{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "fe1dbed0", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:26.227138Z", "iopub.status.busy": "2025-03-25T05:13:26.226763Z", "iopub.status.idle": "2025-03-25T05:13:26.402488Z", "shell.execute_reply": "2025-03-25T05:13:26.402021Z" } }, "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 = \"Esophageal_Cancer\"\n", "cohort = \"GSE218109\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Esophageal_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Esophageal_Cancer/GSE218109\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Esophageal_Cancer/GSE218109.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Esophageal_Cancer/gene_data/GSE218109.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Esophageal_Cancer/clinical_data/GSE218109.csv\"\n", "json_path = \"../../output/preprocess/Esophageal_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "6cd68014", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "1ca62361", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:26.404160Z", "iopub.status.busy": "2025-03-25T05:13:26.403994Z", "iopub.status.idle": "2025-03-25T05:13:26.479880Z", "shell.execute_reply": "2025-03-25T05:13:26.479467Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Esophageal Squamous Cell Carcinoma tumors from Indian patients: nuclear-stabilized p53 (NS+) versus unstable p53 (NS-) protein\"\n", "!Series_summary\t\"Transcriptional profiling of Esophageal Squamous Cell Carcinoma (ESCC) tumors comparing samples harbouring nuclear-stabilized p53 (NS+) versus unstable p53 (NS-) protein, determined through immunohistochemistry (IHC) staining of the tumor sections. The goal was to identify the genes that were differentially regulated between NS+ and NS- ESCC samples.\"\n", "!Series_overall_design\t\"Two-condition experiment, NS+ versus NS- esophageal tumors. NS+ tumors: 17, NS- tumors: 19.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['Sex: M', 'Sex: F'], 1: ['age: 22', 'age: 45', 'age: 52', 'age: 50', 'age: 34', 'age: 55', 'age: 48', 'age: 64', 'age: 70', 'age: 68', 'age: 23', 'age: 62', 'age: 59', 'age: 58', 'age: 41', 'age: 47', 'age: 66', 'age: 38', 'age: 79', 'age: 61', 'age: 39', 'age: 32', 'age: 46', 'age: 69', 'age: 54'], 2: ['tissue: Esophageal Squamous Cell Carcinoma'], 3: ['Stage: pT3N2', 'Stage: pT3N0', 'Stage: pT3N1', 'Stage: pT3N1bM1b', 'Stage: pT2PN1a', 'Stage: pT2N0Mx', 'Stage: pT2N2', 'Stage: NA', 'Stage: pT2N0', 'Stage: pT2N1b', 'Stage: pT3N1Mx', 'Stage: pT3N2Mx', 'Stage: pT2N1', 'Stage: pT3N0Mx'], 4: ['grade: I', 'grade: II'], 5: ['p53 status: unstable p53 (NS-)', 'p53 status: nuclear-stabilized p53 (NS+)']}\n" ] } ], "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": "30b89f44", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "44e70764", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:26.481077Z", "iopub.status.busy": "2025-03-25T05:13:26.480955Z", "iopub.status.idle": "2025-03-25T05:13:26.491016Z", "shell.execute_reply": "2025-03-25T05:13:26.490611Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of selected clinical data:\n", "{'Esophageal_Cancer': [0.0, 1.0, nan, nan, nan], 'Age': [22, 45, 52, 50, 34], 'Gender': [1.0, 0.0, nan, nan, nan]}\n", "Clinical data saved to ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE218109.csv\n" ] } ], "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 the background information, this dataset contains gene expression data\n", "# comparing esophageal tumor samples with different p53 status\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# For trait: p53 status in row 5\n", "trait_row = 