_data.shape}\")\n",
"except Exception as e:\n",
" print(f\"Error loading gene data: {e}\")\n",
" raise ValueError(\"Cannot proceed without gene expression data\")\n",
"\n",
"# 2. Load the clinical data created in Step 2\n",
"try:\n",
" clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
" print(f\"Loaded clinical data shape: {clinical_df.shape}\")\n",
" print(\"Clinical data preview:\")\n",
" print(clinical_df.head())\n",
"except Exception as e:\n",
" print(f\"Error loading clinical data: {e}\")\n",
" clinical_df = pd.DataFrame()\n",
"\n",
"# The sample IDs in the gene expression data are GSM IDs\n",
"gene_sample_ids = gene_data.columns\n",
"print(f\"Gene data has {len(gene_sample_ids)} samples\")\n",
"print(f\"First few sample IDs: {list(gene_sample_ids[:5])}\")\n",
"\n",
"# Create a clinical DataFrame with the same sample IDs as the gene data\n",
"# We know from previous steps that all samples are cancer cell lines (all have trait=1)\n",
"clinical_features = pd.DataFrame(index=gene_sample_ids)\n",
"clinical_features[trait] = 1 # All samples are gastric cancer cell lines\n",
"\n",
"print(f\"Prepared clinical features shape: {clinical_features.shape}\")\n",
"print(clinical_features.head())\n",
"\n",
"# Link 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",
"# 3. Handle 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. Determine whether the trait and demographic features are biased\n",
"is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"print(f\"Is trait biased: {is_trait_biased}\")\n",
"print(f\"Linked data shape after removing biased features: {linked_data.shape}\")\n",
"\n",
"# 5. Conduct quality check and save the cohort information\n",
"note = \"Dataset contains gene expression data from 27 gastric cancer cell lines. All samples have the same trait value (all are cancer), making the dataset unsuitable for associative studies.\"\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_trait_biased, \n",
" df=linked_data,\n",
" note=note\n",
")\n",
"\n",
"# 6. Save the linked data if it's usable\n",
"print(f\"Data quality check result: {'Usable' if is_usable else 'Not 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(f\"Data not saved due to quality issues.\")\n"
]
},
{
"cell_type": "markdown",
"id": "cf02978c",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1b6795e0",
"metadata": {},
"outputs": [],
"source": [
"# 1. The gene data was already normalized in Step 6\n",
"gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
"print(f\"Loaded normalized gene data shape: {gene_data.shape}\")\n",
"\n",
"# 2. Create clinical features for the same samples that are in the gene data\n",
"# From previous steps, we know all samples are gastric cancer cell lines\n",
"clinical_features = pd.DataFrame(index=gene_data.columns)\n",
"clinical_features[trait] = 1 # All samples are gastric cancer\n",
"print(f\"Prepared clinical features shape: {clinical_features.shape}\")\n",
"print(clinical_features.head())\n",
"\n",
"# Direct approach to link clinical and genetic data\n",
"linked_data = clinical_features.copy()\n",
"# Add gene expression data as additional columns\n",
"for gene in gene_data.index:\n",
" linked_data[gene] = gene_data.loc[gene]\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"linked_data_processed = handle_missing_values(linked_data, trait)\n",
"print(f\"Linked data shape after handling missing values: {linked_data_processed.shape}\")\n",
"\n",
"# 4. Determine whether the trait is biased\n",
"is_trait_biased, linked_data_processed = judge_and_remove_biased_features(linked_data_processed, trait)\n",
"print(f\"Is trait biased: {is_trait_biased}\")\n",
"print(f\"Linked data shape after removing biased features: {linked_data_processed.shape}\")\n",
"\n",
"# 5. Conduct quality check and save cohort information\n",
"note = \"Dataset contains gene expression data from 27 gastric cancer cell lines. All samples have the same trait value (all are cancer), making the dataset unsuitable for case-control associative studies.\"\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_trait_biased, \n",
" df=linked_data_processed,\n",
" note=note\n",
")\n",
"\n",
"# 6. Save the linked data if it's usable\n",
"print(f\"Data quality check result: {'Usable' if is_usable else 'Not usable'}\")\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data_processed.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(f\"Data not saved due to quality issues.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|