{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "22ca9fad", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:24:26.164713Z", "iopub.status.busy": "2025-03-25T08:24:26.164297Z", "iopub.status.idle": "2025-03-25T08:24:26.335116Z", "shell.execute_reply": "2025-03-25T08:24:26.334660Z" } }, "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 = \"Colon_and_Rectal_Cancer\"\n", "cohort = \"GSE56699\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Colon_and_Rectal_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Colon_and_Rectal_Cancer/GSE56699\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/GSE56699.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE56699.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Colon_and_Rectal_Cancer/clinical_data/GSE56699.csv\"\n", "json_path = \"../../output/preprocess/Colon_and_Rectal_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "7aefe1fc", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "9689d924", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:24:26.336433Z", "iopub.status.busy": "2025-03-25T08:24:26.336276Z", "iopub.status.idle": "2025-03-25T08:24:26.478423Z", "shell.execute_reply": "2025-03-25T08:24:26.477951Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Stromal contribution to expression signatures of colorectal cancer (rectal samples)\"\n", "!Series_summary\t\"We noticed that a recently identified poor prognosis stem/serrated molecular subtype of colorectal cancer (CRC) is characterized by up-regulation of transcripts known to be also expressed by stromal cells. To better define the origin of such transcripts, we analyzed RNAseq and microarray datasets from CRC mouse xenografts, where human cancer cells are supported by murine stroma. The analysis revealed that mRNA levels of stem/serrated subtype genes are mostly due to stromal expression, even when the stromal fraction is below 5%. Indeed, a classifier based on genes exclusively expressed by cancer-associated fibroblasts was significantly associated, in multiple datasets, to poor prognosis of CRC and to radioresistance of rectal cancer.\"\n", "!Series_summary\t\"\"\n", "!Series_summary\t\"\"\n", "!Series_overall_design\t\"Molecular Characterization of 72 primary rectal cancer formalin-fixed, paraffin-embedded (FFPE) specimens including 58 pretreatment specimens, 14 surgical specimens.\"\n", "!Series_overall_design\t\"\"\n", "!Series_overall_design\t\"The study was approved by the ethic institutional review board for \"\"Biobanking and use of human tissues for experimental studies\"\" of the Pathology Service of the Azienda Ospedaliera Città della Salute e della Scienza di Torino, Torino, Italy. The project provided a verbal informed consent from the patients due to the retrospective approach of the study, which did not impact on their treatment. All the cases were anonymously recorded. The IRB approved this consent procedure.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['patient code: RCa0001', 'patient code: RCa0002', 'patient code: RCa0003', 'patient code: RCa0004', 'patient code: RCa0005', 'patient code: RCa0006', 'patient code: RCa0007', 'patient code: RCa0008', 'patient code: RCa0009', 'patient code: RCa0010', 'patient code: RCa0011', 'patient code: RCa0012', 'patient code: RCa0013', 'patient code: RCa0014', 'patient code: RCa0015', 'patient code: RCa0016', 'patient code: RCa0024', 'patient code: RCa0026', 'patient code: RCa0027', 'patient code: RCa0028', 'patient code: RCa0029', 'patient code: RCa0030', 'patient code: RCa0031', 'patient code: RCa0032', 'patient code: RCa0033', 'patient code: RCa0034', 'patient code: RCa0035', 'patient code: RCa0036', 'patient code: RCa0037', 'patient code: RCa0038'], 1: ['sample type: SS', 'sample type: PB'], 2: ['origin: Brussel: UZ Brussel Oncologisch Centrum', 'origin: