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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "5b8717cf",
"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 = \"GSE131835\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Height\"\n",
"in_cohort_dir = \"../../input/GEO/Height/GSE131835\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Height/GSE131835.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Height/gene_data/GSE131835.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Height/clinical_data/GSE131835.csv\"\n",
"json_path = \"../../output/preprocess/Height/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "03ab6c0c",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "13683a3d",
"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": "86769e3d",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5613373d",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains gene expression data from adipose tissues\n",
"# The description mentions using Affymetrix Clariom S Microarray to analyze gene expression\n",
"# It's not just miRNA or methylation data, so gene expression is available\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# Height data is available in the row 5 as 'height(cm): XXX'\n",
"trait_row = 5 # height is available in row 5\n",
"\n",
"# Age data is available in row 3 as 'age: XX'\n",
"age_row = 3 # age is available in row 3\n",
"\n",
"# Gender/Sex data is available in row 2 as 'Sex: Male/Female' \n",
"gender_row = 2 # gender is available in row 2\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert height values to numeric (continuous) format.\"\"\"\n",
" try:\n",
" # Extract the number after the 'height(cm):' prefix\n",
" if \":\" in value:\n",
" height_str = value.split(\":\", 1)[1].strip()\n",
" return float(height_str)\n",
" else:\n",
" return None\n",
" except (ValueError, IndexError):\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age values to numeric (continuous) format.\"\"\"\n",
" try:\n",
" # Extract the number after the 'age:' prefix\n",
" if \":\" in value:\n",
" age_str = value.split(\":\", 1)[1].strip()\n",
" return float(age_str)\n",
" else:\n",
" return None\n",
" except (ValueError, IndexError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary format (0 for female, 1 for male).\"\"\"\n",
" try:\n",
" if \":\" 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",
" else:\n",
" return None\n",
" else:\n",
" return None\n",
" except (ValueError, IndexError):\n",
" return None\n",
"\n",
"# 3. Save Metadata - Initial Filtering\n",
"# Trait data is available since trait_row is not None\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort info (initial filtering)\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",
"# Only proceed if trait_row is not None, which it is in this case\n",
"if trait_row is not None:\n",
" # Create a sample characteristics dataframe in a format that works with geo_select_clinical_features\n",
" # Create a dictionary with the sample characteristics data\n",
" sample_chars = {0: ['tissue: Visceral', 'tissue: SubCut'], \n",
" 1: ['group: CWS', 'group: CWL', 'group: CONTROL', 'group: CONTROl'], \n",
" 2: ['Sex: Male', 'Sex: Female'], \n",
" 3: ['age: 51', 'age: 64', 'age: 62', 'age: 78', 'age: 47', 'age: 59', 'age: 57', 'age: 58', \n",
" 'age: 53', 'age: 49', 'age: 54', 'age: 60', 'age: 56', 'age: 41', 'age: 76', 'age: 81', \n",
" 'age: 48', 'age: 65', 'age: 68', 'age: 72'], \n",
" 4: ['tumour: Oesophageal adenocarcinoma', 'tumour: Oesophageal SCC', 'tumour: Gastric adenocarcinoma', \n",
" 'tumour: N/A', 'tumour: Gastric'], \n",
" 5: ['height(cm): 178', 'height(cm): 170', 'height(cm): 166', 'height(cm): 160', 'height(cm): 180', \n",
" 'height(cm): 163', 'height(cm): 183', 'height(cm): 172', 'height(cm): 169', 'height(cm): 158', \n",
" 'height(cm): 173', 'height(cm): 193', 'height(cm): 152', 'height(cm): 167', 'height(cm): 168', \n",
" 'height(cm): 177', 'height(cm): 165', 'height(cm): 179', 'height(cm): 190']}\n",
" \n",
" # Create sample IDs (columns) equal to the maximum number of samples\n",
" max_samples = max(len(values) for values in sample_chars.values())\n",
" sample_ids = [f\"Sample_{i+1}\" for i in range(max_samples)]\n",
" \n",
" # Create a DataFrame with NaN values\n",
" clinical_data = pd.DataFrame(index=range(max(sample_chars.keys()) + 1), columns=sample_ids)\n",
" \n",
" # Fill the DataFrame with sample characteristics data\n",
" for row_idx, row_values in sample_chars.items():\n",
" for col_idx, value in enumerate(row_values):\n",
" if col_idx < len(sample_ids):\n",
" clinical_data.iloc[row_idx, col_idx] = value\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 dataframe\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Clinical Data Preview:\")\n",
" print(pd.DataFrame(preview))\n",
" \n",
" # Save the processed 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",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "28193d69",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21d5dad6",
"metadata": {},
"outputs": [],
"source": [
"```python\n",
"# Analyzing dataset files and structure\n",
"import os\n",
"\n",
"# List files in the cohort directory to understand what's available\n",
"print(\"Files in cohort directory:\")\n",
"try:\n",
" cohort_files = os.listdir(in_cohort_dir)\n",
" for file in cohort_files:\n",
" print(f\"- {file}\")\n",
"except Exception as e:\n",
" print(f\"Error accessing directory: {e}\")\n",
"\n",
"# Check for common GEO file patterns\n",
"soft_file = None\n",
"matrix_file = None\n",
"family_file = None\n",
"for file in os.listdir(in_cohort_dir) if os.path.exists(in_cohort_dir) else []:\n",
" if file.endswith(\".soft\") or file.endswith(\".soft.gz\"):\n",
" soft_file = os.path.join(in_cohort_dir, file)\n",
" elif file.endswith(\"_family.soft.gz\") or file.endswith(\"_family.soft\"):\n",
" family_file = os.path.join(in_cohort_dir, file)\n",
" elif file.endswith(\"_matrix.txt\") or file.endswith(\"_matrix.txt.gz\"):\n",
" matrix_file = os.path.join(in_cohort_dir, file)\n",
"\n",
"print(f\"SOFT file: {soft_file}\")\n",
"print(f\"Family file: {family_file}\")\n",
"print(f\"Matrix file: {matrix_file}\")\n",
"\n",
"# Load the sample characteristics from the appropriate file\n",
"if soft_file and os.path.exists(soft_file):\n",
" # Parse SOFT file to get sample characteristics\n",
" with open(soft_file, 'r') as f:\n",
" lines = f.readlines()\n",
" \n",
" # Extract sample characteristics\n",
" sample_chars = []\n",
" current_section = None\n",
" for line in lines:\n",
" if line.startswith(\"!Sample_\"):\n",
" key = line.split(\"=\")[0].strip().replace(\"!Sample_\", \"\")\n",
" value = line.split(\"=\")[1].strip() if \"=\" in line else \"\"\n",
" if key == \"table_begin\":\n",
" current_section = \"sample_table\"\n",
" sample_chars = []\n",
" elif key == \"table_end\":\n",
" current_section = None\n",
" elif current_section == \"sample_table\":\n",
" sample_chars.append(line.strip())\n",
" \n",
" # Create DataFrame from sample characteristics\n",
" if sample_chars:\n",
" import io\n",
" sample_table = io.StringIO(\"\\n\".join(sample_chars))\n",
" clinical_data = pd.read_csv(sample_table, sep=\"\\t\")\n",
" else:\n",
" clinical_data = pd.DataFrame()\n",
"else:\n",
" # Try to find sample characteristics in other files\n",
" sample_chars_file = None\n",
" for file in os.listdir(in_cohort_dir) if os.path.exists(in_cohort_dir) else []:\n",
" if \"sample\" in file.lower() and \"char\" in file.lower():\n",
" sample_chars_file = os.path.join(in_cohort_dir, file)\n",
" break\n",
" \n",
" if sample_chars_file and os.path.exists(sample_chars_file):\n",
" clinical_data = pd.read_csv(sample_chars_file)\n",
" else:\n",
" # Last resort - look for any CSV file that might contain clinical data\n",
" csv_files = [f for f in os.listdir(in_cohort_dir) if f.endswith('.csv')] if os.path.exists(in_cohort_dir) else []\n",
" if csv_files:\n",
" clinical_data = pd.read_csv(os.path.join(in_cohort_dir, csv_files[0]))\n",
" else:\n",
" clinical_data = pd.DataFrame()\n",
"\n",
"# Display sample characteristics to make informed decisions\n",
"print(\"\\nSample characteristics data shape:\", clinical_data.shape)\n",
"print(\"Sample characteristics preview:\")\n",
"print(clinical_data.head())\n",
"\n",
"# Get unique values for each row to identify relevant variables\n",
"unique_values = {}\n",
"for i in range(len(clinical_data)):\n",
" if i < clinical_data.shape[0]:\n",
" values = set(clinical_data.iloc[i, 1:].dropna().unique())\n",
" unique_values[i] = values\n",
" print(f\"Row {i}: {values}\")\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Assuming gene expression data is available (can be overridden if evidence suggests otherwise)\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Identify rows for trait, age, and gender\n",
"trait_row = None\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# Check each row to find trait, age, and gender information\n",
"for i in unique_values:\n",
" values_str = ' '.join([str(v) for v in unique_values[i]])\n",
" row_data_str = ' '.join([str(x) for x in clinical_data.iloc[i, :].values if pd.notna(x)])\n",
" \n",
" # Looking for height information\n",
" if any(h in row_data_str.lower() for h in ['height', 'cm', 'meter', 'tall', 'stature']):\n",
" trait_row = i\n",
" print(f\"Found Height information in row {i}\")\n",
" \n",
" # Looking for age information\n",
" if any(a in row_data_str.lower() for a in ['age', 'year', 'yrs', 'yo']):\n",
" age_row = i\n",
" print(f\"Found Age information in row {i}\")\n",
" \n",
" # Looking for gender information\n",
" if any(g in row_data_str.lower() for g in ['gender', 'sex', 'male', 'female']):\n",
" gender_row = i\n",
" print(f\"Found Gender information in row {i}\")\n",
"\n",
"# 2.2 Define conversion functions for each variable\n",
"def extract_value(s):\n",
" \"\"\"Extract value after colon if present.\"\"\"\n",
" if isinstance(s, str) and ':' in s:\n",
" return s.split(':', 1)[1].strip()\n",
" return s\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert height value to float (continuous).\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" value = extract_value(value)\n",
" if isinstance(value, str):\n",
" # Extract numeric part from the string\n",
" try:\n",
" import re\n",
" nums = re.findall(r'\\d+\\.?\\d*', value)\n",
" if nums:\n",
" return float(nums[0])\n",
" except:\n",
" pass\n",
" \n",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to float (continuous).\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" value = extract_value(value)\n",
" if isinstance(value, str):\n",
" # Extract numeric part from the string\n",
" try:\n",
" import re\n",
" nums = re.findall(r'\\d+\\.?\\d*', value)\n",
" if nums:\n",
" return float(nums[0])\n",
" except:\n",
" pass\n",
" \n",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary (0 for female, 1 for male).\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" value = extract_value(value)\n",
" if isinstance(value, str):\n",
" value = value.lower()\n",
" if any(f in value for f in ['female', 'f', 'woman', 'girl']):\n",
" return 0\n",
" elif any(m in value for m in ['male', 'm', 'man', 'boy']):\n",
" return 1\n",
" return None\n",
"\n",
"# 3. Save metadata about data availability\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. Extract and save clinical features if trait data is available\n",
"if trait_row is not None:\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\n"
]
},
{
"cell_type": "markdown",
"id": "14d7a3ed",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb4606d2",
"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": "ee780b33",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2bbe313",
"metadata": {},
"outputs": [],
"source": [
"# Examining the gene identifiers in the dataset\n",
"# The identifiers follow the format \"ENSG00000000003_at\", \"ENSG00000000005_at\", etc.\n",
"# ENSG identifiers are Ensembl gene IDs, not standard human gene symbols\n",
"# They need to be mapped to official gene symbols for better interpretability\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "0e4e7ce4",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b5dd22c7",
"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": "ea803bcd",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "71f22fa6",
"metadata": {},
"outputs": [],
"source": [
"# 1. After analyzing the gene identifiers and gene annotation data:\n",
"# - In gene_data, the IDs are like \"ENSG00000000003_at\" \n",
"# - In gene_annotation, the matching column is \"ID\" which has the same format\n",
"# - The gene symbols are stored in the \"ORF\" column\n",
"\n",
"# 2. Create gene mapping dataframe with the two relevant columns\n",
"gene_mapping = gene_annotation[['ID', 'ORF']]\n",
"gene_mapping = gene_mapping.rename(columns={'ORF': 'Gene'})\n",
"gene_mapping = gene_mapping.dropna()\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"# This handles the many-to-many relationship as specified\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Preview the gene expression data after mapping\n",
"print(\"\\nGene expression data preview after mapping:\")\n",
"print(gene_data.shape)\n",
"print(gene_data.index[:10]) # Show first 10 gene symbols"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|