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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "704f0c67",
"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 = \"Essential_Thrombocythemia\"\n",
"cohort = \"GSE159514\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Essential_Thrombocythemia\"\n",
"in_cohort_dir = \"../../input/GEO/Essential_Thrombocythemia/GSE159514\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Essential_Thrombocythemia/GSE159514.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE159514.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE159514.csv\"\n",
"json_path = \"../../output/preprocess/Essential_Thrombocythemia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "342bd257",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b6ddcfa",
"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": "7a466ed0",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eeccfaa8",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on background information, this dataset contains gene expression profiling data from microarray\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# For trait: The trait is available from the 'disease' field (row 0)\n",
"trait_row = 0\n",
"\n",
"# For age and gender: Not available in the sample characteristics dictionary\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"Convert trait value to binary (0 for control, 1 for case)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if applicable\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Based on the context, this is a study on myelofibrosis\n",
" # Essential Thrombocythemia (ET) specifically relates to PET (Post-ET myelofibrosis)\n",
" if 'PET' in value: # Post-ET myelofibrosis is related to Essential Thrombocythemia\n",
" return 1\n",
" else:\n",
" return 0 # Other conditions (PPV, Overt-PMF, Pre-PMF) are not Essential Thrombocythemia\n",
"\n",
"def convert_age(value: str) -> Optional[float]:\n",
" \"\"\"Convert age value to continuous number\"\"\"\n",
" # Not available in this dataset\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> Optional[int]:\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
" # Not available in this dataset\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Conduct initial filtering on usability\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 proper DataFrame from the sample characteristics dictionary\n",
" # The dictionary has two columns (0 and 1) which need to be converted to a DataFrame with proper shape\n",
" \n",
" # First, create a dictionary where keys are column names and values are column data\n",
" sample_chars = {\n",
" 0: ['disease: PPV', 'disease: Overt-PMF', 'disease: PET', 'disease: Pre-PMF'],\n",
" 1: ['driver mutation: JAK2V617F', 'driver mutation: CALR Type 1', \n",
" 'driver mutation: MPL', 'driver mutation: TN', \n",
" 'driver mutation: CALR Type 2', 'driver mutation: CALR', \n",
" 'driver mutation: JAK2 ex12']\n",
" }\n",
" \n",
" # For the geo_select_clinical_features function, we need a DataFrame where each row is a feature\n",
" # and each column is a sample. For this simple example, reshape it appropriately\n",
" clinical_data = pd.DataFrame()\n",
" for i, values in sample_chars.items():\n",
" # Add each feature as a row\n",
" row_df = pd.DataFrame([values])\n",
" clinical_data = pd.concat([clinical_data, row_df], ignore_index=True)\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 clinical data\n",
" print(\"Preview of selected clinical data:\")\n",
" preview = preview_df(selected_clinical_df)\n",
" print(preview)\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save clinical data as CSV\n",
" selected_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": "1dc156dd",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b2b9e18",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"import pandas as pd\n",
"from typing import Callable, Optional, Dict, Any\n",
"\n",
"# Check for required data files\n",
"clinical_data_path = os.path.join(in_cohort_dir, \"sample_characteristics.csv\")\n",
"if os.path.exists(clinical_data_path):\n",
" clinical_data = pd.read_csv(clinical_data_path)\n",
" print(f\"Clinical data loaded with shape {clinical_data.shape}\")\n",
"else:\n",
" print(f\"Clinical data file not found at {clinical_data_path}\")\n",
" clinical_data = pd.DataFrame()\n",
"\n",
"metadata_path = os.path.join(in_cohort_dir, \"metadata.txt\")\n",
"if os.path.exists(metadata_path):\n",
" with open(metadata_path, 'r') as f:\n",
" metadata = f.read()\n",
" print(f\"Metadata file loaded ({len(metadata)} characters)\")\n",
"else:\n",
" metadata = \"\"\n",
" print(f\"Metadata file not found at {metadata_path}\")\n",
"\n",
"# Check for gene expression data\n",
"matrix_path = os.path.join(in_cohort_dir, \"matrix.csv\")\n",
"is_gene_available = os.path.exists(matrix_path)\n",
"if is_gene_available:\n",
" print(f\"Gene expression matrix file found at {matrix_path}\")\n",
"else:\n",
" print(\"Gene expression matrix file not found, setting is_gene_available to False\")\n",
"\n",
"# Since we don't have clinical data, we can't identify trait, age, and gender rows\n",
"# Set all to None to indicate data is not available\n",
"trait_row = None\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# Define conversion functions for completeness, but they won't be used since data is not available\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value to binary (0 for control, 1 for Essential Thrombocythemia)\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" value = str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if any(term in value.lower() for term in ['et', 'essential thrombocythemia', 'thrombocythaemia']):\n",
" return 1\n",
" elif any(term in value.lower() for term in ['control', 'healthy', 'normal']):\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous numeric format\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" value = str(value)\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" import re\n",
" age_match = re.search(r'(\\d+)', value)\n",
" if age_match:\n",
" return float(age_match.group(1))\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" value = str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if any(term in value for term in ['female', 'f', 'woman']):\n",
" return 0\n",
" elif any(term in value for term in ['male', 'm', 'man']):\n",
" return 1\n",
" return None\n",
"\n",
"# Check if trait data is available\n",
"is_trait_available = trait_row is not None\n",
"print(f\"Trait data available: {is_trait_available}\")\n",
"\n",
"# Save metadata using the library function\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",
"print(f\"Cohort metadata saved to {json_path}\")\n",
"print(f\"Dataset analysis complete. Gene data available: {is_gene_available}, Trait data available: {is_trait_available}\")\n",
"\n",
"# We skip clinical feature extraction since trait_row is None (data not available)\n",
"if trait_row is not None:\n",
" # Use geo_select_clinical_features function to 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",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected 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",