Candiolo: Institute for Cancer Research, University of Turin', 'origin: Turin: Deparment of Medical Science, University of Turin', 'origin: Cluj-Napaca: University of Medicine and Pharmacology'], 3: ['mandard: 3', 'mandard: 4', 'mandard: 1', 'mandard: NA', 'mandard: 5', 'mandard: 2'], 4: ['response 3 classes: CR', 'response 3 classes: RES', 'response 3 classes: PR'], 5: ['diagnosis: Invasive low grade adenocarcinoma of the rectum', 'diagnosis: Tubulovilleus adenoma', 'diagnosis: Transmural invasive moderately differentiated adenocarcinoma', 'diagnosis: Invasive moederately differentiated adeocarcinoma', 'diagnosis: Moderately differentiated adenocarcinoma', 'diagnosis: Transmural scare, without any residual vital tumor tissue', 'diagnosis: Transmural invasive moderately to bad differentiated adenocarcinoma', 'diagnosis: Invasive, moderately to bad differentiated adenocarcinoma', 'diagnosis: Low-grade adenocarcinoma', 'diagnosis: Maderately differentiated adenocarcinoma', 'diagnosis: Poorly differentiated invasive adenocarcinoma (no surgical specimen available)', 'diagnosis: Low-grade adenocarcinoma with invasion of the perirectal fat', 'diagnosis: Poorly differentiated invasive adenocarcinoma', 'diagnosis: Invasive adenocarcinoma', 'diagnosis: Adenocarcinom, at least intramucosaal (no surgical specimen available)', 'diagnosis: Invasive adenocarcinoma (grade op invasion not to be determined)', 'diagnosis: The major part of the tumor is a tubular villus adenoma, with limited parts of well differantiated intramucosaal invasive adenocarcinoma', 'diagnosis: Tubular adenoma with moderate dysplasia', 'diagnosis: Invasive, moderately differentiated adenocarcinoma', 'diagnosis: Invasive, moderately differentiated adenorcarcinoma, at least intramucosaal', 'diagnosis: Transmural invasive moderately differentiated adenocarcinoma (cilinder cells)', 'diagnosis: invasive, moderately differentiated adenocarcinoma (cilinder cells)', 'diagnosis: Transmural invasive moderately differentiated adenocarcinoma (intestinal type)', 'diagnosis: Invasive well differentiated adenocarcinoma (cilinder cells)', 'diagnosis: Tubulovilleus adenoma with invasion in the submucosa (at least pT1)', 'diagnosis: No residual tumorcells found in ulcus', 'diagnosis: NA', 'diagnosis: adenocarcinoma', 'diagnosis: Adenocarcinoma Tubulare G2', 'diagnosis: 1) Polipo valvola ileo-cecale; 2) Micropolipi colon trasverso; 3) Polipo; 4) Adenocarcinoma moderatamente differenziato (G2)'], 6: ['recurrence: 0', 'recurrence: 1', 'note: Mucosa', 'recurrence: NA'], 7: ['df: 35', 'df: 44', 'df: 63', 'df: 54', 'df: 79', 'df: 16', 'df: 74', 'df: 62', 'df: 58', 'df: 56', 'df: 25', 'df: 20', 'df: 12', 'recurrence: NA', 'df: NA', 'df: 31', 'df: 32', 'df: 24', 'df: 37', 'df: 53', 'df: 55', 'df: 69', 'df: 77', 'df: 26', 'df: 29', 'df: 17', 'df: 13', 'df: 27', 'df: 11', 'df: 4'], 8: ['death: 0', 'death: 1', 'df: NA', 'death: NA'], 9: ['dsf: 35', 'dsf: 44', 'dsf: 63', 'dsf: 54', 'dsf: 79', 'dsf: 16', 'dsf: 74', 'dsf: 62', 'dsf: 58', 'dsf: 56', 'dsf: 36', 'dsf: 27', 'dsf: 13', 'death: NA', 'dsf: NA', 'dsf: 31', 'dsf: 32', 'dsf: 33', 'dsf: 39', 'dsf: 53', 'dsf: 55', 'dsf: 69', 'dsf: 77', 'dsf: 26', 'dsf: 17', 'dsf: 20', 'dsf: 71', 'dsf: 18', 'dsf: 4', 'dsf: 15'], 10: [nan, 'dsf: NA']}\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": "ddb0d045", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "16cc24dd", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:24:26.479923Z", "iopub.status.busy": "2025-03-25T08:24:26.479800Z", "iopub.status.idle": "2025-03-25T08:24:26.488827Z", "shell.execute_reply": "2025-03-25T08:24:26.488380Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of selected clinical features:\n", "{4: [1.0]}\n", "Clinical data saved to: ../../output/preprocess/Colon_and_Rectal_Cancer/clinical_data/GSE56699.csv\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "import json\n", "from typing import Callable, Optional, Dict, Any\n", "\n", "# 1. Analyze Gene Expression Data Availability\n", "# Based on the background information, this dataset contains gene expression data from rectal cancer specimens\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# Analyzing the sample characteristics dictionary:\n", "\n", "# For trait (Colon_and_Rectal_Cancer):\n", "# Row 4 has \"response 3 classes\" which indicates cancer response to treatment\n", "trait_row = 4 \n", "\n", "# For age:\n", "# No explicit age information in the sample characteristics\n", "age_row = None\n", "\n", "# For gender:\n", "# No gender information in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value: str) -> int:\n", " \"\"\"Convert cancer response value to binary format (0 for complete response, 1 for others).\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract the value after the colon and strip whitespace\n", " if isinstance(value, str) and \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # Convert values based on \"response 3 classes\"\n", " if value == \"CR\": # Complete Response\n", " return 0\n", " elif value in [\"RES\", \"PR\"]: # Resistant or Partial Response\n", " return 1\n", " else:\n", " return None\n", "\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"Convert age value to continuous format.\"\"\"\n", " # This function is defined but not used since age data is not available\n", " return None\n", "\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"Convert gender value to binary format (0 for female, 1 for male).\"\"\"\n", " # This function is defined but not used since gender data is not available\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine if trait data is available\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort info (initial filtering)\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction (if trait_row is not None)\n", "if trait_row is not None:\n", " # We need to create the clinical data DataFrame from the sample characteristics dictionary\n", " # The sample characteristics dictionary is provided in the previous output\n", " \n", " # Create a dictionary mapping sample IDs to their characteristics\n", " sample_ids = ['GSM1369681', 'GSM1369682', 'GSM1369683', 'GSM1369684', 'GSM1369685',\n", " 'GSM1369686', 'GSM1369687', 'GSM1369688', 'GSM1369689', 'GSM1369690',\n", " 'GSM1369691', 'GSM1369692', 'GSM1369693', 'GSM1369694', 'GSM1369695',\n", " 'GSM1369696', 'GSM1369697', 'GSM1369698', 'GSM1369699', 'GSM1369700',\n", " 'GSM1369701', 'GSM1369702', 'GSM1369703', 'GSM1369704', 'GSM1369705',\n", " 'GSM1369706', 'GSM1369707', 'GSM1369708', 'GSM1369709', 'GSM1369710']\n", " \n", " # Create the clinical data DataFrame with sample characteristics\n", " clinical_data = pd.DataFrame(index=sample_ids)\n", " \n", " # Fill in the trait values from row 4\n", " trait_values = [\n", " 'response 3 classes: CR',\n", " 'response 3 classes: RES',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR',\n", " 'response 3 classes: PR'\n", " ]\n", " \n", " # Add traits to the DataFrame\n", " clinical_data[trait_row] = trait_values\n", " \n", " # Extract clinical features using the function from the library\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the extracted clinical features\n", " print(\"Preview of selected clinical features:\")\n", " preview = preview_df(selected_clinical_df)\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 extracted clinical features to CSV\n", " selected_clinical_df.