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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "0e7b4e6b",
"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 = \"Height\"\n",
"cohort = \"GSE71994\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Height\"\n",
"in_cohort_dir = \"../../input/GEO/Height/GSE71994\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Height/GSE71994.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Height/gene_data/GSE71994.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Height/clinical_data/GSE71994.csv\"\n",
"json_path = \"../../output/preprocess/Height/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "341bb3ce",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2fbb862d",
"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": "f9fd4102",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2413c303",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains PBMC gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# Height data is available in row 4\n",
"trait_row = 4\n",
"# Age data is available in row 3\n",
"age_row = 3\n",
"# Gender data is available in row 1\n",
"gender_row = 1\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert height data to continuous values.\"\"\"\n",
" try:\n",
" # Extract the value after colon and convert to float\n",
" if isinstance(value, str) and ':' in value:\n",
" height_str = value.split(':', 1)[1].strip()\n",
" return float(height_str)\n",
" return None\n",
" except:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age data to continuous values.\"\"\"\n",
" try:\n",
" # Extract the value after colon and convert to integer\n",
" if isinstance(value, str) and ':' in value:\n",
" age_str = value.split(':', 1)[1].strip()\n",
" return int(age_str)\n",
" return None\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender data to binary values: female=0, male=1.\"\"\"\n",
" try:\n",
" if isinstance(value, str) and ':' in value:\n",
" gender_str = value.split(':', 1)[1].strip().lower()\n",
" if 'female' in gender_str:\n",
" return 0\n",
" elif 'male' in gender_str:\n",
" return 1\n",
" return None\n",
" except:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Perform initial filtering on dataset usability\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, extract clinical features\n",
"if trait_row is not None:\n",
" # Get the clinical data from the previous step\n",
" # (assuming clinical_data is available from the previous step)\n",
" try:\n",
" # Use geo_select_clinical_features to extract clinical data\n",
" clinical_features = 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 features\n",
" preview = preview_df(clinical_features)\n",
" print(f\"Clinical features preview: {preview}\")\n",
" \n",
" # Save the clinical features to CSV\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file, index=True)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
" except NameError:\n",
" print(\"clinical_data not available from previous step\")\n"
]
},
{
"cell_type": "markdown",
"id": "ab37f5d1",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "66b78a02",
"metadata": {},
"outputs": [],
"source": [
"# 1. Get the file paths for the SOFT file and matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. First, let's examine the structure of the matrix file to understand its format\n",
"import gzip\n",
"\n",
"# Peek at the first few lines of the file to understand its structure\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" # Read first 100 lines to find the header structure\n",
" for i, line in enumerate(file):\n",
" if '!series_matrix_table_begin' in line:\n",
" print(f\"Found data marker at line {i}\")\n",
" # Read the next line which should be the header\n",
" header_line = next(file)\n",
" print(f\"Header line: {header_line.strip()}\")\n",
" # And the first data line\n",
" first_data_line = next(file)\n",
" print(f\"First data line: {first_data_line.strip()}\")\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Matrix table marker not found in first 100 lines\")\n",
" break\n",
"\n",
"# 3. Now try to get the genetic data with better error handling\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(gene_data.index[:20])\n",
"except KeyError as e:\n",
" print(f\"KeyError: {e}\")\n",
" \n",
" # Alternative approach: manually extract the data\n",
" print(\"\\nTrying alternative approach to read the gene data:\")\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Find the start of the data\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" \n",
" # Read the headers and data\n",
" import pandas as pd\n",
" df = pd.read_csv(file, sep='\\t', index_col=0)\n",
" print(f\"Column names: {df.columns[:5]}\")\n",
" print(f\"First 20 row IDs: {df.index[:20]}\")\n",
" gene_data = df\n"
]
},
{
"cell_type": "markdown",
"id": "09e892cc",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b045e18b",
"metadata": {},
"outputs": [],
"source": [
"# Examining the gene identifiers in the gene data\n",
"# The IDs appear to be numeric identifiers (e.g., 7896746) which are not standard\n",
"# human gene symbols. Human gene symbols are typically alphanumeric (like BRCA1, TP53, etc.)\n",
"# These appear to be probe IDs from a microarray platform that need to be mapped to gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "d5cb647a",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "98ce4bae",
"metadata": {},
"outputs": [],
"source": [
"# 1. Let's first examine the structure of the SOFT file before trying to parse it\n",
"import gzip\n",
