{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "50c0ee70", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:42:56.489019Z", "iopub.status.busy": "2025-03-25T08:42:56.488835Z", "iopub.status.idle": "2025-03-25T08:42:56.657785Z", "shell.execute_reply": "2025-03-25T08:42:56.657435Z" } }, "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 = \"Endometrioid_Cancer\"\n", "cohort = \"GSE73637\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Endometrioid_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Endometrioid_Cancer/GSE73637\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Endometrioid_Cancer/GSE73637.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Endometrioid_Cancer/gene_data/GSE73637.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Endometrioid_Cancer/clinical_data/GSE73637.csv\"\n", "json_path = \"../../output/preprocess/Endometrioid_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "4932b9c0", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "7a794db3", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:42:56.659297Z", "iopub.status.busy": "2025-03-25T08:42:56.659146Z", "iopub.status.idle": "2025-03-25T08:42:57.082588Z", "shell.execute_reply": "2025-03-25T08:42:57.082226Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics [Cell lines]\"\n", "!Series_summary\t\"Compositional statistics and random gene-sets were used to assign the tumor site of origin and histopathology of 18 epithelial ovarian cancer cell lines\"\n", "!Series_overall_design\t\"In the study presented here, we applied Gene Expression Compositional Assignment (GECA) to epithelial ovarian cell lines, using first a reference library of solid tumors (expO [http://www.intgen.org/expo/]) and then a second library of expert pathologically-reviewed epithelial ovarian cancer samples (GSE73551)\"\n", "Sample Characteristics Dictionary:\n", "{0: ['cell type: ovarian cells'], 1: ['cell line: COV504', 'cell line: COV362', 'cell line: UWB1.289+BRCA1', 'cell line: OV56', 'cell line: UWB1.289', 'cell line: COV318', 'cell line: NCI/ADR-RES', 'cell line: OVCAR3', 'cell line: OVCAR4', 'cell line: OVCAR8', 'cell line: IGR-OV1', 'cell line: SK-OV-3', 'cell line: OVCAR5', 'cell line: ES-2', 'cell line: TOV-21G', 'cell line: TOV-112D', 'cell line: PEO1', 'cell line: PEO4'], 2: ['tumor site of origin: Ovarian'], 3: ['histopathology: Serous', 'histopathology: Endometrioid', 'histopathology: Poorly differentiated serous', 'histopathology: Undifferentiated carcinoma', 'histopathology: Poorly differentiated carcinoma', 'histopathology: Moderately differentiated carcinoma', 'histopathology: Endometroid with serous/clear cell', 'histopathology: Well-differentiated adenocarcinoma', 'histopathology: Poorly differentiated clear cell', 'histopathology: Clear Cell']}\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": "d33e50fb", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "68646ed4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:42:57.083902Z", "iopub.status.busy": "2025-03-25T08:42:57.083787Z", "iopub.status.idle": "2025-03-25T08:42:57.091638Z", "shell.execute_reply": "2025-03-25T08:42:57.091297Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of clinical data:\n", "{'GSM1899888': [0.0], 'GSM1899889': [0.0], 'GSM1899890': [0.0], 'GSM1899891': [1.0], 'GSM1899892': [1.0], 'GSM1899893': [1.0], 'GSM1899894': [0.0], 'GSM1899895': [0.0], 'GSM1899896': [0.0], 'GSM1899897': [0.0], 'GSM1899898': [0.0], 'GSM1899899': [0.0], 'GSM1899900': [0.0], 'GSM1899901': [0.0], 'GSM1899902': [0.0], 'GSM1899903': [0.0], 'GSM1899904': [0.0], 'GSM1899905': [0.0], 'GSM1899906': [0.0], 'GSM1899907': [0.0], 'GSM1899908': [0.0], 'GSM1899909': [0.0], 'GSM1899910': [0.0], 'GSM1899911': [0.0], 'GSM1899912': [0.0], 'GSM1899913': [0.0], 'GSM1899914': [0.0], 'GSM1899915': [0.0], 'GSM1899916': [1.0], 'GSM1899917': [1.0], 'GSM1899918': [1.0], 'GSM1899919': [0.0], 'GSM1899920': [0.0], 