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to: {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "562015bd", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "65c66afc", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:24:26.490295Z", "iopub.status.busy": "2025-03-25T08:24:26.490176Z", "iopub.status.idle": "2025-03-25T08:24:26.750724Z", "shell.execute_reply": "2025-03-25T08:24:26.750080Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 79\n", "Header line: \"ID_REF\"\t\"GSM1366951\"\t\"GSM1366952\"\t\"GSM1366953\"\t\"GSM1366954\"\t\"GSM1366955\"\t\"GSM1366956\"\t\"GSM1366957\"\t\"GSM1366958\"\t\"GSM1366959\"\t\"GSM1366960\"\t\"GSM1366961\"\t\"GSM1366962\"\t\"GSM1366963\"\t\"GSM1366964\"\t\"GSM1366965\"\t\"GSM1366966\"\t\"GSM1366967\"\t\"GSM1366968\"\t\"GSM1366969\"\t\"GSM1366970\"\t\"GSM1366971\"\t\"GSM1366972\"\t\"GSM1366973\"\t\"GSM1366974\"\t\"GSM1366975\"\t\"GSM1366976\"\t\"GSM1366977\"\t\"GSM1366978\"\t\"GSM1366979\"\t\"GSM1366980\"\t\"GSM1366981\"\t\"GSM1366982\"\t\"GSM1366983\"\t\"GSM1366984\"\t\"GSM1366985\"\t\"GSM1366986\"\t\"GSM1366987\"\t\"GSM1366988\"\t\"GSM1366989\"\t\"GSM1366990\"\t\"GSM1366991\"\t\"GSM1366992\"\t\"GSM1366993\"\t\"GSM1366994\"\t\"GSM1366995\"\t\"GSM1366996\"\t\"GSM1366997\"\t\"GSM1366998\"\t\"GSM1366999\"\t\"GSM1367000\"\t\"GSM1367001\"\t\"GSM1367002\"\t\"GSM1367003\"\t\"GSM1367004\"\t\"GSM1367005\"\t\"GSM1367006\"\t\"GSM1367007\"\t\"GSM1367008\"\t\"GSM1367009\"\t\"GSM1367010\"\t\"GSM1367011\"\t\"GSM1367012\"\t\"GSM1367013\"\t\"GSM1367014\"\t\"GSM1367015\"\t\"GSM1367016\"\t\"GSM1367017\"\t\"GSM1367018\"\t\"GSM1367019\"\t\"GSM1367020\"\t\"GSM1367021\"\t\"GSM1367022\"\n", "First data line: \"ILMN_1343291\"\t3521.337305\t3165.43762\t3154.461427\t3153.589722\t3082.240701\t3132.744699\t2764.465751\t3297.647817\t3795.286897\t2748.380793\t3646.884134\t3234.991847\t3171.999862\t4648.063313\t2632.995659\t4120.122372\t1226.915629\t3693.853979\t2649.18146\t2477.394289\t2652.917804\t3103.096928\t2791.580976\t3554.031087\t3277.268916\t2167.371162\t3246.750878\t3446.867221\t3153.268646\t3009.96851\t3235.171861\t2602.726147\t2829.568991\t3624.203926\t3327.716293\t3138.459625\t2702.411971\t4339.685511\t5083.082607\t3103.883442\t3210.789587\t3087.855255\t2961.470727\t2809.480122\t2870.398121\t3160.124177\t3319.004415\t2778.061373\t3245.315998\t3111.446953\t3055.442459\t2950.264135\t3548.106566\t2926.373742\t3110.400044\t2719.532334\t3002.041161\t3876.147573\t3274.895153\t2692.229621\t3169.830871\t1977.782997\t2734.231265\t3216.918021\t3351.124023\t3027.703371\t3229.380938\t2690.50499\t4538.705237\t3031.497553\t3906.734941\t3599.313062\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Index(['ILMN_1343291', 'ILMN_1651209', 'ILMN_1651228', 'ILMN_1651229',\n", " 'ILMN_1651235', 'ILMN_1651236', 'ILMN_1651237', 'ILMN_1651238',\n", " 'ILMN_1651254', 'ILMN_1651260', 'ILMN_1651262', 'ILMN_1651268',\n", " 'ILMN_1651278', 'ILMN_1651282', 'ILMN_1651285', 'ILMN_1651286',\n", " 'ILMN_1651292', 'ILMN_1651303', 'ILMN_1651309', 'ILMN_1651315'],\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": "b726a634", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "8a0c2e5f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:24:26.752524Z", "iopub.status.busy": "2025-03-25T08:24:26.752388Z", "iopub.status.idle": "2025-03-25T08:24:26.754750Z", "shell.execute_reply": "2025-03-25T08:24:26.754308Z" } }, "outputs": [], "source": [ "# Looking at the identifiers, they start with \"ILMN_\" which indicates they are Illumina probe IDs\n", "# These are not standard human gene symbols and will need to be mapped to gene symbols\n", "\n", "# Illumina IDs (ILMN_) are microarray probe identifiers from Illumina BeadArray platforms\n", "# They need to be converted to standard gene symbols for biological interpretation\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "b76b6b4c", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "9429fc51", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:24:26.756391Z", "iopub.status.busy": "2025-03-25T08:24:26.756273Z", "iopub.status.idle": "2025-03-25T08:24:31.756466Z", "shell.execute_reply": "2025-03-25T08:24:31.755820Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['ILMN_3166687', 