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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e74b8683",
"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 = \"Rectal_Cancer\"\n",
"cohort = \"GSE170999\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Rectal_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Rectal_Cancer/GSE170999\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Rectal_Cancer/GSE170999.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Rectal_Cancer/gene_data/GSE170999.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Rectal_Cancer/clinical_data/GSE170999.csv\"\n",
"json_path = \"../../output/preprocess/Rectal_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "e55782a8",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "86fa40bb",
"metadata": {},
"outputs": [],
"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": "55710cf0",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21c47658",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the Series_summary information, this dataset contains gene expression data from Affymetrix U133 platform\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Identifying rows containing trait, age, and gender information\n",
"trait_row = 0 # KRAS mutation status is in row 0\n",
"age_row = None # Age information is not available\n",
"gender_row = None # Gender information is not available\n",
"\n",
"# 2.2 Data Type Conversion functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert KRAS mutation status to binary (0: wild-type, 1: mutant)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after the colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to binary\n",
" if \"wild-type\" in value.lower():\n",
" return 0 # KRAS wild-type\n",
" elif \"mutant\" in value.lower():\n",
" return 1 # KRAS mutant\n",
" else:\n",
" return None # Unknown or other values\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to numeric (continuous) value\"\"\"\n",
" # Not used since age data is not available\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary (0: female, 1: male)\"\"\"\n",
" # Not used since gender data is not available\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",
"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 not None, clinical data is available\n",
"if trait_row is not None:\n",
" # Create clinical data DataFrame from the sample characteristics dictionary\n",
" sample_chars = {0: ['kras_mutant or wild_type: KRAS-wild-type', 'kras_mutant or wild_type: KRAS-mutant']}\n",
" clinical_data = pd.DataFrame()\n",
" \n",
" for key, values in sample_chars.items():\n",
" clinical_data[key] = values\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 extracted features\n",
" print(\"Clinical Features Preview:\")\n",
" print(preview_df(selected_clinical_df))\n",
" \n",
" # Save the clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n"
]
},
{
"cell_type": "markdown",
"id": "5c7fb41a",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18d07a12",
"metadata": {},
"outputs": [],
"source": [
"# 1. First get the file paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "a7610db1",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "47af28d2",
"metadata": {},
"outputs": [],
"source": [
"# These identifiers are not standard human gene symbols, which typically follow conventions like \n",
"# HGNC symbols (e.g., BRCA1, TP53) or Ensembl IDs (starting with ENSG).\n",
"# \n",
"# The identifiers shown (like '1007_s_at', '1053_at') appear to be Affymetrix probe IDs,\n",
"# which are specific to the microarray platform used for gene expression profiling.\n",
"# These need to be mapped to standard gene symbols for meaningful analysis.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "0129196c",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9bd0c5c0",
"metadata": {},
"outputs": [],
"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": "0b33c6d6",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7ba66294",
"metadata": {},
"outputs": [],
"source": [
"# 1. Based on the previews, we need to map:\n",
"# - 'ID' in the gene_annotation dataframe (contains probe IDs like '1007_s_at')\n",
"# - 'Gene Symbol' in the gene_annotation dataframe (contains gene symbols like 'DDR1 /// MIR4640')\n",
"\n",
"# 2. Get gene mapping dataframe by extracting the identifier and symbol columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"# This function handles dividing expression values among multiple genes and summing by gene\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print preview of gene expression data after mapping\n",
"print(\"Gene expression data after mapping (first 5 genes):\")\n",
"print(gene_data.head())\n"
]
},
{
"cell_type": "markdown",
"id": "ff1e46af",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "44e0c5a0",
"metadata": {},
"outputs": [],
"source": [
"# 1. Extract clinical features from the original clinical_data\n",
"clinical_features = geo_select_clinical_features(\n",
" 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",
"# Save the clinical features data\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 data saved to {out_clinical_data_file}\")\n",
"\n",
"# 1. Normalize gene symbols in the 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. Link the clinical and genetic data\n",
"# Ensure clinical data has the proper format\n",
"clinical_df = clinical_features.copy()\n",
"clinical_df = clinical_df.T\n",
"clinical_df.columns = [trait]\n",
"\n",
"linked_data = pd.concat([clinical_df, normalized_gene_data.T], axis=1)\n",
"print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n",
"print(linked_data.head())\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"linked_data_cleaned = handle_missing_values(linked_data, trait)\n",
"print(f\"Shape after handling missing values: {linked_data_cleaned.shape}\")\n",
"\n",
"# Check if we have sufficient data after handling missing values\n",
"if linked_data_cleaned.shape[0] == 0 or linked_data_cleaned.shape[1] <= 1:\n",
" print(f\"Insufficient data after handling missing values. All samples were filtered out.\")\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, # Set to False since we have no usable trait data\n",
" is_biased=None, \n",
" df=linked_data_cleaned,\n",
" note=f\"No usable samples after handling missing values. All samples had missing trait values.\"\n",
" )\n",
" print(f\"Data quality check failed. The dataset is not suitable for association studies.\")\n",
"else:\n",
" # 4. Determine whether the trait and demographic features are severely biased\n",
" is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, trait)\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=unbiased_linked_data,\n",
" note=f\"Dataset contains gene expression data from rectal cancer patients with KRAS mutation status.\"\n",
" )\n",
"\n",
" # 6. Save the data if it's usable\n",
" if is_usable:\n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" # Save the data\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(f\"Data quality check failed. The dataset is not suitable for association studies.\")\n"
]
},
{
"cell_type": "markdown",
"id": "15f885df",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1571787a",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols in the 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. Extract KRAS mutation information from the SOFT file\n",
"# Read the SOFT file to look for sample characteristics that indicate KRAS status for each sample\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" soft_content = f.read()\n",
"\n",
"# Extract sample blocks from the SOFT file\n",
"sample_blocks = re.findall(r'^\\^SAMPLE = (GSM\\d+).*?!Sample_title = \"(.*?)\".*?!Sample_characteristics_ch1 = (.*?)(?=\\n\\n|\\n\\^|\\Z)', \n",
" soft_content, re.DOTALL | re.MULTILINE)\n",
"\n",
"# Create a dictionary to map sample IDs to KRAS status\n",
"kras_status = {}\n",
"for sample_id, title, characteristics in sample_blocks:\n",
" # Look for KRAS status in the characteristics\n",
" if 'KRAS-mutant' in characteristics or 'KRAS-mutant' in title:\n",
" kras_status[sample_id] = 1 # Mutant\n",
" elif 'KRAS-wild-type' in characteristics or 'KRAS-wild-type' in title:\n",
" kras_status[sample_id] = 0 # Wild-type\n",
" else:\n",
" # If not found in characteristics, try to extract from sample title\n",
" if 'KRAS-mutant' in title.lower():\n",
" kras_status[sample_id] = 1\n",
" elif 'KRAS-wild-type' in title.lower() or 'KRAS-wt' in title.lower():\n",
" kras_status[sample_id] = 0\n",
"\n",
"# Create a clinical DataFrame with sample IDs as index\n",
"sample_ids = normalized_gene_data.columns\n",
"clinical_df = pd.DataFrame(index=sample_ids)\n",
"\n",
"# Fill in the KRAS status for each sample\n",
"clinical_df[trait] = clinical_df.index.map(lambda x: kras_status.get(x))\n",
"\n",
"# Save the clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"print(f\"Sample KRAS status: {clinical_df[trait].value_counts().to_dict()}\")\n",
"\n",
"# 3. Link the clinical and genetic data\n",
"linked_data = pd.concat([clinical_df, normalized_gene_data.T], axis=1)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(linked_data.head())\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"linked_data_cleaned = handle_missing_values(linked_data, trait)\n",
"print(f\"Shape after handling missing values: {linked_data_cleaned.shape}\")\n",
"\n",
"# Check if we still have data after handling missing values\n",
"if linked_data_cleaned.shape[0] == 0 or linked_data_cleaned.shape[1] <= 1:\n",
" print(\"All samples were filtered out during missing value handling.\")\n",
" # Create a minimal DataFrame for validation purposes\n",
" dummy_df = pd.DataFrame({trait: [0, 1]})\n",
" # Validate and save information indicating the dataset is not usable\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=dummy_df,\n",
" note=\"Dataset contains gene expression data from rectal cancer patients with KRAS mutation status, but sample IDs couldn't be properly linked between clinical and genetic data.\"\n",
" )\n",
" print(f\"Data quality check failed. The dataset is not suitable for association studies.\")\n",
"else:\n",
" # 5. Determine whether the trait and demographic features are severely biased\n",
" is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, trait)\n",
"\n",
" # 6. 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=unbiased_linked_data,\n",
" note=f\"Dataset contains gene expression data from rectal cancer patients with KRAS mutation status.\"\n",
" )\n",
"\n",
" # 7. Save the data if it's usable\n",
" if is_usable:\n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" # Save the data\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(f\"Data quality check failed. The dataset is not suitable for association studies.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|