{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "fd56f70d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:50.750133Z", "iopub.status.busy": "2025-03-25T05:13:50.750032Z", "iopub.status.idle": "2025-03-25T05:13:50.907429Z", "shell.execute_reply": "2025-03-25T05:13:50.907082Z" } }, "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 = \"GSE75241\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Esophageal_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Esophageal_Cancer/GSE75241\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Esophageal_Cancer/GSE75241.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Esophageal_Cancer/gene_data/GSE75241.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Esophageal_Cancer/clinical_data/GSE75241.csv\"\n", "json_path = \"../../output/preprocess/Esophageal_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "031574a0", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "f636a22a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:50.908821Z", "iopub.status.busy": "2025-03-25T05:13:50.908684Z", "iopub.status.idle": "2025-03-25T05:13:50.969210Z", "shell.execute_reply": "2025-03-25T05:13:50.968902Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene expression profile of esophageal squamous cell carcinoma\"\n", "!Series_summary\t\"The goal was to identify the differently expressed genes between esophageal tumor and nonmalignant surrounding mucosa\"\n", "!Series_overall_design\t\"15 paired ESCC samples and matched nonmalignant mucosa were analyzed\"\n", "Sample Characteristics Dictionary:\n", "{0: ['patient: 1', 'patient: 2', 'patient: 3', 'patient: 4', 'patient: 5', 'patient: 6', 'patient: 7', 'patient: 8', 'patient: 9', 'patient: 10', 'patient: 11', 'patient: 12', 'patient: 14', 'patient: 15', 'patient: 16'], 1: ['tissue: nonmalignant surrounding mucosa', 'tissue: esophageal tumor'], 2: [nan, 'tumor differentiation: poor', 'tumor differentiation: moderate', 'tumor differentiation: well']}\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": "445577fe", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "c7dfb039", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:50.970416Z", "iopub.status.busy": "2025-03-25T05:13:50.970311Z", "iopub.status.idle": "2025-03-25T05:13:50.977217Z", "shell.execute_reply": "2025-03-25T05:13:50.976934Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Data Preview:\n", "{'GSM1946756': [0.0], 'GSM1946757': [1.0], 'GSM1946758': [0.0], 'GSM1946759': [1.0], 'GSM1946760': [0.0], 'GSM1946761': [1.0], 'GSM1946762': [0.0], 'GSM1946763': [1.0], 'GSM1946764': [0.0], 'GSM1946765': [1.0], 'GSM1946766': [0.0], 'GSM1946767': [1.0], 'GSM1946768': [0.0], 'GSM1946769': [1.0], 'GSM1946770': [0.0], 'GSM1946771': [1.0], 'GSM1946772': [0.0], 'GSM1946773': [1.0], 'GSM1946774': [0.0], 'GSM1946775': [1.0], 'GSM1946776': [0.0], 'GSM1946777': [1.0], 'GSM1946778': [0.0], 'GSM1946779': [1.0], 'GSM1946780': [0.0], 'GSM1946781': [1.0], 'GSM1946782': [0.0], 'GSM1946783': [1.0], 'GSM1946784': [0.0], 'GSM1946785': [1.0]}\n", "Clinical data saved to ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE75241.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains gene expression data\n", "# comparing esophageal squamous cell carcinoma tumor samples with non-malignant tissue\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Identify the rows in the sample characteristics dictionary for each variable\n", "\n", "# For trait - tissue type (tumor vs normal)\n", "trait_row = 1 # The key 1 has tumor vs non-malignant surrounding mucosa\n", "\n", "# For age - not available in the provided data\n", "age_row = None # Age is not mentioned in the sample