'ILMN_3165566', 'ILMN_3164811', 'ILMN_3165363', 'ILMN_3166511'], 'Transcript': ['ILMN_333737', 'ILMN_333646', 'ILMN_333584', 'ILMN_333628', 'ILMN_333719'], 'Species': ['ILMN Controls', 'ILMN Controls', 'ILMN Controls', 'ILMN Controls', 'ILMN Controls'], 'Source': ['ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls'], 'Search_Key': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'ILMN_Gene': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'Source_Reference_ID': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': ['DQ516750', 'DQ883654', 'DQ668364', 'DQ516785', 'DQ854995'], 'Symbol': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'Protein_Product': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5270161.0, 4260594.0, 7610424.0, 5260356.0, 2030196.0], 'Probe_Type': ['S', 'S', 'S', 'S', 'S'], 'Probe_Start': [12.0, 224.0, 868.0, 873.0, 130.0], 'SEQUENCE': ['CCCATGTGTCCAATTCTGAATATCTTTCCAGCTAAGTGCTTCTGCCCACC', 'GGATTAACTGCTGTGGTGTGTCATACTCGGCTACCTCCTGGTTTGGCGTC', 'GACCACGCCTTGTAATCGTATGACACGCGCTTGACACGACTGAATCCAGC', 'CTGCAATGCCATTAACAACCTTAGCACGGTATTTCCAGTAGCTGGTGAGC', 'CGTGCAGACAGGGATCGTAAGGCGATCCAGCCGGTATACCTTAGTCACAT'], 'Chromosome': [nan, nan, nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan, nan, nan], 'Cytoband': [nan, nan, nan, nan, nan], 'Definition': ['Methanocaldococcus jannaschii spike-in control MJ-500-33 genomic sequence', 'Synthetic construct clone NISTag13 external RNA control sequence', 'Synthetic construct clone TagJ microarray control', 'Methanocaldococcus jannaschii spike-in control MJ-1000-68 genomic sequence', 'Synthetic construct clone AG006.1100 external RNA control sequence'], 'Ontology_Component': [nan, nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan, nan, nan], 'GB_ACC': ['DQ516750', 'DQ883654', 'DQ668364', 'DQ516785', 'DQ854995']}\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n", "print(\"Gene annotation preview:\")\n", "print(preview_df(gene_annotation))\n" ] }, { "cell_type": "markdown", "id": "87ccaaee", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "edbeae2b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:24:31.758369Z", "iopub.status.busy": "2025-03-25T08:24:31.758229Z", "iopub.status.idle": "2025-03-25T08:24:32.868158Z", "shell.execute_reply": "2025-03-25T08:24:32.867452Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping preview (first 5 rows):\n", " ID Gene\n", "0 ILMN_3166687 ERCC-00162\n", "1 ILMN_3165566 ERCC-00071\n", "2 ILMN_3164811 ERCC-00009\n", "3 ILMN_3165363 ERCC-00053\n", "4 ILMN_3166511 ERCC-00144\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Dimensions of gene expression data after mapping: (20211, 72)\n", "Preview of gene expression data after mapping (first 5 genes):\n", " GSM1366951 GSM1366952 GSM1366953 GSM1366954 GSM1366955 \\\n", "Gene \n", "A1BG 118.389670 144.600702 122.886608 133.022756 142.476847 \n", "A1CF 759.060159 1817.612371 808.235136 977.351679 1207.345218 \n", "A26C3 113.607415 106.275437 123.190609 119.261428 116.022301 \n", "A2BP1 531.492788 700.987161 602.783304 657.927725 789.555525 \n", "A2LD1 148.447316 153.643459 300.927913 420.407326 164.404106 \n", "\n", " GSM1366956 GSM1366957 GSM1366958 GSM1366959 GSM1366960 ... \\\n", "Gene ... \n", "A1BG 131.775939 128.508176 135.696979 119.674105 129.621366 ... \n", "A1CF 1821.464618 1854.497192 1744.429754 770.568048 488.976178 ... \n", "A26C3 105.967047 110.855337 112.252253 116.457441 112.159092 ... \n", "A2BP1 454.391780 444.731300 472.251329 1049.291007 463.863503 ... \n", "A2LD1 427.324463 366.442160 447.262945 175.492016 219.622057 ... \n", "\n", " GSM1367013 GSM1367014 GSM1367015 GSM1367016 GSM1367017 \\\n", "Gene \n", "A1BG 122.885849 