5\n", "# For age: age in row 1\n", "age_row = 1\n", "# For gender: sex in row 0\n", "gender_row = 0\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(val):\n", " \"\"\"Convert p53 status to binary (0 for NS-, 1 for NS+)\"\"\"\n", " if not isinstance(val, str):\n", " return None\n", " \n", " val = val.lower()\n", " if \"p53 status:\" in val:\n", " val = val.split(\"p53 status:\")[1].strip()\n", " \n", " if \"nuclear-stabilized\" in val or \"ns+\" in val:\n", " return 1\n", " elif \"unstable\" in val or \"ns-\" in val:\n", " return 0\n", " return None\n", "\n", "def convert_age(val):\n", " \"\"\"Convert age to numeric value\"\"\"\n", " if not isinstance(val, str):\n", " return None\n", " \n", " if \"age:\" in val:\n", " try:\n", " age = int(val.split(\"age:\")[1].strip())\n", " return age\n", " except:\n", " pass\n", " return None\n", "\n", "def convert_gender(val):\n", " \"\"\"Convert gender to binary (0 for female, 1 for male)\"\"\"\n", " if not isinstance(val, str):\n", " return None\n", " \n", " val = val.lower()\n", " if \"sex:\" in val:\n", " val = val.split(\"sex:\")[1].strip()\n", " \n", " if val == 'f' or val == 'female':\n", " return 0\n", " elif val == 'm' or val == 'male':\n", " return 1\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Trait data availability is determined by whether trait_row is None\n", "is_trait_available = trait_row is not None\n", "\n", "# Use the validate_and_save_cohort_info function for 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", "def get_feature_data(clinical_df, row_idx, feature_name, convert_func):\n", " \"\"\"Helper function to extract and process feature data\"\"\"\n", " feature_values = clinical_df.iloc[row_idx].tolist()\n", " converted_values = [convert_func(val) for val in feature_values]\n", " return pd.DataFrame({feature_name: converted_values})\n", "\n", "# 4. Clinical Feature Extraction\n", "if trait_row is not None:\n", " # Create a sample DataFrame based on the characteristics data where:\n", " # - Each column is a sample\n", " # - Each row represents a different characteristic (indexed by the keys in sample_chars_dict)\n", " sample_chars_dict = {0: ['Sex: M', 'Sex: F'], \n", " 1: ['age: 22', 'age: 45', 'age: 52', 'age: 50', 'age: 34', 'age: 55', 'age: 48', \n", " 'age: 64', 'age: 70', 'age: 68', 'age: 23', 'age: 62', 'age: 59', 'age: 58', \n", " 'age: 41', 'age: 47', 'age: 66', 'age: 38', 'age: 79', 'age: 61', 'age: 39', \n", " 'age: 32', 'age: 46', 'age: 69', 'age: 54'], \n", " 2: ['tissue: Esophageal Squamous Cell Carcinoma'], \n", " 3: ['Stage: pT3N2', 'Stage: pT3N0', 'Stage: pT3N1', 'Stage: pT3N1bM1b', 'Stage: pT2PN1a', \n", " 'Stage: pT2N0Mx', 'Stage: pT2N2', 'Stage: NA', 'Stage: pT2N0', 'Stage: pT2N1b', \n", " 'Stage: pT3N1Mx', 'Stage: pT3N2Mx', 'Stage: pT2N1', 'Stage: pT3N0Mx'], \n", " 4: ['grade: I', 'grade: II'], \n", " 5: ['p53 status: unstable p53 (NS-)', 'p53 status: nuclear-stabilized p53 (NS+)']}\n", " \n", " # Extract individual features directly\n", " feature_list = []\n", " \n", " # Extract trait data\n", " trait_values = sample_chars_dict[trait_row]\n", " trait_converted = [convert_trait(val) for val in trait_values]\n", " trait_df = pd.DataFrame({trait: trait_converted})\n", " feature_list.append(trait_df)\n", " \n", " # Extract age data if available\n", " if age_row is not None:\n", " age_values = sample_chars_dict[age_row]\n", " age_converted = [convert_age(val) for val in age_values]\n", " age_df = pd.DataFrame({'Age': age_converted})\n", " feature_list.append(age_df)\n", " \n", " # Extract gender data if available\n", " if gender_row is not None:\n", " gender_values = sample_chars_dict[gender_row]\n", " gender_converted = [convert_gender(val) for val in gender_values]\n", " gender_df = pd.DataFrame({'Gender': gender_converted})\n", " feature_list.append(gender_df)\n", " \n", " # Combine all features\n", " # Note: This will align data by index, effectively creating a proper clinical DataFrame\n", " selected_clinical_df = pd.concat(feature_list, axis=1)\n", " \n", " # Preview the selected clinical data\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of selected clinical data:\")\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 the selected clinical data to a CSV file\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": "f3c1ab5f", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "80032663", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:26.492358Z", "iopub.status.busy": "2025-03-25T05:13:26.492182Z", "iopub.status.idle": "2025-03-25T05:13:26.593087Z", "shell.execute_reply": "2025-03-25T05:13:26.592597Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 67\n", "Header line: \"ID_REF\"\t\"GSM6734720\"\t\"GSM6734721\"\t\"GSM6734722\"\t\"GSM6734723\"\t\"GSM6734724\"\t\"GSM6734725\"\t\"GSM6734726\"\t\"GSM6734727\"\t\"GSM6734728\"\t\"GSM6734729\"\t\"GSM6734730\"\t\"GSM6734731\"\t\"GSM6734732\"\t\"GSM6734733\"\t\"GSM6734734\"\t\"GSM6734735\"\t\"GSM6734736\"\t\"GSM6734737\"\t\"GSM6734738\"\t\"GSM6734739\"\t\"GSM6734740\"\t\"GSM6734741\"\t\"GSM6734742\"\t\"GSM6734743\"\t\"GSM6734744\"\t\"GSM6734745\"\t\"GSM6734746\"\t\"GSM6734747\"\t\"GSM6734748\"\t\"GSM6734749\"\t\"GSM6734750\"\t\"GSM6734751\"\t\"GSM6734752\"\t\"GSM6734753\"\t\"GSM6734754\"\t\"GSM6734755\"\n", "First data line: 12\t9.15E+02\t1.50E+03\t2.05E+03\t1.77E+03\t1.19E+03\t2.75E+03\t6.58E+02\t1.53E+03\t7.63E+02\t2.48E+03\t1.23E+03\t1.33E+03\t1.11E+03\t1.14E+04\t2.24E+03\t5.97E+03\t2.53E+03\t1.43E+03\t4.77E+02\t3.44E+03\t5.13E+03\t2.82E+03\t4.34E+03\t9.98E+02\t1.09E+03\t6.81E+03\t9.47E+02\t2.08E+03\t1.45E+03\t4.91E+03\t2.11E+03\t1.40E+01\t1.14E+03\t3.27E+03\t2.21E+03\t3.34E+03\n", "Index(['12', '14', '15', '16', '17', '18', '19', '20', '22', '23', '24', '25',\n", " '26', '27', '30', '33', '35', '36', '37', '38'],\n", " dtype='object', name='ID')\n" ] } ], "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": "dea7927a", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "2f55618c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:26.594394Z", "iopub.status.busy": "2025-03-25T05:13:26.594242Z", "iopub.status.idle": "2025-03-25T05:13:26.596338Z", "shell.execute_reply": "2025-03-25T05:13:26.595992Z" } }, "outputs": [], "source": [ "# Examining the gene identifiers in the expression data\n", "# The identifiers appear to be numeric values (12, 14, 15, 16...) which are not standard human gene symbols\n", "# These are likely to be probe IDs or some other platform-specific identifiers \n", "# that need to be mapped to human gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "2bd85e0e", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "788a5b37", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:26.597458Z", "iopub.status.busy": "2025-03-25T05:13:26.597347Z", "iopub.status.idle": "2025-03-25T05:13:27.013922Z", "shell.execute_reply": "2025-03-25T05:13:27.013392Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Examining SOFT file structure:\n", "Line 0: ^DATABASE = GeoMiame\n", "Line 1: !Database_name = Gene Expression Omnibus (GEO)\n", "Line 2: !Database_institute = NCBI NLM NIH\n", "Line 3: !Database_web_link = http://www.ncbi.nlm.nih.gov/geo\n", "Line 4: !Database_email = geo@ncbi.nlm.nih.gov\n", "Line 5: ^SERIES = GSE218109\n", "Line 6: !Series_title = Esophageal Squamous Cell Carcinoma tumors from Indian patients: nuclear-stabilized p53 (NS+) versus unstable p53 (NS-) protein\n", "Line 7: !Series_geo_accession = GSE218109\n", "Line 8: !Series_status = Public on Mar 29 2024\n", "Line 9: !Series_submission_date = Nov 16 2022\n", "Line 10: !Series_last_update_date = Mar 30 2024\n", "Line 11: !Series_pubmed_id = 38358025\n", "Line 12: !Series_summary = Transcriptional profiling of Esophageal Squamous Cell Carcinoma (ESCC) tumors comparing samples harbouring nuclear-stabilized p53 (NS+) versus unstable p53 (NS-) protein, determined through immunohistochemistry (IHC) staining of the tumor sections. The goal was to identify the genes that were differentially regulated between NS+ and NS- ESCC samples.\n", "Line 13: !Series_overall_design = Two-condition experiment, NS+ versus NS- esophageal tumors. NS+ tumors: 17, NS- tumors: 19.\n", "Line 14: !Series_type = Expression profiling by array\n", "Line 15: !Series_contributor = Sara,A,George\n", "Line 16: !Series_contributor = Murali,D,Bashyam\n", "Line 17: !Series_sample_id = GSM6734720\n", "Line 18: !Series_sample_id = GSM6734721\n", "Line 19: !Series_sample_id = GSM6734722\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': [1, 2, 3, 4, 5], 'COL': [266, 266, 266, 266, 266], 'ROW': [170, 168, 166, 164, 162], 'NAME': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'DarkCorner', 'DarkCorner'], 'SPOT_ID': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'DarkCorner', 'DarkCorner'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'pos', 'pos'], 'REFSEQ': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan], 'GENE': [nan, nan, nan, nan, nan], 'GENE_SYMBOL': [nan, nan, nan, nan, nan], 'GENE_NAME': [nan, nan, nan, nan, nan], 'UNIGENE_ID': [nan, nan, nan, nan, nan], 'ENSEMBL_ID': [nan, nan, nan, nan, nan], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': [nan, nan, nan, nan, nan], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, nan, nan], 'CYTOBAND': [nan, nan, nan, nan, nan], 'DESCRIPTION': [nan, nan, nan, nan, nan], 'GO_ID': [nan, nan, nan, nan, nan], 'SEQUENCE': [nan, nan, nan, nan, nan], 'SPOT_ID.1': [nan, nan, nan, nan, nan], 'ORDER': [1, 2, 3, 4, 5]}\n" ] } ], "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": "2822eba9", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "20a742a4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:27.015323Z", "iopub.status.busy": "2025-03-25T05:13:27.015201Z", "iopub.status.idle": "2025-03-25T05:13:27.345973Z", "shell.execute_reply": "2025-03-25T05:13:27.345329Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns in gene_annotation: ['ID', 'COL', 'ROW', 'NAME', 'SPOT_ID', 'CONTROL_TYPE', 'REFSEQ', 'GB_ACC', 'GENE', 'GENE_SYMBOL', 'GENE_NAME', 'UNIGENE_ID', 'ENSEMBL_ID', 'TIGR_ID', 'ACCESSION_STRING', 'CHROMOSOMAL_LOCATION', 'CYTOBAND', 'DESCRIPTION', 'GO_ID', 'SEQUENCE', 'SPOT_ID.1', 'ORDER']\n", "\n", "Gene mapping preview (first 5 rows):\n", " ID Gene\n", "11 12 APOBEC3B\n", "13 14 ATP11B\n", "14 15 LOC100132006\n", "15 16 DNAJA1\n", "17 18 EHMT2\n", "\n", "Total probes in annotation: 45220\n", "Probes with gene symbols: 32696\n", "\n", "Gene expression data preview (first 5 genes):\n", " GSM6734720 GSM6734721 GSM6734722 GSM6734723 GSM6734724 GSM6734725 \\\n", "Gene \n", "A1BG 5040.0 1800.0 3190.0 3580.0 873.0 5500.0 \n", "A1CF 31.0 137.0 25.3 30.3 42.9 12.7 \n", "A2LD1 1250.0 785.0 791.0 625.0 829.0 446.0 \n", "A2M 126000.0 238000.0 93400.0 19100.0 8470.0 107000.0 \n", "A2ML1 1390.0 3640.0 611.0 1050.0 1790.0 148.0 \n", "\n", " GSM6734726 GSM6734727 GSM6734728 GSM6734729 ... GSM6734746 \\\n", "Gene ... \n", "A1BG 3420.0 1020.0 667.0 2390.0 ... 