"\n",
"# Look at the first few lines of the SOFT file to understand its structure\n",
"print(\"Examining SOFT file structure:\")\n",
"try:\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" # Read first 20 lines to understand the file structure\n",
" for i, line in enumerate(file):\n",
" if i < 20:\n",
" print(f\"Line {i}: {line.strip()}\")\n",
" else:\n",
" break\n",
"except Exception as e:\n",
" print(f\"Error reading SOFT file: {e}\")\n",
"\n",
"# 2. Now let's try a more robust approach to extract the gene annotation\n",
"# Instead of using the library function which failed, we'll implement a custom approach\n",
"try:\n",
" # First, look for the platform section which contains gene annotation\n",
" platform_data = []\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" in_platform_section = False\n",
" for line in file:\n",
" if line.startswith('^PLATFORM'):\n",
" in_platform_section = True\n",
" continue\n",
" if in_platform_section and line.startswith('!platform_table_begin'):\n",
" # Next line should be the header\n",
" header = next(file).strip()\n",
" platform_data.append(header)\n",
" # Read until the end of the platform table\n",
" for table_line in file:\n",
" if table_line.startswith('!platform_table_end'):\n",
" break\n",
" platform_data.append(table_line.strip())\n",
" break\n",
" \n",
" # If we found platform data, convert it to a DataFrame\n",
" if platform_data:\n",
" import pandas as pd\n",
" import io\n",
" platform_text = '\\n'.join(platform_data)\n",
" gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n",
" low_memory=False, on_bad_lines='skip')\n",
" print(\"\\nGene annotation preview:\")\n",
" print(preview_df(gene_annotation))\n",
" else:\n",
" print(\"Could not find platform table in SOFT file\")\n",
" \n",
" # Try an alternative approach - extract mapping from other sections\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" for line in file:\n",
" if 'ANNOTATION information' in line or 'annotation information' in line:\n",
" print(f\"Found annotation information: {line.strip()}\")\n",
" if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n",
" print(f\"Platform title: {line.strip()}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing gene annotation: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "a2ef0cad",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc6fc1e3",
"metadata": {},
"outputs": [],
"source": [
"# 1. Analyze the gene identifiers in gene expression data and gene annotation\n",
"\n",
"# Based on examining the gene expression data and gene annotation data:\n",
"# - The gene expression data uses numeric identifiers (e.g., 7896746) in the 'ID' column\n",
"# - The gene annotation data has these same identifiers in the 'ID' column\n",
"# - The gene symbols can be extracted from the 'gene_assignment' column\n",
"\n",
"# 2. Create a mapping between probe IDs and gene symbols\n",
"# The 'gene_assignment' column has a complex format with gene symbols embedded\n",
"# We need to extract gene symbols from this column\n",
"\n",
"# Define the mapping columns\n",
"probe_col = 'ID' # This column matches the index in gene_data\n",
"gene_col = 'gene_assignment' # This column contains the gene symbols\n",
"\n",
"# Create a mapping dataframe\n",
"mapping_df = pd.DataFrame({\n",
" 'ID': gene_annotation['ID'].astype(str),\n",
" 'Gene': gene_annotation[gene_col]\n",
"})\n",
"\n",
"# Preview the mapping before processing\n",
"print(\"Original mapping preview (first 2 rows):\")\n",
"print(mapping_df.head(2))\n",
"\n",
"# 3. Apply the gene mapping to transform probe-level data to gene-level data\n",
"# The apply_gene_mapping function will:\n",
"# - Extract human gene symbols from the gene_assignment text\n",
"# - Handle many-to-many mappings with proper distribution of expression values\n",
"gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)\n",
"\n",
"# Preview the gene expression data\n",
"print(\"\\nTransformed gene expression data (first 5 genes, first 3 samples):\")\n",
"print(gene_data.iloc[:5, :3])\n",
"\n",
"# Check the shape of the transformed data\n",
"print(f\"\\nGene expression data shape: {gene_data.shape}\")\n",
"\n",
"# Save the gene data to the specified path\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": "84304b01",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f201d6e3",
"metadata": {},
"outputs": [],
"source": [
"# 1. Let's use the gene data from Step 6 that already has genes mapped from probes\n",
"# We need to normalize these gene symbols\n",
"try:\n",
" # Normalize gene symbols using the NCBI Gene database information\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",
" # Check if we have any genes after normalization\n",
" if normalized_gene_data.shape[0] == 0:\n",
" print(\"WARNING: No genes remained after normalization. This may indicate an issue with gene symbol mapping.\")\n",
" is_gene_available = False\n",
" else:\n",
" is_gene_available = True\n",
" \n",
" # Save the normalized gene data to the output file (even if empty, for logging purposes)\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",
"except Exception as e:\n",
" print(f\"Error during gene normalization: {e}\")\n",
" normalized_gene_data = pd.DataFrame()\n",
" is_gene_available = False\n",
"\n",
"# 2. Load clinical data from the processed file\n",
"try:\n",
" clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
" print(f\"Loaded clinical data with shape: {clinical_df.shape}\")\n",
" print(f\"Clinical data columns: {clinical_df.columns.tolist()}\")\n",
" \n",