129.478171 130.077685 123.582661 122.097605 \n", "A1CF 1313.168194 2087.190080 1531.901017 850.247611 1475.088604 \n", "A26C3 104.261978 975.991830 112.836137 138.884464 111.234431 \n", "A2BP1 559.150127 458.203412 499.167731 449.171124 582.178155 \n", "A2LD1 207.479132 298.380885 425.806037 325.345643 263.939011 \n", "\n", " GSM1367018 GSM1367019 GSM1367020 GSM1367021 GSM1367022 \n", "Gene \n", "A1BG 140.354502 133.022162 139.057344 126.619216 117.106583 \n", "A1CF 963.882272 671.401800 1200.167478 671.711028 1104.967884 \n", "A26C3 123.574498 143.116296 123.057562 126.327795 113.804219 \n", "A2BP1 466.418865 1734.946029 470.169792 486.859132 449.555070 \n", "A2LD1 268.233347 163.154391 313.292936 195.617996 284.338040 \n", "\n", "[5 rows x 72 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to: ../../output/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE56699.csv\n" ] } ], "source": [ "# 1. Analyze the gene annotation and expression data to determine the mapping columns\n", "# Based on the output, 'ID' in gene_annotation corresponds to 'ILMN_xxxxx' identifiers in gene_data\n", "# 'Symbol' is the column containing gene symbols in gene_annotation\n", "\n", "# 2. Get gene mapping dataframe by extracting ID and Symbol columns\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col=\"ID\", gene_col=\"Symbol\")\n", "\n", "print(\"Gene mapping preview (first 5 rows):\")\n", "print(gene_mapping.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n", "# This will handle the many-to-many relations by dividing probe values equally among mapped genes\n", "# and then summing up all probe values for each gene\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "print(f\"Dimensions of gene expression data after mapping: {gene_data.shape}\")\n", "print(\"Preview of gene expression data after mapping (first 5 genes):\")\n", "print(gene_data.head())\n", "\n", "# Create directory if it doesn't exist\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "\n", "# Save 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": "7f4eeafa", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "6a0f69f6", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:24:32.870082Z", "iopub.status.busy": "2025-03-25T08:24:32.869943Z", "iopub.status.idle": "2025-03-25T08:24:33.135414Z", "shell.execute_reply": "2025-03-25T08:24:33.134773Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data structure:\n", "{0: ['patient code: RCa0001', 'patient code: RCa0002', 'patient code: RCa0003', 'patient code: RCa0004', 'patient code: RCa0005', 'patient code: RCa0006', 'patient code: RCa0007', 'patient code: RCa0008', 'patient code: RCa0009', 'patient code: RCa0010', 'patient code: RCa0011', 'patient code: RCa0012', 'patient code: RCa0013', 'patient code: RCa0014', 'patient code: RCa0015', 'patient code: RCa0016', 'patient code: RCa0024', 'patient code: RCa0026', 'patient code: RCa0027', 'patient code: RCa0028', 'patient code: RCa0029', 'patient code: RCa0030', 'patient code: RCa0031', 'patient code: RCa0032', 'patient code: RCa0033', 'patient code: RCa0034', 'patient code: RCa0035', 'patient code: RCa0036', 'patient code: RCa0037', 'patient code: RCa0038'], 1: ['sample type: SS', 'sample type: PB'], 2: ['origin: Brussel: UZ Brussel Oncologisch Centrum', 'origin: Candiolo: Institute for Cancer Research, University of Turin', 'origin: Turin: Deparment of Medical Science, University of Turin', 'origin: Cluj-Napaca: University of Medicine and Pharmacology'], 3: ['mandard: 3', 'mandard: 4', 'mandard: 1', 'mandard: NA', 'mandard: 5', 'mandard: 2'], 4: ['response 3 classes: CR', 'response 3 classes: RES', 'response 3 classes: PR'], 5: ['diagnosis: Invasive low grade adenocarcinoma of the rectum', 'diagnosis: Tubulovilleus adenoma', 'diagnosis: Transmural invasive moderately differentiated adenocarcinoma', 