'GSM1899921': [0.0], 'GSM1899922': [0.0], 'GSM1899923': [0.0], 'GSM1899924': [0.0], 'GSM1899925': [0.0], 'GSM1899926': [0.0], 'GSM1899927': [0.0], 'GSM1899928': [0.0], 'GSM1899929': [0.0], 'GSM1899930': [0.0], 'GSM1899931': [1.0], 'GSM1899932': [1.0], 'GSM1899933': [1.0], 'GSM1899934': [0.0], 'GSM1899935': [0.0], 'GSM1899936': [0.0], 'GSM1899937': [0.0], 'GSM1899938': [0.0], 'GSM1899939': [0.0]}\n", "Clinical data saved to ../../output/preprocess/Endometrioid_Cancer/clinical_data/GSE73637.csv\n" ] } ], "source": [ "# 1. Evaluate gene expression data availability\n", "# This dataset appears to be gene expression data from cell lines, not miRNA or methylation data\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Find keys for trait, age, and gender\n", "# For trait (Endometrioid Cancer), we can use histopathology (key 3)\n", "trait_row = 3\n", "\n", "# No age information is available in the sample characteristics\n", "age_row = None\n", "\n", "# No gender information is available (these are cell lines)\n", "gender_row = None\n", "\n", "# 2.2 Define conversion functions for each variable\n", "def convert_trait(value):\n", " \"\"\"Convert histopathology information to binary trait values for Endometrioid Cancer\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after the colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary: 1 for Endometrioid, 0 for others\n", " if 'Endometrioid' in value or 'Endometroid' in value: # Account for possible spelling variation\n", " return 1\n", " else:\n", " return 0\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age information - not available for this dataset\"\"\"\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender information - not available for this dataset\"\"\"\n", " return None\n", "\n", "# 3. Save metadata\n", "# Check if trait data is available (i.e., if trait_row is not None)\n", "is_trait_available = trait_row is not None\n", "\n", "# Save the initial filtering results\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_data, # This should be available from a previous step\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 data\n", " print(\"Preview of clinical data:\")\n", " print(preview_df(clinical_df))\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": "97d1b01a", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "90205418", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:42:57.092763Z", "iopub.status.busy": "2025-03-25T08:42:57.092564Z", "iopub.status.idle": "2025-03-25T08:42:57.734483Z", "shell.execute_reply": "2025-03-25T08:42:57.734102Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 61\n", "Header line: \"ID_REF\"\t\"GSM1899888\"\t\"GSM1899889\"\t\"GSM1899890\"\t\"GSM1899891\"\t\"GSM1899892\"\t\"GSM1899893\"\t\"GSM1899894\"\t\"GSM1899895\"\t\"GSM1899896\"\t\"GSM1899897\"\t\"GSM1899898\"\t\"GSM1899899\"\t\"GSM1899900\"\t\"GSM1899901\"\t\"GSM1899902\"\t\"GSM1899903\"\t\"GSM1899904\"\t\"GSM1899905\"\t\"GSM1899906\"\t\"GSM1899907\"\t\"GSM1899908\"\t\"GSM1899909\"\t\"GSM1899910\"\t\"GSM1899911\"\t\"GSM1899912\"\t\"GSM1899913\"\t\"GSM1899914\"\t\"GSM1899915\"\t\"GSM1899916\"\t\"GSM1899917\"\t\"GSM1899918\"\t\"GSM1899919\"\t\"GSM1899920\"\t\"GSM1899921\"\t\"GSM1899922\"\t\"GSM1899923\"\t\"GSM1899924\"\t\"GSM1899925\"\t\"GSM1899926\"\t\"GSM1899927\"\t\"GSM1899928\"\t\"GSM1899929\"\t\"GSM1899930\"\t\"GSM1899931\"\t\"GSM1899932\"\t\"GSM1899933\"\t\"GSM1899934\"\t\"GSM1899935\"\t\"GSM1899936\"\t\"GSM1899937\"\t\"GSM1899938\"\t\"GSM1899939\"\n", "First data line: 1\t9.113926239\t9.353160006\t9.340419788\t9.068727157\t8.993265236\t9.006226326\t10.25871409\t10.13329143\t10.13550261\t9.671620081\t9.57993636\t9.361243542\t9.883231123\t8.850008948\t8.631560726\t8.812670809\t10.30292328\t10.2893897\t10.34595556\t9.009325384\t9.172475557\t9.247407779\t8.964204246\t9.020628865\t9.207742138\t10.32458758\t10.36534658\t10.35711252\t9.734924289\t9.72507106\t9.784513633\t8.790526196\t8.835592535\t8.940153313\t9.218608395\t9.328660633\t9.128393683\t9.954817706\t9.82187008\t9.811056786\t9.606890499\t9.503037579\t9.620021927\t10.6333148\t10.40253562\t10.64616983\t9.202343369\t8.832061635\t9.433059912\t9.141812092\t9.320294952\t8.881678583\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Index(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',\n", " '14', '15', '16', '17', '18', '19', '20'],\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": "2f808057", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "c51253d7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:42:57.735834Z", "iopub.status.busy": "2025-03-25T08:42:57.735717Z", "iopub.status.idle": "2025-03-25T08:42:57.737650Z", "shell.execute_reply": "2025-03-25T08:42:57.737359Z" } }, "outputs": [], "source": [ "# Looking at the identifiers in the gene expression data, I can see they are just numeric indices (1, 2, 3, etc.)\n", "# These are not human gene symbols but rather numeric probe IDs that need to be mapped to gene symbols\n", "# This is typical for microarray data where probes need to be mapped to their corresponding genes\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "1e8f6583", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "157bd939", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:42:57.738728Z", "iopub.status.busy": "2025-03-25T08:42:57.738623Z", "iopub.status.idle": "2025-03-25T08:42:58.729856Z", "shell.execute_reply": "2025-03-25T08:42:58.729468Z" } }, "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 = GSE73637\n", "Line 6: !Series_title = Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics [Cell lines]\n", "Line 7: !Series_geo_accession = GSE73637\n", "Line 8: !Series_status = Public on Nov 08 2016\n", "Line 9: !Series_submission_date = Sep 30 2015\n", "Line 10: !Series_last_update_date = Nov 10 2016\n", "Line 11: !Series_pubmed_id = 27353327\n", "Line 12: !Series_summary = Compositional statistics and random gene-sets were used to assign the tumor site of origin and histopathology of 18 epithelial ovarian cancer cell lines\n", "Line 13: !Series_overall_design = In the study presented here, we applied Gene Expression Compositional Assignment (GECA) to epithelial ovarian cell lines, using first a reference library of solid tumors (expO [http://www.intgen.org/expo/]) and then a second library of expert pathologically-reviewed epithelial ovarian cancer samples (GSE73551)\n", "Line 14: !Series_type = Expression profiling by array\n", "Line 15: !Series_contributor = Jaine,K,Blayney\n", "Line 16: !Series_sample_id = GSM1899888\n", "Line 17: !Series_sample_id = GSM1899889\n", "Line 18: !Series_sample_id = GSM1899890\n", "Line 19: !Series_sample_id = GSM1899891\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': [1, 2, 3, 4, 5], 'ProbeSetID': ['200000_s_at', '200001_at', '200002_at', '200003_s_at', '200004_at'], 'GeneSymbol': ['PRPF8', 'CAPNS1', 'RPL35', 'RPL28', 'EIF4G2'], 'Array': ['Ovarian Cancer DSA', 'Ovarian Cancer DSA', 'Ovarian Cancer DSA', 'Ovarian Cancer DSA', 'Ovarian Cancer DSA'], 'Annotation Date': ['10-Jan-11', '10-Jan-11', '10-Jan-11', '10-Jan-11', '10-Jan-11'], 'Sequence Type': ['Affymetrix human normalisation control', 'Affymetrix human normalisation control', 'Affymetrix human normalisation control', 'Affymetrix human normalisation control', 'Affymetrix human normalisation control'], 'Ensembl Version': ['release 60', 'release 60', 'release 60', 'release 60', 'release 60'], 'Ensembl Genome Version': ['GRCh37', 'GRCh37', 'GRCh37', 'GRCh37', 'GRCh37'], 'Orientation / Description': ['Sense (Fully Exonic)', 'Sense (Fully Exonic)', 'Sense (Fully Exonic)', 'Sense (Fully Exonic)', 'Sense (Fully Exonic)'], 'No. probes aligned': ['9', '8', '8', '9', '11'], 'Probeset mapping position': ['Chr 17: 1554017-1554762', 'Chr 19: 36640714-36641203', 'Chr 9: 127622482-127623828', 'Chr 19: 55897742-55898063', 'Chr 11: 10818748-10819118'], 'Ensembl Gene ID': ['ENSG00000174231', 'ENSG00000126247', 'ENSG00000136942', 'ENSG00000108107', 'ENSG00000110321'], 'Chromosomal location': ['Chr 17p11.1', 'Chr 19p11', 'Chr 9p11.1', 'Chr 19p11', 'Chr 11p11.11'], 'Strand': ['Reverse Strand', 'Forward Strand', 'Reverse Strand', 'Forward Strand', 'Reverse Strand'], 'Gene Description': ['PRP8 pre-mRNA processing factor 8 homolog (S. cerevisiae) [Source:HGNC Symbol;Acc:17340]', 'calpain, small subunit 1 [Source:HGNC Symbol;Acc:1481]', 'ribosomal protein L35 [Source:HGNC Symbol;Acc:10344]', 'ribosomal protein L28 [Source:HGNC Symbol;Acc:10330]', 'eukaryotic translation initiation factor 4 gamma, 2 [Source:HGNC Symbol;Acc:3297]'], 'Entrez Gene': ['10594', '826', '11224', '6158', '1982'], 'Alias Gene Symbols': ['PRP8 /// PRPF8-001 /// PRPC8 /// HPRP8 /// RP13 /// Prp8', 'CDPS /// 30K /// CANPS /// CAPNS1-201 /// CAPNS1-202 /// CANP /// CSS1 /// CAPN4 /// CALPAIN4', 'RPL35-002 /// RPL35-005 /// RPL35-001 /// RPL35-004 /// RPL35-003', 'RPL28-203 /// RPL28-201 /// RPL28-205 /// FLJ43307 /// RPL28-202 /// RPL28-204', 'DAP5 /// AAG1 /// EIF4G2-204 /// EIF4G2-203 /// NAT1 /// p97 /// EIF4G2-202 /// FLJ41344 /// EIF4G2-201 /// P97'], 'Ensembl Transcript ID': ['ENST00000304992', 'ENST00000457326 /// ENST00000246533', 'ENST00000493018 /// ENST00000348462 /// ENST00000487431 /// ENST00000373570 /// ENST00000495728', 'ENST00000431533', 'ENST00000396525 /// ENST00000339995 /// ENST00000429377'], 'RefSeq Transcript ID': ['NM_006445.3', '--- /// NM_001003962.1 // NM_001749.2', '--- /// NM_007209.3 /// --- /// --- /// ---', 'NM_001136136.1', 'NM_001042559.2 /// NM_001172705.1 // NM_001418.3 /// NM_001172705.1'], 'Unigene ID': ['Hs.181368', '--- /// ---', '--- /// --- /// --- /// --- /// Hs.182825', '---', '--- /// Hs.183684 /// Hs.183684'], 'ORF': ['PRPF8', 'CAPNS1', 'RPL35', 'RPL28', 'EIF4G2'], 'GB_ACC': ['NM_006445', 'NM_001003962', 'NM_007209', 'NM_001136136', 'NM_001042559'], 'SPOT_ID': ['ENST00000304992', 'ENST00000457326 ENST00000246533', 'ENST00000493018 ENST00000348462 ENST00000487431 ENST00000373570 ENST00000495728', 'ENST00000431533', 'ENST00000396525 ENST00000339995 ENST00000429377']}\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": "3d7f6387", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "f0131c8c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:42:58.731257Z", "iopub.status.busy": "2025-03-25T08:42:58.731130Z", "iopub.status.idle": "2025-03-25T08:42:59.822573Z", "shell.execute_reply": "2025-03-25T08:42:59.822181Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping preview (first 5 rows):\n", " ID Gene\n", "0 1 PRPF8\n", "1 2 CAPNS1\n", "2 3 RPL35\n", "3 4 RPL28\n", "4 5 EIF4G2\n", "Total mapping entries: 120373\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data after mapping:\n", "Shape: (20172, 52)\n", "Sample of genes (first 5):\n", "Index(['A1BG', 'A1CF', 'A2LD1', 'A2M', 'A2ML1'], dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Endometrioid_Cancer/gene_data/GSE73637.csv\n" ] } ], "source": [ "# 1. Identify which columns in the gene annotation dataframe correspond to identifiers and symbols\n", "# From the gene annotation preview, we can see:\n", "# - 'ID' column contains numeric identifiers matching gene_data.index\n", "# - 'GeneSymbol' column contains the gene symbols we need\n", "\n", "# 2. Create a mapping dataframe from the gene annotation data\n", "mapping_df = gene_annotation[['ID', 'GeneSymbol']]\n", "mapping_df = mapping_df.rename(columns={'GeneSymbol': 'Gene'})\n", "mapping_df = mapping_df.dropna()\n", "mapping_df = mapping_df.astype({'ID': 'str'}) # Convert ID to string to match gene_data index\n", "\n", "print(\"Gene mapping preview (first 5 rows):\")\n", "print(mapping_df.head())\n", "print(f\"Total mapping entries: {len(mapping_df)}\")\n", "\n", "# 3. Apply the mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "\n", "print(\"\\nGene expression data after mapping:\")\n", "print(f\"Shape: {gene_data.shape}\")\n", "print(\"Sample of genes (first 5):\")\n", "print(gene_data.index[:5])\n", "\n", "# Save the gene expression data to CSV\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": "d5704c05", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "9b858016", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:42:59.823931Z", "iopub.status.busy": "2025-03-25T08:42:59.823807Z", "iopub.status.idle": "2025-03-25T08:43:10.815772Z", "shell.execute_reply": "2025-03-25T08:43:10.815128Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (20036, 52)\n", "First few genes with their expression values after normalization:\n", " GSM1899888 GSM1899889 GSM1899890 GSM1899891 GSM1899892 \\\n", "Gene \n", "A1BG 5.263569 5.598553 5.263475 6.084976 5.997425 \n", "A1CF 2.647713 2.383580 2.605635 2.587824 2.507772 \n", "A2M 6.292947 6.771711 5.949791 6.474002 6.744352 \n", "A2ML1 3.756357 2.472887 3.527244 2.165761 2.767217 \n", "A3GALT2 6.355374 6.217401 6.484687 6.666866 6.405594 \n", "\n", " GSM1899893 GSM1899894 GSM1899895 GSM1899896 GSM1899897 ... \\\n", "Gene ... \n", "A1BG 6.125724 5.257894 5.212058 5.444705 6.021699 ... \n", "A1CF 3.821882 3.124388 2.756973 3.170520 2.728037 ... \n", "A2M 6.833200 7.674972 7.644879 7.283829 6.150173 ... \n", "A2ML1 2.353374 2.475354 2.225454 2.432270 2.534942 ... \n", "A3GALT2 6.348537 6.816183 6.693292 6.754839 6.271786 ... \n", "\n", " GSM1899930 GSM1899931 GSM1899932 GSM1899933 GSM1899934 \\\n", "Gene \n", "A1BG 7.236568 6.078750 6.354082 6.612614 5.132084 \n", "A1CF 2.643583 2.481840 2.706462 2.628780 3.202872 \n", "A2M 6.844386 19.750863 19.743618 19.835477 7.906550 \n", "A2ML1 2.533890 2.317816 2.400485 2.515641 2.524883 \n", "A3GALT2 6.664464 6.208147 6.399175 6.524429 6.472844 \n", "\n", " GSM1899935 GSM1899936 GSM1899937 GSM1899938 GSM1899939 \n", "Gene \n", "A1BG 4.797291 5.079646 5.236975 5.478133 5.114841 \n", "A1CF 2.380427 2.695625 2.450733 2.426742 2.828121 \n", "A2M 10.295305 7.889205 7.065245 8.272117 6.548691 \n", "A2ML1 2.730668 2.379279 2.541936 2.378526 2.484086 \n", "A3GALT2 6.010204 6.609448 6.638761 6.632453 6.995594 \n", "\n", "[5 rows x 52 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Endometrioid_Cancer/gene_data/GSE73637.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Raw clinical data shape: (4, 53)\n", "Clinical features:\n", " GSM1899888 GSM1899889 GSM1899890 GSM1899891 \\\n", "Endometrioid_Cancer 0.0 0.0 0.0 1.0 \n", "\n", " GSM1899892 GSM1899893 GSM1899894 GSM1899895 \\\n", "Endometrioid_Cancer 1.0 1.0 0.0 0.0 \n", "\n", " GSM1899896 GSM1899897 ... GSM1899930 GSM1899931 \\\n", "Endometrioid_Cancer 0.0 0.0 ... 0.0 1.0 \n", "\n", " GSM1899932 GSM1899933 GSM1899934 GSM1899935 \\\n", "Endometrioid_Cancer 1.0 1.0 0.0 0.0 \n", "\n", " GSM1899936 GSM1899937 GSM1899938 GSM1899939 \n", "Endometrioid_Cancer 0.0 0.0 0.0 0.0 \n", "\n", "[1 rows x 52 columns]\n", "Clinical features saved to ../../output/preprocess/Endometrioid_Cancer/clinical_data/GSE73637.csv\n", "Linked data shape: (52, 20037)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Endometrioid_Cancer A1BG A1CF A2M A2ML1\n", "GSM1899888 0.0 5.263569 2.647713 6.292947 3.756357\n", "GSM1899889 0.0 5.598553 2.383580 6.771711 2.472887\n", "GSM1899890 0.0 5.263475 2.605635 5.949791 3.527244\n", "GSM1899891 1.0 6.084976 2.587824 6.474002 2.165761\n", "GSM1899892 1.0 5.997425 2.507772 6.744352 2.767217\n", "Missing values before handling:\n", " Trait (Endometrioid_Cancer) missing: 0 out of 52\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: (52, 20037)\n", "For the feature 'Endometrioid_Cancer', the least common label is '1.0' with 9 occurrences. This represents 17.31% of the dataset.\n", "The distribution of the feature 'Endometrioid_Cancer' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Endometrioid_Cancer/GSE73637.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 }