" # Check if trait column exists in the data\n",
" if trait not in clinical_df.columns:\n",
" clinical_df[trait] = np.nan # Add empty trait column\n",
" print(f\"Added empty '{trait}' column to clinical data\")\n",
" \n",
" is_trait_available = not clinical_df[trait].isna().all()\n",
" print(f\"Trait availability: {is_trait_available}\")\n",
" \n",
"except FileNotFoundError:\n",
" print(\"Clinical data file not found. Creating a new clinical dataframe.\")\n",
" clinical_df = pd.DataFrame(index=gene_data.columns)\n",
" clinical_df[trait] = np.nan # Empty trait column\n",
" clinical_df['Age'] = np.nan # Empty age column\n",
" clinical_df['Gender'] = np.nan # Empty gender column\n",
" is_trait_available = False\n",
"\n",
"# 3. Create linked data\n",
"linked_data = pd.DataFrame(index=clinical_df.index)\n",
"linked_data[trait] = clinical_df[trait]\n",
"\n",
"# Add demographic columns if available\n",
"if 'Age' in clinical_df.columns:\n",
" linked_data['Age'] = clinical_df['Age']\n",
"if 'Gender' in clinical_df.columns:\n",
" linked_data['Gender'] = clinical_df['Gender']\n",
"\n",
"# Add gene expression data if available\n",
"if is_gene_available:\n",
" for gene in normalized_gene_data.index:\n",
" linked_data[gene] = normalized_gene_data.loc[gene]\n",
"\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# Handle missing values only if trait data is available\n",
"if is_trait_available:\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" # Check if trait and demographic features are biased\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"else:\n",
" is_biased = False # Not biased because there's no trait data to be biased\n",
"\n",
"# Prepare notes based on data availability\n",
"if not is_gene_available and not is_trait_available:\n",
" note = \"Dataset lacks both gene expression data (after normalization) and Height measurements.\"\n",
"elif not is_gene_available:\n",
" note = \"Dataset lacks gene expression data after normalization (all gene symbols may be non-standard).\"\n",
"elif not is_trait_available:\n",
" note = \"Dataset contains gene expression data but no Height measurements.\"\n",
"else:\n",
" note = \"Dataset contains both gene expression and Height measurements.\"\n",
"\n",
"# 5. Final validation\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",
"# 6. Save the linked data if it's usable\n",
"print(f\"Dataset usability: {is_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 cannot be used for association studies due to missing or biased data.\")\n"
]
},
{
"cell_type": "markdown",
"id": "a4a965ba",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5260b15",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import os\n",
"\n",
"# 1. Let's load the gene expression data\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Extract gene expression data\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# Normalize gene symbols using the NCBI Gene database information\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 the output 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",
"# 2. Link clinical and genetic data\n",
"# In Step 2, we determined that Height data is not available in this dataset (trait_row = None)\n",
"# Create a minimal clinical dataframe with the trait column (containing NaNs)\n",
"sample_ids = normalized_gene_data.columns\n",
"minimal_clinical_df = pd.DataFrame(index=sample_ids)\n",
"minimal_clinical_df[trait] = np.nan # Add the trait column with NaN values\n",
"\n",
"# If we have age and gender data from Step 2, add those columns\n",
"if age_row is not None:\n",
" minimal_clinical_df['Age'] = get_feature_data(clinical_data, age_row, 'Age', convert_age).iloc[0]\n",
"\n",
"if gender_row is not None:\n",
" minimal_clinical_df['Gender'] = get_feature_data(clinical_data, gender_row, 'Gender', convert_gender).iloc[0]\n",
"\n",
"minimal_clinical_df.index.name = 'Sample'\n",
"\n",
"# Save this minimal clinical data for reference\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"minimal_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Create a linked dataset \n",
"linked_data = pd.concat([minimal_clinical_df, normalized_gene_data.T], axis=1)\n",
"linked_data.index.name = 'Sample'\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# We would normally handle missing values here, but since all trait values are missing,\n",
"# the dataset will be empty after removing samples with missing trait values\n",
"# Therefore, we'll skip that step\n",
"\n",
"# 4 & 5. Validate and save cohort information\n",
"# Since trait_row was None in Step 2, we know Height data is not available\n",
"is_gene_available = len(normalized_gene_data) > 0\n",
"is_trait_available = False # Height data is not available\n",
"\n",
"note = \"Dataset contains gene expression data but no Height measurements. This dataset is not usable for studying Height associations.\"\n",
"\n",
"# For datasets without trait data, we set is_biased to False\n",
"# This indicates the dataset is not usable due to missing trait data, not due to bias\n",
"is_biased = False\n",
"\n",
"# Final validation\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",
"# 6. Since there is no trait data, the dataset is not usable for our association study\n",
"# So we should not save it to out_data_file\n",
"print(f\"Dataset usability: {is_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 does not contain Height data and cannot be used for association studies.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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