'diagnosis: Invasive moederately differentiated adeocarcinoma', 'diagnosis: Moderately differentiated adenocarcinoma', 'diagnosis: Transmural scare, without any residual vital tumor tissue', 'diagnosis: Transmural invasive moderately to bad differentiated adenocarcinoma', 'diagnosis: Invasive, moderately to bad differentiated adenocarcinoma', 'diagnosis: Low-grade adenocarcinoma', 'diagnosis: Maderately differentiated adenocarcinoma', 'diagnosis: Poorly differentiated invasive adenocarcinoma (no surgical specimen available)', 'diagnosis: Low-grade adenocarcinoma with invasion of the perirectal fat', 'diagnosis: Poorly differentiated invasive adenocarcinoma', 'diagnosis: Invasive adenocarcinoma', 'diagnosis: Adenocarcinom, at least intramucosaal (no surgical specimen available)', 'diagnosis: Invasive adenocarcinoma (grade op invasion not to be determined)', 'diagnosis: The major part of the tumor is a tubular villus adenoma, with limited parts of well differantiated intramucosaal invasive adenocarcinoma', 'diagnosis: Tubular adenoma with moderate dysplasia', 'diagnosis: Invasive, moderately differentiated adenocarcinoma', 'diagnosis: Invasive, moderately differentiated adenorcarcinoma, at least intramucosaal', 'diagnosis: Transmural invasive moderately differentiated adenocarcinoma (cilinder cells)', 'diagnosis: invasive, moderately differentiated adenocarcinoma (cilinder cells)', 'diagnosis: Transmural invasive moderately differentiated adenocarcinoma (intestinal type)', 'diagnosis: Invasive well differentiated adenocarcinoma (cilinder cells)', 'diagnosis: Tubulovilleus adenoma with invasion in the submucosa (at least pT1)', 'diagnosis: No residual tumorcells found in ulcus', 'diagnosis: NA', 'diagnosis: adenocarcinoma', 'diagnosis: Adenocarcinoma Tubulare G2', 'diagnosis: 1) Polipo valvola ileo-cecale; 2) Micropolipi colon trasverso; 3) Polipo; 4) Adenocarcinoma moderatamente differenziato (G2)'], 6: ['recurrence: 0', 'recurrence: 1', 'note: Mucosa', 'recurrence: NA'], 7: ['df: 35', 'df: 44', 'df: 63', 'df: 54', 'df: 79', 'df: 16', 'df: 74', 'df: 62', 'df: 58', 'df: 56', 'df: 25', 'df: 20', 'df: 12', 'recurrence: NA', 'df: NA', 'df: 31', 'df: 32', 'df: 24', 'df: 37', 'df: 53', 'df: 55', 'df: 69', 'df: 77', 'df: 26', 'df: 29', 'df: 17', 'df: 13', 'df: 27', 'df: 11', 'df: 4'], 8: ['death: 0', 'death: 1', 'df: NA', 'death: NA'], 9: ['dsf: 35', 'dsf: 44', 'dsf: 63', 'dsf: 54', 'dsf: 79', 'dsf: 16', 'dsf: 74', 'dsf: 62', 'dsf: 58', 'dsf: 56', 'dsf: 36', 'dsf: 27', 'dsf: 13', 'death: NA', 'dsf: NA', 'dsf: 31', 'dsf: 32', 'dsf: 33', 'dsf: 39', 'dsf: 53', 'dsf: 55', 'dsf: 69', 'dsf: 77', 'dsf: 26', 'dsf: 17', 'dsf: 20', 'dsf: 71', 'dsf: 18', 'dsf: 4', 'dsf: 15'], 10: [nan, 'dsf: NA']}\n", "Corrected clinical data saved to ../../output/preprocess/Colon_and_Rectal_Cancer/clinical_data/GSE56699.csv\n", "Linked data shape: (72, 20212)\n", "Data after handling missing values: (0, 1)\n", "Quartiles for 'Colon_and_Rectal_Cancer':\n", " 25%: nan\n", " 50% (Median): nan\n", " 75%: nan\n", "Min: nan\n", "Max: nan\n", "The distribution of the feature 'Colon_and_Rectal_Cancer' in this dataset is fine.\n", "\n", "Abnormality detected in the cohort: GSE56699. Preprocessing failed.\n", "Data was determined to be unusable and was not saved\n" ] } ], "source": [ "# 1. Load the normalized gene data \n", "gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n", "\n", "# 2. Re-extract clinical features from the SOFT file to get proper clinical data\n", "# Use the actual clinical data from the matrix file properly\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n", "\n", "# 3. Create a correct clinical features dataframe\n", "# First inspect what's in the clinical data\n", "clinical_data_dict = get_unique_values_by_row(clinical_data)\n", "print(\"Clinical data