characteristics\n", "\n", "# For gender - not available in the provided data\n", "gender_row = None # Gender is not mentioned in the sample characteristics\n", "\n", "# 2.2 Data Type Conversion Functions\n", "\n", "def convert_trait(value):\n", " \"\"\"\n", " Convert tissue type to binary:\n", " 0 - nonmalignant surrounding mucosa (control)\n", " 1 - esophageal tumor (case)\n", " \"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " if \"tumor\" in value.lower():\n", " return 1\n", " elif \"nonmalignant\" in value.lower() or \"surrounding mucosa\" in value.lower():\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Placeholder function for age conversion (not used)\"\"\"\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Placeholder function for gender conversion (not used)\"\"\"\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Save initial validation information\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", " # Extract clinical features\n", " 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 clinical dataframe\n", " print(\"Clinical Data Preview:\")\n", " print(preview_df(clinical_df))\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 to CSV\n", " clinical_df.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "02797370", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "2af0a122", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:50.978352Z", "iopub.status.busy": "2025-03-25T05:13:50.978252Z", "iopub.status.idle": "2025-03-25T05:13:51.041615Z", "shell.execute_reply": "2025-03-25T05:13:51.041182Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 64\n", "Header line: \"ID_REF\"\t\"GSM1946756\"\t\"GSM1946757\"\t\"GSM1946758\"\t\"GSM1946759\"\t\"GSM1946760\"\t\"GSM1946761\"\t\"GSM1946762\"\t\"GSM1946763\"\t\"GSM1946764\"\t\"GSM1946765\"\t\"GSM1946766\"\t\"GSM1946767\"\t\"GSM1946768\"\t\"GSM1946769\"\t\"GSM1946770\"\t\"GSM1946771\"\t\"GSM1946772\"\t\"GSM1946773\"\t\"GSM1946774\"\t\"GSM1946775\"\t\"GSM1946776\"\t\"GSM1946777\"\t\"GSM1946778\"\t\"GSM1946779\"\t\"GSM1946780\"\t\"GSM1946781\"\t\"GSM1946782\"\t\"GSM1946783\"\t\"GSM1946784\"\t\"GSM1946785\"\n", "First data line: 2315554\t8.17671\t8.3064\t8.2427\t8.39671\t8.51383\t8.12902\t8.30535\t8.38525\t7.97932\t8.13759\t8.328\t8.35267\t8.23582\t8.12066\t8.45462\t7.89502\t7.98993\t8.2095\t8.26696\t7.91252\t8.22498\t8.40417\t8.08198\t8.26314\t8.35753\t8.09386\t8.06862\t7.72916\t8.21028\t8.10057\n", "Index(['2315554', '2315633', '2315674', '2315739', '2315894', '2315918',\n", " '2315951', '2316218', '2316245', '2316379', '2316558', '2316605',\n", " '2316746', '2316905', '2316953', '2317246', '2317317', '2317434',\n", " '2317472', '2317512'],\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": "f47d0721", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "43d55899", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:51.042933Z", "iopub.status.busy": "2025-03-25T05:13:51.042827Z", "iopub.status.idle": "2025-03-25T05:13:51.044616Z", "shell.execute_reply": "2025-03-25T05:13:51.044349Z" } }, "outputs": [], "source": [ "# Looking at the identifiers in the gene expression data (2315554, 2315633, etc.)\n", "# These appear to be numerical IDs rather than human gene symbols\n", "# They are likely probe IDs from a microarray platform and need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "d97a8f65", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "b4aea801", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:51.045841Z", "iopub.status.busy": "2025-03-25T05:13:51.045744Z", "iopub.status.idle": "2025-03-25T05:13:52.397509Z", "shell.execute_reply": "2025-03-25T05:13:52.397149Z" } }, "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 = GSE75241\n", "Line 