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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "fe5db780",
"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 = \"GSE161533\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Stomach_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Stomach_Cancer/GSE161533\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Stomach_Cancer/GSE161533.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Stomach_Cancer/gene_data/GSE161533.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Stomach_Cancer/clinical_data/GSE161533.csv\"\n",
"json_path = \"../../output/preprocess/Stomach_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "dd093a27",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "337ad9c6",
"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": "71824a8b",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c5b4067b",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# The dataset uses Affymetrix Gene Chip Human Genome U133 plus 2.0 Array, which contains gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# For trait: Examining the tissue type which indicates stomach cancer status\n",
"trait_row = 0 # 'tissue' field - has normal, paratumor, and tumor tissue types\n",
"\n",
"# For age: Age data is available in key 2\n",
"age_row = 2 # 'age' field with multiple values\n",
"\n",
"# For gender: Gender data is available in key 3\n",
"gender_row = 3 # 'gender' field with Male and Female values\n",
"\n",
"# 2.2 Data Type Conversion\n",
"\n",
"# Convert trait to binary (tumor vs non-tumor)\n",
"def convert_trait(value):\n",
" if not isinstance(value, str):\n",
" return None\n",
" value = value.lower().strip()\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"tumor tissue\" in value:\n",
" return 1 # Tumor tissue (case)\n",
" elif \"normal tissue\" in value:\n",
" return 0 # Normal tissue (control)\n",
" elif \"paratumor tissue\" in value:\n",
" return None # We'll exclude paratumor tissue as it's neither case nor control\n",
" return None\n",
"\n",
"# Convert age to continuous\n",
"def convert_age(value):\n",
" if not isinstance(value, str):\n",
" return None\n",
" value = value.lower().strip()\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except ValueError:\n",
" return None\n",
"\n",
"# Convert gender to binary (0=female, 1=male)\n",
"def convert_gender(value):\n",
" if not isinstance(value, str):\n",
" return None\n",
" value = value.lower().strip()\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"male\" in value:\n",
" return 1\n",
" elif \"female\" in value:\n",
" return 0\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Initial validation - checking if gene and trait data are available\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(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",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" # Extract clinical features using the function from the library\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 extracted clinical data\n",
" preview = preview_df(clinical_df)\n",
" print(\"Preview of clinical data:\")\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": "f000ac35",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dd404382",
"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": "81ac5eed",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de29d4e5",
"metadata": {},
"outputs": [],
"source": [
"# Examining the gene identifiers shown in the previous step\n",
"# These identifiers (e.g., '1007_s_at', '1053_at') appear to be Affymetrix probe IDs\n",
"# rather than standard human gene symbols (which would be like BRCA1, TP53, etc.)\n",
"# Affymetrix probe IDs need to be mapped to official gene symbols for biological interpretation\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "7dfb6770",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2fef7c0f",
"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": "0938a050",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1ad5024",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify the relevant columns in the gene annotation dataframe\n",
"# The gene expression data uses identifiers like '1007_s_at', which matches the 'ID' column in the gene annotation\n",
"# The gene symbols are stored in the 'Gene Symbol' column\n",
"\n",
"# 2. Create the gene mapping dataframe using the get_gene_mapping function\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
"print(\"\\nGene mapping dataframe (first 5 rows):\")\n",
"print(mapping_df.head())\n",
"print(f\"Shape of mapping dataframe: {mapping_df.shape}\")\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"print(\"\\nConverted gene expression data (first 5 genes, 5 samples):\")\n",
"if not gene_data.empty:\n",
" print(gene_data.iloc[:5, :5])\n",
" print(f\"\\nShape of gene expression data: {gene_data.shape}\")\n",
"else:\n",
" print(\"Gene expression data is empty after mapping.\")\n"
]
},
{
"cell_type": "markdown",
"id": "e2d618be",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2e1f3d4",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols in the obtained gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\n",
"\n",
"# Save the normalized gene data\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. Load the clinical data created in Step 2\n",
"# Inspect clinical data structure before manipulating\n",
"clinical_df = pd.read_csv(out_clinical_data_file)\n",
"print(f\"Loaded clinical data shape: {clinical_df.shape}\")\n",
"print(f\"Loaded clinical data columns: {clinical_df.columns.tolist()}\")\n",
"\n",
"# Create clinical features DataFrame with proper structure \n",
"# In this dataset, we know from step 2 that the first column is unnamed and contains feature names\n",
"clinical_features = clinical_df.copy()\n",
"# Set the first unnamed column as index if it exists\n",
"if 'Unnamed: 0' in clinical_features.columns:\n",
" clinical_features = clinical_features.set_index('Unnamed: 0') \n",
"clinical_features = clinical_features.T # Transpose to get samples as rows and features as columns\n",
"print(f\"Clinical features shape after format adjustment: {clinical_features.shape}\")\n",
"\n",
"# Link clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features.T, normalized_gene_data)\n",
"print(f\"Linked data shape after linking: {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",
"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=\"Dataset contains gene expression data from esophageal squamous cell carcinoma patients, with normal, paratumor, and tumor tissue samples.\"\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": "9973ed83",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b202f75",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols in the obtained gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\n",
"\n",
"# Save the normalized gene data\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. Load the clinical data created in Step 2\n",
"clinical_df = pd.read_csv(out_clinical_data_file)\n",
"print(f\"Loaded clinical data shape: {clinical_df.shape}\")\n",
"\n",
"# Prepare clinical data properly, understanding the data structure from previous steps\n",
"# The DataFrame needs to be transposed to have samples as rows and features as columns\n",
"clinical_features = pd.DataFrame()\n",
"for col in clinical_df.columns:\n",
" if col != 'Unnamed: 0': # Skip the unnamed index column if it exists\n",
" sample_id = col\n",
" # Get trait, age, gender values for this sample\n",
" values = clinical_df[col].values\n",
" if len(values) >= 3: # Make sure we have enough values\n",
" clinical_features.loc[sample_id, trait] = values[0] # Stomach_Cancer status\n",
" clinical_features.loc[sample_id, 'Age'] = values[1] # Age\n",
" clinical_features.loc[sample_id, 'Gender'] = values[2] # Gender\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 = pd.concat([clinical_features, normalized_gene_data.T], axis=1)\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",
"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=\"Dataset contains gene expression data from esophageal squamous cell carcinoma patients, with normal, paratumor, and tumor tissue samples.\"\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.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|