3530.0 \n", "A1CF 15.7 69.9 61.4 32.8 ... 32.6 \n", "A2LD1 1080.0 296.0 2570.0 275.0 ... 736.0 \n", "A2M 122000.0 56500.0 46400.0 23200.0 ... 46400.0 \n", "A2ML1 267.0 3350.0 1070.0 3430.0 ... 1710.0 \n", "\n", " GSM6734747 GSM6734748 GSM6734749 GSM6734750 GSM6734751 GSM6734752 \\\n", "Gene \n", "A1BG 1010.0 1250.0 1650.0 442.0 2470.0 3030.0 \n", "A1CF 57.7 22.8 42.2 15.8 28.2 48.7 \n", "A2LD1 1280.0 1820.0 1130.0 565.0 275.0 402.0 \n", "A2M 22300.0 24400.0 44400.0 23100.0 12500.0 19100.0 \n", "A2ML1 4500.0 3310.0 2760.0 2070.0 1240.0 546.0 \n", "\n", " GSM6734753 GSM6734754 GSM6734755 \n", "Gene \n", "A1BG 1990.0 1750.0 1390.0 \n", "A1CF 45.4 30.5 36.7 \n", "A2LD1 521.0 181.0 410.0 \n", "A2M 43800.0 26200.0 149000.0 \n", "A2ML1 403.0 1430.0 4350.0 \n", "\n", "[5 rows x 36 columns]\n", "Total number of genes in expression data: 15298\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data saved to ../../output/preprocess/Esophageal_Cancer/gene_data/GSE218109.csv\n" ] } ], "source": [ "# Identify which columns in gene_annotation contain the probe IDs and gene symbols\n", "# From the preview, 'ID' column appears to contain numeric identifiers matching those in gene_data\n", "# 'GENE_SYMBOL' appears to be the column containing gene symbols\n", "\n", "# 1. Get the mapping between probe IDs and gene symbols\n", "probe_col = \"ID\"\n", "gene_col = \"GENE_SYMBOL\"\n", "\n", "# First, let's check if these columns exist in our annotation data\n", "print(f\"Columns in gene_annotation: {gene_annotation.columns.tolist()}\")\n", "\n", "# 2. Get the gene mapping dataframe by extracting the two relevant columns\n", "mapping_data = gene_annotation[[probe_col, gene_col]].copy()\n", "mapping_data = mapping_data.dropna(subset=[gene_col]) # Drop rows with no gene symbol\n", "mapping_data = mapping_data.astype({probe_col: 'str'}) # Convert ID to string to match gene_data index\n", "\n", "# Rename the gene symbol column to 'Gene' to match the expected column name in apply_gene_mapping\n", "mapping_data = mapping_data.rename(columns={gene_col: 'Gene'})\n", "\n", "# Preview the mapping\n", "print(\"\\nGene mapping preview (first 5 rows):\")\n", "print(mapping_data.head())\n", "\n", "# Check how many probes have gene symbols\n", "print(f\"\\nTotal probes in annotation: {len(gene_annotation)}\")\n", "print(f\"Probes with gene symbols: {len(mapping_data)}\")\n", "\n", "# 3. Apply the gene mapping to convert probe-level data to gene-level data\n", "# Using the library function to handle the many-to-many mapping\n", "gene_data = apply_gene_mapping(gene_data, mapping_data)\n", "\n", "# Check the resulting gene expression data\n", "print(\"\\nGene expression data preview (first 5 genes):\")\n", "print(gene_data.head())\n", "print(f\"Total number of genes in expression data: {len(gene_data)}\")\n", "\n", "# Save the gene expression data\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "a11a65b2", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "eaae3870", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:27.347952Z", "iopub.status.busy": "2025-03-25T05:13:27.347820Z", "iopub.status.idle": "2025-03-25T05:13:33.211093Z", "shell.execute_reply": "2025-03-25T05:13:33.210424Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (14998, 36)\n", "First few genes with their expression values after normalization:\n", " GSM6734720 GSM6734721 GSM6734722 GSM6734723 GSM6734724 \\\n", "Gene \n", "A1BG 5040.0 1800.0 3190.0 3580.0 873.0 \n", "A1CF 31.0 137.0 25.3 30.3 42.9 \n", "A2M 126000.0 238000.0 93400.0 19100.0 8470.0 \n", "A2ML1 1390.0 3640.0 611.0 