6: !Series_title = Gene expression profile of esophageal squamous cell carcinoma\n", "Line 7: !Series_geo_accession = GSE75241\n", "Line 8: !Series_status = Public on Jun 26 2019\n", "Line 9: !Series_submission_date = Nov 20 2015\n", "Line 10: !Series_last_update_date = Jan 13 2020\n", "Line 11: !Series_pubmed_id = 29682174\n", "Line 12: !Series_pubmed_id = 31901859\n", "Line 13: !Series_summary = The goal was to identify the differently expressed genes between esophageal tumor and nonmalignant surrounding mucosa\n", "Line 14: !Series_overall_design = 15 paired ESCC samples and matched nonmalignant mucosa were analyzed\n", "Line 15: !Series_type = Expression profiling by array\n", "Line 16: !Series_contributor = Pedro,P,Nicolau-Neto\n", "Line 17: !Series_contributor = Paulo,P,Santos\n", "Line 18: !Series_sample_id = GSM1946756\n", "Line 19: !Series_sample_id = GSM1946757\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': [2315100, 2315106, 2315109, 2315111, 2315113], 'GB_LIST': ['NR_024005,NR_034090,NR_024004,AK093685', 'DQ786314', nan, nan, 'DQ786265'], 'SPOT_ID': ['chr1:11884-14409', 'chr1:14760-15198', 'chr1:19408-19712', 'chr1:25142-25532', 'chr1:27563-27813'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10'], 'RANGE_STRAND': ['+', '+', '+', '+', '+'], 'RANGE_START': ['11884', '14760', '19408', '25142', '27563'], 'RANGE_STOP': ['14409', '15198', '19712', '25532', '27813'], 'total_probes': ['20', '8', '4', '4', '4'], 'gene_assignment': ['NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771 /// NR_034090 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 9 // 15q26.3 // 100288486 /// NR_024004 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771 /// AK093685 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771', '---', '---', '---', '---'], 'mrna_assignment': ['NR_024005 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 (DDX11L2), transcript variant 2, non-coding RNA. // chr1 // 100 // 80 // 16 // 16 // 0 /// NR_034090 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 9 (DDX11L9), non-coding RNA. // chr1 // 100 // 80 // 16 // 16 // 0 /// NR_024004 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 (DDX11L2), transcript variant 1, non-coding RNA. // chr1 // 100 // 75 // 15 // 15 // 0 /// AK093685 // GenBank // Homo sapiens cDNA FLJ36366 fis, clone THYMU2007824. // chr1 // 94 // 80 // 15 // 16 // 0 /// ENST00000513886 // ENSEMBL // cdna:known chromosome:GRCh37:16:61555:64090:1 gene:ENSG00000233614 // chr1 // 100 // 80 // 16 // 16 // 0 /// ENST00000456328 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000223972 // chr1 // 100 // 80 // 16 // 16 // 0 /// ENST00000518655 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000253101 // chr1 // 100 // 80 // 16 // 16 // 0', 'DQ786314 // GenBank // Homo sapiens clone HLS_IMAGE_811138 mRNA sequence. // chr1 // 100 // 38 // 3 // 3 // 0', '---', '---', 'DQ786265 // GenBank // Homo sapiens clone HLS_IMAGE_298685 mRNA sequence. // chr1 // 100 // 100 // 4 // 4 // 0'], 'category': ['main', 'main', '---', '---', 'main']}\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": "dc9313e4", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "4cf703d9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:52.398861Z", "iopub.status.busy": "2025-03-25T05:13:52.398734Z", "iopub.status.idle": "2025-03-25T05:13:55.552990Z", "shell.execute_reply": "2025-03-25T05:13:55.552515Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation columns: ['ID', 'GB_LIST', 'SPOT_ID', 'seqname', 'RANGE_GB', 'RANGE_STRAND', 'RANGE_START', 'RANGE_STOP', 'total_probes', 'gene_assignment', 'mrna_assignment', 'category']\n", "Mapping dataframe shape: (316481, 2)\n", "First few rows of mapping:\n", " ID Gene\n", "0 2315100 NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-As...\n", "1 2315106 ---\n", "2 2315109 ---\n", "3 2315111 ---\n", "4 2315113 ---\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape after mapping: (48895, 30)\n", "First few rows of gene expression data:\n", " GSM1946756 GSM1946757 GSM1946758 GSM1946759 GSM1946760 GSM1946761 \\\n", "Gene \n", "A- 19.686233 21.078992 19.644611 19.255770 19.082776 19.978018 \n", "A-2 3.041177 3.179220 3.144313 3.210930 3.121263 3.458567 \n", "A-52 4.624367 4.627967 4.647400 4.688933 4.598800 4.648167 \n", "A-E 1.734940 1.726010 1.729277 1.715336 1.663119 1.794793 \n", "A-I 6.196227 6.294127 6.293147 6.598947 6.161653 6.351923 \n", "\n", " GSM1946762 GSM1946763 GSM1946764 GSM1946765 ... GSM1946776 \\\n", "Gene ... \n", "A- 19.205867 19.183984 19.728438 19.648668 ... 19.475774 \n", "A-2 3.038037 3.072120 3.068183 3.080643 ... 3.016570 \n", "A-52 4.695467 4.721133 4.626467 4.796533 ... 4.695800 \n", "A-E 1.751473 1.746305 1.639704 1.660835 ... 1.796903 \n", "A-I 6.257723 6.555667 6.270857 6.587713 ... 6.281370 \n", "\n", " GSM1946777 GSM1946778 GSM1946779 GSM1946780 GSM1946781 GSM1946782 \\\n", "Gene \n", "A- 19.881772 19.397177 20.176165 19.485679 19.773278 20.317010 \n", "A-2 3.227047 3.088177 3.113230 3.013857 3.107053 3.052563 \n", "A-52 4.711133 4.706633 4.729300 4.625967 4.579533 4.736200 \n", "A-E 1.887610 1.749953 1.701367 1.706467 1.683693 1.708582 \n", "A-I 6.410690 6.281490 6.437733 6.231920 6.498627 6.332860 \n", "\n", " GSM1946783 GSM1946784 GSM1946785 \n", "Gene \n", "A- 20.407381 19.243382 19.567424 \n", "A-2 3.206957 3.061277 3.094650 \n", "A-52 4.700967 4.663533 4.680933 \n", "A-E 1.644940 1.774332 1.727359 \n", "A-I 6.451783 6.306790 6.386287 \n", "\n", "[5 rows x 30 columns]\n", "Gene expression data shape after normalization: (18418, 30)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Esophageal_Cancer/gene_data/GSE75241.csv\n" ] } ], "source": [ "# 1. Identify the columns for gene IDs and gene symbols in the annotation dataframe\n", "# Based on the preview, we need:\n", "# - 'ID' column for probe IDs which matches the gene expression data index\n", "# - 'gene_assignment' column which contains gene symbols\n", "\n", "# First, let's parse the gene annotation properly \n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# Display the columns to confirm we have the right ones\n", "print(\"Gene annotation columns:\", gene_annotation.columns.tolist())\n", "\n", "# 2. Create a mapping dataframe with probe IDs and gene symbols\n", "# Extract the ID column and gene_assignment column\n", "if 'ID' in gene_annotation.columns and 'gene_assignment' in gene_annotation.columns:\n", " mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')\n", " print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n", " print(\"First few rows of mapping:\")\n", " print(mapping_df.head())\n", " \n", " # 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n", " # This handles the many-to-many relationships as described\n", " gene_data = apply_gene_mapping(gene_data, mapping_df)\n", " print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n", " print(\"First few rows of gene expression data:\")\n", " print(gene_data.head())\n", " \n", " # Normalize gene symbols to official symbols and aggregate duplicate genes\n", " gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Gene expression data shape after normalization: {gene_data.shape}\")\n", " \n", " # Save the gene expression 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", "else:\n", " print(\"Error: Required columns not found in gene annotation dataframe\")\n", " print(\"Available columns:\", gene_annotation.columns.tolist())\n" ] }, { "cell_type": "markdown", "id": "d11e3914", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "3577addf", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:55.554512Z", "iopub.status.busy": "2025-03-25T05:13:55.554210Z", "iopub.status.idle": "2025-03-25T05:14:03.639294Z", "shell.execute_reply": "2025-03-25T05:14:03.638706Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (18418, 30)\n", "First few genes with their expression values after normalization:\n", " GSM1946756 GSM1946757 GSM1946758 GSM1946759 GSM1946760 \\\n", "Gene \n", "A1BG 2.011265 1.946160 1.979805 2.054520 1.964573 \n", "A1BG-AS1 2.011265 1.946160 1.979805 2.054520 1.964573 \n", "A1CF 1.566023 1.530593 1.628517 1.542593 1.563090 \n", "A2M 4.416360 4.980305 4.217160 4.178945 4.489790 \n", "A2ML1 6.222100 5.579050 6.362350 5.534400 6.413550 \n", "\n", " GSM1946761 GSM1946762 GSM1946763 GSM1946764 GSM1946765 ... \\\n", "Gene ... \n", "A1BG 1.938865 1.984317 2.031433 1.959395 1.96234 ... \n", "A1BG-AS1 1.938865 1.984317 2.031433 1.959395 1.96234 ... \n", "A1CF 1.496433 1.624193 1.644417 1.559427 1.51756 ... \n", "A2M 5.703700 3.795370 5.072700 4.543965 5.35270 ... \n", "A2ML1 5.610750 6.239700 5.720800 6.172350 5.47575 ... \n", "\n", " GSM1946776 GSM1946777 GSM1946778 GSM1946779 GSM1946780 \\\n", "Gene \n", "A1BG 1.994905 1.950978 2.016523 1.996433 1.952212 \n", "A1BG-AS1 1.994905 1.950978 2.016523 1.996433 1.952212 \n", "A1CF 1.684330 1.558917 1.637427 1.563087 1.576770 \n", "A2M 4.134060 4.864150 4.580130 5.123950 4.257135 \n", "A2ML1 6.057000 4.311355 6.217400 2.849605 6.131650 \n", "\n", " GSM1946781 GSM1946782 GSM1946783 GSM1946784 GSM1946785 \n", "Gene \n", "A1BG 1.94519 1.977313 1.872837 2.027700 1.996077 \n", "A1BG-AS1 1.94519 1.977313 1.872837 2.027700 1.996077 \n", "A1CF 1.59521 1.600170 1.479100 1.570997 1.578763 \n", "A2M 5.41210 4.288080 5.623400 3.803960 4.879335 \n", "A2ML1 5.16890 6.133500 5.276850 6.085500 5.920700 \n", "\n", "[5 rows x 30 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Esophageal_Cancer/gene_data/GSE75241.csv\n", "Raw clinical data shape: (3, 31)\n", "Clinical features:\n", " GSM1946756 GSM1946757 GSM1946758 GSM1946759 GSM1946760 \\\n", "Esophageal_Cancer 0.0 1.0 0.0 1.0 0.0 \n", "\n", " GSM1946761 GSM1946762 GSM1946763 GSM1946764 GSM1946765 \\\n", "Esophageal_Cancer 1.0 0.0 1.0 0.0 1.0 \n", "\n", " ... GSM1946776 GSM1946777 GSM1946778 GSM1946779 \\\n", "Esophageal_Cancer ... 0.0 1.0 0.0 1.0 \n", "\n", " GSM1946780 GSM1946781 GSM1946782 GSM1946783 GSM1946784 \\\n", "Esophageal_Cancer 0.0 1.0 0.0 1.0 0.0 \n", "\n", " GSM1946785 \n", "Esophageal_Cancer 1.0 \n", "\n", "[1 rows x 30 columns]\n", "Clinical features saved to ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE75241.csv\n", "Linked data shape: (30, 18419)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Esophageal_Cancer A1BG A1BG-AS1 A1CF A2M\n", "GSM1946756 0.0 2.011265 2.011265 1.566023 4.416360\n", "GSM1946757 1.0 1.946160 1.946160 1.530593 4.980305\n", "GSM1946758 0.0 1.979805 1.979805 1.628517 4.217160\n", "GSM1946759 1.0 2.054520 2.054520 1.542593 4.178945\n", "GSM1946760 0.0 1.964573 1.964573 1.563090 4.489790\n", "Missing values before handling:\n", " Trait (Esophageal_Cancer) missing: 0 out of 30\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: (30, 18419)\n", "For the feature 'Esophageal_Cancer', the least common label is '0.0' with 15 occurrences. This represents 50.00% of the dataset.\n", "The distribution of the feature 'Esophageal_Cancer' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Esophageal_Cancer/GSE75241.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 }