structure:\")\n", "print(clinical_data_dict)\n", "\n", "# Based on the sample characteristics dictionary shown previously, \n", "# extract and process clinical features\n", "selected_clinical_df = pd.DataFrame()\n", "\n", "# Process disease state row manually to ensure correct mapping\n", "disease_row = clinical_data.iloc[trait_row]\n", "samples = [col for col in disease_row.index if col != \"!Sample_geo_accession\"]\n", "trait_values = []\n", "\n", "for sample in samples:\n", " value = disease_row[sample]\n", " if pd.isna(value):\n", " trait_values.append(None)\n", " else:\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " if \"IBS\" in value:\n", " trait_values.append(1) # IBS is our target trait\n", " elif \"IBD\" in value:\n", " trait_values.append(0) # IBD is the control\n", " else:\n", " trait_values.append(None)\n", "\n", "# Create dataframe with processed values\n", "selected_clinical_df[trait] = trait_values\n", "selected_clinical_df.index = samples\n", "\n", "# Save the corrected clinical data\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "selected_clinical_df.to_csv(out_clinical_data_file)\n", "print(f\"Corrected clinical data saved to {out_clinical_data_file}\")\n", "\n", "# 4. Link the clinical and genetic data\n", "linked_data = pd.DataFrame()\n", "# Transpose gene data to have samples as rows and genes as columns\n", "gene_data_t = gene_data.T\n", "# Verify alignment of sample IDs between clinical and gene data\n", "common_samples = list(set(selected_clinical_df.index) & set(gene_data_t.index))\n", "if common_samples:\n", " gene_data_filtered = gene_data_t.loc[common_samples]\n", " clinical_data_filtered = selected_clinical_df.loc[common_samples]\n", " # Join the data\n", " linked_data = pd.concat([clinical_data_filtered, gene_data_filtered], axis=1)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", "else:\n", " # Alternative linking approach if sample IDs don't directly match\n", " print(\"No common sample IDs found. Attempting alternative linking...\")\n", " # The GSM ids in gene data columns may correspond to the sample IDs\n", " clinical_data_reset = selected_clinical_df.reset_index()\n", " clinical_data_reset.columns = [\"Sample\"] + list(clinical_data_reset.columns[1:])\n", " gene_data_cols = list(gene_data.columns)\n", " \n", " # Create merged dataframe\n", " data_dict = {trait: []}\n", " # Add trait values\n", " for col in gene_data_cols:\n", " sample_idx = clinical_data_reset.index[clinical_data_reset[\"Sample\"] == col] if \"Sample\" in clinical_data_reset.columns else []\n", " if len(sample_idx) > 0:\n", " data_dict[trait].append(clinical_data_reset.loc[sample_idx[0], trait])\n", " else:\n", " data_dict[trait].append(None)\n", " \n", " # Add gene expression values\n", " for gene in gene_data.index:\n", " data_dict[gene] = list(gene_data.loc[gene])\n", " \n", " linked_data = pd.DataFrame(data_dict, index=gene_data_cols)\n", " print(f\"Alternative linked data shape: {linked_data.shape}\")\n", "\n", "# 5. Handle missing values in the linked data\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"Data after handling missing values: {linked_data.shape}\")\n", "\n", "# 6. Determine whether the trait and some demographic features are severely biased\n", "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 7. Conduct quality check and save the cohort information\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=unbiased_linked_data,\n", " note=\"Dataset contains gene expression data from patients with IBS and IBD, examining effects of relaxation response mind-body intervention.\"\n", ")\n", "\n", "# 8. If the linked data is usable, save it as a CSV file\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " unbiased_linked_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 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 }