1050.0 1790.0 \n", "A4GALT 576.0 1140.0 732.0 924.0 76.3 \n", "\n", " GSM6734725 GSM6734726 GSM6734727 GSM6734728 GSM6734729 ... \\\n", "Gene ... \n", "A1BG 5500.0 3420.0 1020.0 667.0 2390.0 ... \n", "A1CF 12.7 15.7 69.9 61.4 32.8 ... \n", "A2M 107000.0 122000.0 56500.0 46400.0 23200.0 ... \n", "A2ML1 148.0 267.0 3350.0 1070.0 3430.0 ... \n", "A4GALT 1400.0 471.0 2760.0 190.0 2640.0 ... \n", "\n", " GSM6734746 GSM6734747 GSM6734748 GSM6734749 GSM6734750 \\\n", "Gene \n", "A1BG 3530.0 1010.0 1250.0 1650.0 442.0 \n", "A1CF 32.6 57.7 22.8 42.2 15.8 \n", "A2M 46400.0 22300.0 24400.0 44400.0 23100.0 \n", "A2ML1 1710.0 4500.0 3310.0 2760.0 2070.0 \n", "A4GALT 378.0 1150.0 1050.0 826.0 324.0 \n", "\n", " GSM6734751 GSM6734752 GSM6734753 GSM6734754 GSM6734755 \n", "Gene \n", "A1BG 2470.0 3030.0 1990.0 1750.0 1390.0 \n", "A1CF 28.2 48.7 45.4 30.5 36.7 \n", "A2M 12500.0 19100.0 43800.0 26200.0 149000.0 \n", "A2ML1 1240.0 546.0 403.0 1430.0 4350.0 \n", "A4GALT 1220.0 1060.0 414.0 412.0 756.0 \n", "\n", "[5 rows x 36 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Esophageal_Cancer/gene_data/GSE218109.csv\n", "Raw clinical data shape: (6, 37)\n", "Clinical features:\n", " GSM6734720 GSM6734721 GSM6734722 GSM6734723 GSM6734724 \\\n", "Esophageal_Cancer 0.0 0.0 0.0 0.0 0.0 \n", "Age 22.0 45.0 52.0 50.0 34.0 \n", "Gender 1.0 1.0 0.0 0.0 0.0 \n", "\n", " GSM6734725 GSM6734726 GSM6734727 GSM6734728 GSM6734729 \\\n", "Esophageal_Cancer 0.0 0.0 0.0 0.0 1.0 \n", "Age 55.0 48.0 64.0 70.0 68.0 \n", "Gender 1.0 0.0 1.0 1.0 0.0 \n", "\n", " ... GSM6734746 GSM6734747 GSM6734748 GSM6734749 \\\n", "Esophageal_Cancer ... 1.0 1.0 0.0 0.0 \n", "Age ... 59.0 39.0 32.0 55.0 \n", "Gender ... 1.0 0.0 0.0 0.0 \n", "\n", " GSM6734750 GSM6734751 GSM6734752 GSM6734753 GSM6734754 \\\n", "Esophageal_Cancer 1.0 1.0 1.0 1.0 1.0 \n", "Age 46.0 69.0 61.0 54.0 38.0 \n", "Gender 1.0 1.0 1.0 1.0 1.0 \n", "\n", " GSM6734755 \n", "Esophageal_Cancer 0.0 \n", "Age 64.0 \n", "Gender 1.0 \n", "\n", "[3 rows x 36 columns]\n", "Clinical features saved to ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE218109.csv\n", "Linked data shape: (36, 15001)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Esophageal_Cancer Age Gender A1BG A1CF\n", "GSM6734720 0.0 22.0 1.0 5040.0 31.0\n", "GSM6734721 0.0 45.0 1.0 1800.0 137.0\n", "GSM6734722 0.0 52.0 0.0 3190.0 25.3\n", "GSM6734723 0.0 50.0 0.0 3580.0 30.3\n", "GSM6734724 0.0 34.0 0.0 873.0 42.9\n", "Missing values before handling:\n", " Trait (Esophageal_Cancer) missing: 0 out of 36\n", " Age missing: 0 out of 36\n", " Gender missing: 0 out of 36\n", " Genes with >20% missing: 0\n", " Samples with >5% missing genes: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (36, 15001)\n", "For the feature 'Esophageal_Cancer', the least common label is '1.0' with 17 occurrences. This represents 47.22% of the dataset.\n", "The distribution of the feature 'Esophageal_Cancer' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: 44.0\n", " 50% (Median): 53.0\n", " 75%: 62.0\n", "Min: 22.0\n", "Max: 79.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '0.0' with 14 occurrences. This represents 38.89% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Esophageal_Cancer/GSE218109.csv\n" ] } ], "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(\"First few genes with their expression values after normalization:\")\n", "print(normalized_gene_data.head())\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. Check if trait data is available before proceeding with clinical data extraction\n", "if trait_row is None:\n", " print(\"Trait row is None. Cannot extract trait information from clinical data.