" selected_clinical_df.to_csv(out_clinical_data_file, index=True)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"else:\n",
" print(\"Skipping clinical feature extraction as trait data is not available\")\n"
]
},
{
"cell_type": "markdown",
"id": "3ef04f37",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d38310a4",
"metadata": {},
"outputs": [],
"source": [
"# 1. First get the file paths again to access the matrix file\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 from the matrix_file\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(\"First 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "d04329b7",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "364cb8ce",
"metadata": {},
"outputs": [],
"source": [
"# Looking at the gene identifiers, I can see they follow the format like \"11715100_at\", \"11715101_s_at\", etc.\n",
"# These are not human gene symbols but appear to be Affymetrix probe IDs\n",
"# They will require mapping to human gene symbols for meaningful biological interpretation\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "25410565",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc744654",
"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": "d2adb263",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce58e838",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify the relevant columns for mapping\n",
"probe_id_column = 'ID'\n",
"gene_symbol_column = 'Gene Symbol'\n",
"\n",
"# 2. Get a gene mapping dataframe by extracting the relevant columns\n",
"mapping_df = get_gene_mapping(gene_annotation, probe_id_column, gene_symbol_column)\n",
"print(f\"Created mapping between {probe_id_column} and {gene_symbol_column}\")\n",
"print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n",
"print(\"First 5 rows of mapping dataframe:\")\n",
"print(mapping_df.head())\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(f\"Mapped gene expression data shape: {gene_data.shape}\")\n",
"print(\"First 10 genes in mapped expression data:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# 4. Normalize gene symbols (if needed)\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene expression data shape: {gene_data.shape}\")\n",
"print(\"First 10 genes after normalization:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# 5. 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": "152d4d15",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6bd69386",
"metadata": {},
"outputs": [],
"source": [
"# 1. Re-acquire gene data as it seems to be missing in this context\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# Extract gene annotation and mapping again\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# 1. Normalize gene symbols in the gene expression data\n",
"print(\"Normalizing gene symbols...\")\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
"\n",
"# Save the normalized gene data to a CSV file\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",
"# Define trait availability based on previous analysis steps\n",
"trait_row = None # From previous steps we determined trait data wasn't properly available\n",
"is_gene_available = True\n",
"is_trait_available = False\n",
"\n",
"# 2. Check if trait data is available before attempting to link\n",
"if trait_row is not None:\n",
" print(\"Loading the original clinical data...\")\n",
" background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
"\n",
" print(\"Extracting clinical features...\")\n",
" # Define conversion functions\n",
" def convert_trait(value):\n",
" if isinstance(value, str) and ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" return 1 if 'PET' in str(value) else 0\n",
" \n",
" def convert_age(value):\n",
" return None\n",
" \n",
" def convert_gender(value):\n",
" return None\n",
" \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",
" print(\"Clinical data preview:\")\n",
" print(preview_df(selected_clinical_df))\n",
"\n",
" # Save the clinical data to a CSV file\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",
"\n",
" # Link clinical and genetic data using the normalized gene data\n",
" print(\"Linking clinical and genetic data...\")\n",
" linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
" # 3. Handle missing values in the linked data\n",
" print(\"Handling 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. Check if trait is biased\n",
" print(\"Checking for bias in trait distribution...\")\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"else:\n",
" print(\"Trait data is not available, skipping clinical data extraction and linking.\")\n",
" # Set variables for final validation\n",
" is_biased = None # Cannot determine bias without trait data\n",
" linked_data = pd.DataFrame() # Empty dataframe for validation\n",
"\n",
"# 5. Final validation\n",
"note = \"Dataset contains gene expression data from patients with myelofibrosis, but Essential Thrombocythemia specific annotations are not available in proper format.\"\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=note\n",
")\n",
"\n",
"print(f\"Dataset usability: {is_usable}\")\n",
"\n",
"# 6. Save linked data if 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(\"Dataset is not usable for trait-gene association studies due to lack of proper trait annotations.\")\n"
]
},
{
"cell_type": "markdown",
"id": "25ba5ab2",
"metadata": {},
"source": [
"### Step 9: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "853fbe3b",
"metadata": {},
"outputs": [],
"source": [
"# 1. Reload gene data and perform gene mapping since it's not available in this context\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"gene_data = get_genetic_data(matrix_file)\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Normalize gene symbols in the gene expression data\n",
"print(\"Normalizing gene symbols...\")\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
"\n",
"# Save the normalized gene data to a CSV file\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",
"# Define the trait availability based on previous steps\n",
"is_trait_available = trait_row is not None\n",
"is_gene_available = True # We have gene data\n",
"\n",
"# Skip clinical data extraction and linking since trait data is unavailable\n",
"print(\"Trait data is not available, skipping clinical data extraction and linking.\")\n",
"linked_data = pd.DataFrame() # Empty dataframe since we can't link\n",
"is_biased = False # Cannot determine bias for non-existent trait data\n",
"\n",
"# Final validation\n",
"note = \"Dataset contains gene expression data from myelofibrosis patients, but Essential Thrombocythemia specific annotations are not properly available for trait-gene association 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=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=note\n",
")\n",
"\n",
"print(f\"Dataset usability: {is_usable}\")\n",
"\n",
"# Save linked data if usable (will not execute since is_usable will be False)\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(\"Dataset is not usable for trait-gene association studies due to lack of proper trait annotations.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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