\")\n", " # Create an empty dataframe for clinical features\n", " clinical_features = pd.DataFrame()\n", " \n", " # Create an empty dataframe for linked data\n", " linked_data = pd.DataFrame()\n", " \n", " # Validate and save cohort info\n", " 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, # Trait data is not available\n", " is_biased=True, # Not applicable but required\n", " df=pd.DataFrame(), # Empty dataframe\n", " note=\"Dataset contains gene expression data but lacks clear trait indicators for Duchenne Muscular Dystrophy status.\"\n", " )\n", " print(\"Data was determined to be unusable due to missing trait indicators and was not saved\")\n", "else:\n", " try:\n", " # Get the file paths for the matrix file to extract clinical data\n", " _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", " \n", " # Get raw clinical data from the matrix file\n", " _, clinical_raw = get_background_and_clinical_data(matrix_file)\n", " \n", " # Verify clinical data structure\n", " print(\"Raw clinical data shape:\", clinical_raw.shape)\n", " \n", " # Extract clinical features using the defined conversion functions\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_raw,\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 features:\")\n", " print(clinical_features)\n", " \n", " # Save clinical features to file\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 features saved to {out_clinical_data_file}\")\n", " \n", " # 3. Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " print(\"Linked data preview (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # 4. Handle missing values\n", " print(\"Missing values before handling:\")\n", " print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n", " if 'Age' in linked_data.columns:\n", " print(f\" Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n", " if 'Gender' in linked_data.columns:\n", " print(f\" Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n", " \n", " gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n", " print(f\" Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n", " print(f\" Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n", " \n", " cleaned_data = handle_missing_values(linked_data, trait)\n", " print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n", " \n", " # 5. Evaluate bias in trait and demographic features\n", " is_trait_biased = False\n", " if len(cleaned_data) > 0:\n", " trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n", " is_trait_biased = trait_biased\n", " else:\n", " print(\"No data remains after handling missing values.\")\n", " is_trait_biased = True\n", " \n", " # 6. Final validation and save\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=cleaned_data,\n", " note=\"Dataset contains gene expression data comparing Duchenne muscular dystrophy vs healthy samples.\"\n", " )\n", " \n", " # 7. Save if usable\n", " if is_usable and len(cleaned_data) > 0:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " cleaned_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(\"Data was determined to be unusable or empty and was not saved\")\n", " \n", " except Exception as e:\n", " print(f\"Error processing data: {e}\")\n", " # Handle the error case by still recording cohort info\n", " 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, # Mark as not available due to processing issues\n", " is_biased=True, \n", " df=pd.DataFrame(), # Empty dataframe\n", " note=f\"Error processing data: {str(e)}\"\n", " )\n", " print(\"Data was determined to be unusable and was not saved\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }