{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "623427dd", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:12:11.350220Z", "iopub.status.busy": "2025-03-25T05:12:11.350066Z", "iopub.status.idle": "2025-03-25T05:12:11.526085Z", "shell.execute_reply": "2025-03-25T05:12:11.525707Z" } }, "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 = \"GSE107754\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Esophageal_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Esophageal_Cancer/GSE107754\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Esophageal_Cancer/GSE107754.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Esophageal_Cancer/gene_data/GSE107754.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Esophageal_Cancer/clinical_data/GSE107754.csv\"\n", "json_path = \"../../output/preprocess/Esophageal_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "29d265d4", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "97b38726", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:12:11.527632Z", "iopub.status.busy": "2025-03-25T05:12:11.527454Z", "iopub.status.idle": "2025-03-25T05:12:11.838225Z", "shell.execute_reply": "2025-03-25T05:12:11.837829Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"A novel genomic signature predicting FDG uptake in diverse metastatic tumors\"\n", "!Series_summary\t\"Purpose: Building a universal genomic signature predicting the intensity of FDG uptake in diverse metastatic tumors may allow us to understand better the biological processes underlying this phenomenon and their requirements of glucose uptake.\"\n", "!Series_summary\t\"Methods: A balanced training set (n=71) of metastatic tumors including some of the most frequent histologies, with matched PET/CT quantification measurements and whole human genome gene expression microarrays, was used to build the signature. Selection of microarray features was carried out exclusively on the basis of their strong association with FDG uptake (as measured by SUVmean35) by means of univariate linear regression. A thorough bioinformatics study of these genes was performed and multivariable models were built by fitting several state of the art regression techniques to the training set for comparison.\"\n", "!Series_summary\t\"Results: The 909 probes with the strongest association with the SUVmean35 (comprising 742 identifiable genes and 62 probes not matched to a symbol) were used to build the signature. Partial Least Squares using 3 components (PLS-3) was the best performing model in the training dataset cross-validation (Root Mean Square Error, RMSE=0.443) and was validated further in an independent validation dataset (n=13) obtaining a performance within the 95% CI of that obtained in the training dataset (RMSE=0.645). Significantly overrepresented biological processes correlating with the SUVmean35 were identified beyond glycolysis, such as ribosome biogenesis and DNA replication (correlating with a higher SUVmean35), and cytoskeleton reorganization and autophagy (correlating with a lower SUVmean35), among others.\"\n", "!Series_summary\t\"Conclusions: PLS-3 is a signature predicting accurately the intensity of FDG uptake in diverse metastatic tumors. FDG-PET might help in the design of specific targeted therapies directed to counteract the identified malignant biological processes more likely activated in a tumor as inferred from the SUVmean35 and also from its variations in response to antineoplastic treatments.\"\n", "!Series_overall_design\t\"Whole human genome microarrays from biopsies of human metastatic tumors (71 patients) with matched SUVmean35 measurements, this submission includes the 71 patients of the training set used to build the genomic signature predicting FDG uptake in diverse metastatic tumors. This dataset is complemented with a validation set comprised of 13 patients.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: Male', 'gender: Female'], 1: ['dataset: Validation set', 'dataset: Training set'], 2: ['biopsy location: Lung', 'biopsy location: Lymph node', 'biopsy location: Primary', 'biopsy location: Liver', 'biopsy location: Retroperitoneal implant', 'tissue: Pancreatic cancer', 'tissue: Esophagus cancer', 'tissue: Breast cancer', 'tissue: Colorectal cancer', 'tissue: Ovarian cancer', 'tissue: Head&neck cancer', 'tissue: Lung cancer', 'tissue: Malignant Melanoma', 'tissue: Endometrial cancer', 'tissue: Cervix cancer', 'tissue: Soft tissue sarcoma', 'tissue: Gastric cancer', 'tissue: Unknown primary', 'tissue: Malignant Mesothelioma', 'tissue: Thyroid cancer', 'tissue: Testes cancer', 'tissue: Non Hodgkin lymphoma', 'tissue: Merkel cell carcinoma', 'tissue: Vaginal cancer', 'tissue: Kidney cancer', 'tissue: Cervical cancer', 'tissue: Bile duct cancer', 'tissue: Urothelial cancer'], 3: ['suvmean35: 4.09', 'suvmean35: 8.36', 'suvmean35: 5.18', 'suvmean35: 10.74', 'suvmean35: 8.62', 'suvmean35: 8.02', 'suvmean35: 6.87', 'suvmean35: 4.93', 'suvmean35: 1.96', 'suvmean35: 8.83', 'suvmean35: 3.96', 'suvmean35: 3.38', 'suvmean35: 9.95', 'suvmean35: 5.19', 'suvmean35: 7.22', 'suvmean35: 5.02', 'suvmean35: 4.92', 'suvmean35: 4.99', 'suvmean35: 4.01', 'suvmean35: 2.52', 'suvmean35: 5.52', 'suvmean35: 8.38', 'suvmean35: 3.46', 'suvmean35: 4.07', 'suvmean35: 4.67', 'suvmean35: 7.09', 'suvmean35: 4.83', 'suvmean35: 6.7', 'suvmean35: 3.95', 'suvmean35: 5.03']}\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": "5a9e4ef9", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "1bb1361a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:12:11.840005Z", "iopub.status.busy": "2025-03-25T05:12:11.839879Z", "iopub.status.idle": "2025-03-25T05:12:11.856307Z", "shell.execute_reply": "2025-03-25T05:12:11.855962Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of clinical features: {0: [0.0, 1.0], 1: [0.0, 0.0], 2: [0.0, nan], 3: [0.0, nan], 4: [0.0, nan], 5: [0.0, nan], 6: [1.0, nan], 7: [0.0, nan], 8: [0.0, nan], 9: [0.0, nan], 10: [0.0, nan], 11: [0.0, nan], 12: [0.0, nan], 13: [0.0, nan], 14: [0.0, nan], 15: [0.0, nan], 16: [0.0, nan], 17: [0.0, nan], 18: [0.0, nan], 19: [0.0, nan], 20: [0.0, nan], 21: [0.0, nan], 22: [0.0, nan], 23: [0.0, nan], 24: [0.0, nan], 25: [0.0, nan], 26: [0.0, nan], 27: [0.0, nan], 28: [nan, nan], 29: [nan, nan]}\n", "Clinical data saved to: ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE107754.csv\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import re\n", "import json\n", "from typing import Optional, Callable, Dict, Any\n", "\n", "# 1. Analyze gene expression data availability\n", "# Based on the background information, this appears to be gene expression microarray data\n", "# The series summary mentions \"whole human genome gene expression microarrays\"\n", "is_gene_available = True\n", "\n", "# 2. Analyze clinical features\n", "# 2.1 Data Availability\n", "# For trait - Esophageal Cancer, look at row 2 where tissue types are mentioned\n", "trait_row = 2 # Row containing tissue information\n", "\n", "# For gender, it's in row 0\n", "gender_row = 0 # Row containing gender information\n", "\n", "# For age, I don't see age information in the sample characteristics\n", "age_row = None # Age data not available\n", "\n", "# 2.2 Data Type Conversion functions\n", "def convert_trait(value):\n", " \"\"\"Convert tissue type to binary for Esophageal Cancer.\"\"\"\n", " if pd.isna(value) or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after colon\n", " match = re.search(r':\\s*(.+)', value)\n", " if not match:\n", " return None\n", " \n", " tissue_value = match.group(1).strip().lower()\n", " \n", " # Check if it's esophageal cancer (binary classification)\n", " if 'esophagus cancer' in tissue_value:\n", " return 1\n", " else:\n", " return 0\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender to binary (0 for female, 1 for male).\"\"\"\n", " if pd.isna(value) or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after colon\n", " match = re.search(r':\\s*(.+)', value)\n", " if not match:\n", " return None\n", " \n", " gender_value = match.group(1).strip().lower()\n", " \n", " if 'female' in gender_value:\n", " return 0\n", " elif 'male' in gender_value:\n", " return 1\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Placeholder function for age conversion (not used as age is not available).\"\"\"\n", " return None\n", "\n", "# 3. Save metadata for initial filtering\n", "# Trait data is available if trait_row is not None\n", "is_trait_available = trait_row is not None\n", "\n", "# Initial validation\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Extract clinical features if trait data is available\n", "if trait_row is not None:\n", " # Load or create clinical data in the correct format\n", " # The sample characteristics dictionary is not in the right format for direct conversion to DataFrame\n", " # We need to find an existing clinical data file or properly structure the data\n", " \n", " clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n", " \n", " if os.path.exists(clinical_data_path):\n", " clinical_data = pd.read_csv(clinical_data_path)\n", " else:\n", " # Create a proper DataFrame structure where columns are samples and rows are features\n", " # Assuming we need to create it from the sample_dict\n", " sample_dict = {\n", " 0: ['gender: Male', 'gender: Female'],\n", " 1: ['dataset: Validation set', 'dataset: Training set'],\n", " 2: ['biopsy location: Lung', 'biopsy location: Lymph node', 'biopsy location: Primary', \n", " 'biopsy location: Liver', 'biopsy location: Retroperitoneal implant', \n", " 'tissue: Pancreatic cancer', 'tissue: Esophagus cancer', 'tissue: Breast cancer', \n", " 'tissue: Colorectal cancer', 'tissue: Ovarian cancer', 'tissue: Head&neck cancer', \n", " 'tissue: Lung cancer', 'tissue: Malignant Melanoma', 'tissue: Endometrial cancer', \n", " 'tissue: Cervix cancer', 'tissue: Soft tissue sarcoma', 'tissue: Gastric cancer', \n", " 'tissue: Unknown primary', 'tissue: Malignant Mesothelioma', 'tissue: Thyroid cancer', \n", " 'tissue: Testes cancer', 'tissue: Non Hodgkin lymphoma', 'tissue: Merkel cell carcinoma', \n", " 'tissue: Vaginal cancer', 'tissue: Kidney cancer', 'tissue: Cervical cancer', \n", " 'tissue: Bile duct cancer', 'tissue: Urothelial cancer'],\n", " 3: ['suvmean35: 4.09', 'suvmean35: 8.36', 'suvmean35: 5.18', 'suvmean35: 10.74', \n", " 'suvmean35: 8.62', 'suvmean35: 8.02', 'suvmean35: 6.87', 'suvmean35: 4.93', \n", " 'suvmean35: 1.96', 'suvmean35: 8.83', 'suvmean35: 3.96', 'suvmean35: 3.38', \n", " 'suvmean35: 9.95', 'suvmean35: 5.19', 'suvmean35: 7.22', 'suvmean35: 5.02', \n", " 'suvmean35: 4.92', 'suvmean35: 4.99', 'suvmean35: 4.01', 'suvmean35: 2.52', \n", " 'suvmean35: 5.52', 'suvmean35: 8.38', 'suvmean35: 3.46', 'suvmean35: 4.07', \n", " 'suvmean35: 4.67', 'suvmean35: 7.09', 'suvmean35: 4.83', 'suvmean35: 6.7', \n", " 'suvmean35: 3.95', 'suvmean35: 5.03']\n", " }\n", " \n", " # Create an index of feature rows\n", " index = list(sample_dict.keys())\n", " \n", " # Find the maximum number of samples (columns) needed\n", " max_cols = max(len(values) for values in sample_dict.values())\n", " \n", " # Create an empty DataFrame with the right dimensions\n", " clinical_data = pd.DataFrame(index=index, columns=range(max_cols))\n", " \n", " # Fill in the DataFrame\n", " for row, values in sample_dict.items():\n", " for col, value in enumerate(values):\n", " clinical_data.loc[row, col] = value\n", " \n", " # Extract clinical features\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender,\n", " age_row=age_row,\n", " convert_age=convert_age\n", " )\n", " \n", " # Preview the extracted features\n", " preview = preview_df(clinical_features)\n", " print(f\"Preview of clinical features: {preview}\")\n", " \n", " # Save the processed clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to: {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "0d90ddac", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "9c983200", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:12:11.857634Z", "iopub.status.busy": "2025-03-25T05:12:11.857517Z", "iopub.status.idle": "2025-03-25T05:12:12.263518Z", "shell.execute_reply": "2025-03-25T05:12:12.263174Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 74\n", "Header line: \"ID_REF\"\t\"GSM2878070\"\t\"GSM2878071\"\t\"GSM2878072\"\t\"GSM2878073\"\t\"GSM2878074\"\t\"GSM2878075\"\t\"GSM2878076\"\t\"GSM2878077\"\t\"GSM2878078\"\t\"GSM2878079\"\t\"GSM2878080\"\t\"GSM2878081\"\t\"GSM2878082\"\t\"GSM2891194\"\t\"GSM2891195\"\t\"GSM2891196\"\t\"GSM2891197\"\t\"GSM2891198\"\t\"GSM2891199\"\t\"GSM2891200\"\t\"GSM2891201\"\t\"GSM2891202\"\t\"GSM2891203\"\t\"GSM2891204\"\t\"GSM2891205\"\t\"GSM2891206\"\t\"GSM2891207\"\t\"GSM2891208\"\t\"GSM2891209\"\t\"GSM2891210\"\t\"GSM2891211\"\t\"GSM2891212\"\t\"GSM2891213\"\t\"GSM2891214\"\t\"GSM2891215\"\t\"GSM2891216\"\t\"GSM2891217\"\t\"GSM2891218\"\t\"GSM2891219\"\t\"GSM2891220\"\t\"GSM2891221\"\t\"GSM2891222\"\t\"GSM2891223\"\t\"GSM2891224\"\t\"GSM2891225\"\t\"GSM2891226\"\t\"GSM2891227\"\t\"GSM2891228\"\t\"GSM2891229\"\t\"GSM2891230\"\t\"GSM2891231\"\t\"GSM2891232\"\t\"GSM2891233\"\t\"GSM2891234\"\t\"GSM2891235\"\t\"GSM2891236\"\t\"GSM2891237\"\t\"GSM2891238\"\t\"GSM2891239\"\t\"GSM2891240\"\t\"GSM2891241\"\t\"GSM2891242\"\t\"GSM2891243\"\t\"GSM2891244\"\t\"GSM2891245\"\t\"GSM2891246\"\t\"GSM2891247\"\t\"GSM2891248\"\t\"GSM2891249\"\t\"GSM2891250\"\t\"GSM2891251\"\t\"GSM2891252\"\t\"GSM2891253\"\t\"GSM2891254\"\t\"GSM2891255\"\t\"GSM2891256\"\t\"GSM2891257\"\t\"GSM2891258\"\t\"GSM2891259\"\t\"GSM2891260\"\t\"GSM2891261\"\t\"GSM2891262\"\t\"GSM2891263\"\t\"GSM2891264\"\n", "First data line: \"A_23_P100001\"\t9.573244642\t9.298651171\t10.77599722\t11.00185427\t9.95489404\t10.47234414\t10.36470678\t11.29547995\t10.6379278\t12.59984726\t9.441342686\t9.411939603\t11.10415919\t10.69280947\t10.21081919\t11.18560381\t13.0405095\t12.63050537\t12.28075271\t8.887157917\t10.98167311\t11.10697503\t10.20069523\t10.50192028\t10.71215514\t12.22059826\t11.40980119\t10.29921193\t10.02228522\t10.20111345\t10.70147544\t8.652688571\t10.73582686\t10.59802642\t10.30502944\t10.15381209\t10.92708466\t11.16442513\t10.8438334\t12.74815701\t11.22011517\t10.52200921\t9.268506372\t9.918579617\t10.11228179\t13.21834905\t9.820645381\t10.57072742\t10.73195927\t9.946199692\t10.09127387\t11.41043888\t9.644003704\t9.212649281\t12.50538835\t9.993892741\t11.75190015\t11.25805045\t11.4339889\t12.29500316\t10.91652064\t11.72956311\t11.74664518\t10.03651693\t9.316040132\t10.35883285\t12.00354988\t12.47704263\t10.71443489\t10.62737159\t10.13220636\t8.54273083\t10.27193153\t10.82911329\t10.70459762\t12.0410874\t10.43479019\t11.85550831\t9.884177813\t11.57649029\t10.8692247\t8.96839008\t10.99250487\t12.28805295\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Index(['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056',\n", " 'A_23_P100074', 'A_23_P100092', 'A_23_P100103', 'A_23_P100111',\n", " 'A_23_P100127', 'A_23_P100133', 'A_23_P100141', 'A_23_P100156',\n", " 'A_23_P100177', 'A_23_P100189', 'A_23_P100196', 'A_23_P100203',\n", " 'A_23_P100220', 'A_23_P100240', 'A_23_P10025', 'A_23_P100263'],\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": "91bede2f", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "8dacb025", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:12:12.264759Z", "iopub.status.busy": "2025-03-25T05:12:12.264636Z", "iopub.status.idle": "2025-03-25T05:12:12.266623Z", "shell.execute_reply": "2025-03-25T05:12:12.266297Z" } }, "outputs": [], "source": [ "# Examining the gene identifiers from the output\n", "# The identifiers (like A_23_P100001) appear to be Agilent microarray probe IDs\n", "# These are not standard human gene symbols and will need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "1eb382c7", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "d7bf9f5b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:12:12.267629Z", "iopub.status.busy": "2025-03-25T05:12:12.267521Z", "iopub.status.idle": "2025-03-25T05:12:12.702203Z", "shell.execute_reply": "2025-03-25T05:12:12.701780Z" } }, "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 = GSE107754\n", "Line 6: !Series_title = A novel genomic signature predicting FDG uptake in diverse metastatic tumors\n", "Line 7: !Series_geo_accession = GSE107754\n", "Line 8: !Series_status = Public on Jan 22 2018\n", "Line 9: !Series_submission_date = Dec 06 2017\n", "Line 10: !Series_last_update_date = Jan 23 2019\n", "Line 11: !Series_pubmed_id = 29349517\n", "Line 12: !Series_summary = Purpose: Building a universal genomic signature predicting the intensity of FDG uptake in diverse metastatic tumors may allow us to understand better the biological processes underlying this phenomenon and their requirements of glucose uptake.\n", "Line 13: !Series_summary = Methods: A balanced training set (n=71) of metastatic tumors including some of the most frequent histologies, with matched PET/CT quantification measurements and whole human genome gene expression microarrays, was used to build the signature. Selection of microarray features was carried out exclusively on the basis of their strong association with FDG uptake (as measured by SUVmean35) by means of univariate linear regression. A thorough bioinformatics study of these genes was performed and multivariable models were built by fitting several state of the art regression techniques to the training set for comparison.\n", "Line 14: !Series_summary = Results: The 909 probes with the strongest association with the SUVmean35 (comprising 742 identifiable genes and 62 probes not matched to a symbol) were used to build the signature. Partial Least Squares using 3 components (PLS-3) was the best performing model in the training dataset cross-validation (Root Mean Square Error, RMSE=0.443) and was validated further in an independent validation dataset (n=13) obtaining a performance within the 95% CI of that obtained in the training dataset (RMSE=0.645). Significantly overrepresented biological processes correlating with the SUVmean35 were identified beyond glycolysis, such as ribosome biogenesis and DNA replication (correlating with a higher SUVmean35), and cytoskeleton reorganization and autophagy (correlating with a lower SUVmean35), among others.\n", "Line 15: !Series_summary = Conclusions: PLS-3 is a signature predicting accurately the intensity of FDG uptake in diverse metastatic tumors. FDG-PET might help in the design of specific targeted therapies directed to counteract the identified malignant biological processes more likely activated in a tumor as inferred from the SUVmean35 and also from its variations in response to antineoplastic treatments.\n", "Line 16: !Series_overall_design = Whole human genome microarrays from biopsies of human metastatic tumors (71 patients) with matched SUVmean35 measurements, this submission includes the 71 patients of the training set used to build the genomic signature predicting FDG uptake in diverse metastatic tumors. This dataset is complemented with a validation set comprised of 13 patients.\n", "Line 17: !Series_type = Expression profiling by array\n", "Line 18: !Series_contributor = Ramon,G,Manzano\n", "Line 19: !Series_contributor = Elena,M,Martinez Navarro\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'SPOT_ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'CONTROL_TYPE': ['FALSE', 'FALSE', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GB_ACC': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GENE': [400451.0, 10239.0, 9899.0, 348093.0, 57099.0], 'GENE_SYMBOL': ['FAM174B', 'AP3S2', 'SV2B', 'RBPMS2', 'AVEN'], 'GENE_NAME': ['family with sequence similarity 174, member B', 'adaptor-related protein complex 3, sigma 2 subunit', 'synaptic vesicle glycoprotein 2B', 'RNA binding protein with multiple splicing 2', 'apoptosis, caspase activation inhibitor'], 'UNIGENE_ID': ['Hs.27373', 'Hs.632161', 'Hs.21754', 'Hs.436518', 'Hs.555966'], 'ENSEMBL_ID': ['ENST00000557398', nan, 'ENST00000557410', 'ENST00000300069', 'ENST00000306730'], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': ['ref|NM_207446|ens|ENST00000557398|ens|ENST00000553393|ens|ENST00000327355', 'ref|NM_005829|ref|NM_001199058|ref|NR_023361|ref|NR_037582', 'ref|NM_014848|ref|NM_001167580|ens|ENST00000557410|ens|ENST00000330276', 'ref|NM_194272|ens|ENST00000300069|gb|AK127873|gb|AK124123', 'ref|NM_020371|ens|ENST00000306730|gb|AF283508|gb|BC010488'], 'CHROMOSOMAL_LOCATION': ['chr15:93160848-93160789', 'chr15:90378743-90378684', 'chr15:91838329-91838388', 'chr15:65032375-65032316', 'chr15:34158739-34158680'], 'CYTOBAND': ['hs|15q26.1', 'hs|15q26.1', 'hs|15q26.1', 'hs|15q22.31', 'hs|15q14'], 'DESCRIPTION': ['Homo sapiens family with sequence similarity 174, member B (FAM174B), mRNA [NM_207446]', 'Homo sapiens adaptor-related protein complex 3, sigma 2 subunit (AP3S2), transcript variant 1, mRNA [NM_005829]', 'Homo sapiens synaptic vesicle glycoprotein 2B (SV2B), transcript variant 1, mRNA [NM_014848]', 'Homo sapiens RNA binding protein with multiple splicing 2 (RBPMS2), mRNA [NM_194272]', 'Homo sapiens apoptosis, caspase activation inhibitor (AVEN), mRNA [NM_020371]'], 'GO_ID': ['GO:0016020(membrane)|GO:0016021(integral to membrane)', 'GO:0005794(Golgi apparatus)|GO:0006886(intracellular protein transport)|GO:0008565(protein transporter activity)|GO:0016020(membrane)|GO:0016192(vesicle-mediated transport)|GO:0030117(membrane coat)|GO:0030659(cytoplasmic vesicle membrane)|GO:0031410(cytoplasmic vesicle)', 'GO:0001669(acrosomal vesicle)|GO:0006836(neurotransmitter transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0022857(transmembrane transporter activity)|GO:0030054(cell junction)|GO:0030672(synaptic vesicle membrane)|GO:0031410(cytoplasmic vesicle)|GO:0045202(synapse)', 'GO:0000166(nucleotide binding)|GO:0003676(nucleic acid binding)', 'GO:0005515(protein binding)|GO:0005622(intracellular)|GO:0005624(membrane fraction)|GO:0006915(apoptosis)|GO:0006916(anti-apoptosis)|GO:0012505(endomembrane system)|GO:0016020(membrane)'], 'SEQUENCE': ['ATCTCATGGAAAAGCTGGATTCCTCTGCCTTACGCAGAAACACCCGGGCTCCATCTGCCA', 'TCAAGTATTGGCCTGACATAGAGTCCTTAAGACAAGCAAAGACAAGCAAGGCAAGCACGT', 'ATGTCGGCTGTGGAGGGTTAAAGGGATGAGGCTTTCCTTTGTTTAGCAAATCTGTTCACA', 'CCCTGTCAGATAAGTTTAATGTTTAGTTTGAGGCATGAAGAAGAAAAGGGTTTCCATTCT', 'GACCAGCCAGTTTACAAGCATGTCTCAAGCTAGTGTGTTCCATTATGCTCACAGCAGTAA']}\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": "be8e1d19", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "129a1d1b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:12:12.703492Z", "iopub.status.busy": "2025-03-25T05:12:12.703348Z", "iopub.status.idle": "2025-03-25T05:12:19.290676Z", "shell.execute_reply": "2025-03-25T05:12:19.290320Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping preview:\n", " ID Gene\n", "0 A_23_P100001 FAM174B\n", "1 A_23_P100011 AP3S2\n", "2 A_23_P100022 SV2B\n", "3 A_23_P100056 RBPMS2\n", "4 A_23_P100074 AVEN\n", "Gene expression data shape: (18488, 84)\n", "Gene expression data preview (first 5 genes, first 5 samples):\n", " GSM2878070 GSM2878071 GSM2878072 GSM2878073 GSM2878074\n", "Gene \n", "A1BG 16.675251 18.589095 17.665959 20.260758 17.885257\n", "A1BG-AS1 8.138944 9.361230 8.513994 9.269932 9.537095\n", "A1CF 15.194191 15.252929 16.779446 19.487898 15.190126\n", "A2LD1 9.231020 9.510204 9.701203 8.614287 8.349443\n", "A2M 14.936963 14.432973 15.230273 14.494223 15.789791\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to: ../../output/preprocess/Esophageal_Cancer/gene_data/GSE107754.csv\n" ] } ], "source": [ "# Let's first rerun the gene annotation extraction with the proper function\n", "# We'll try using the library function now that we understand the file structure\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# From the preview, we can see:\n", "# - 'ID' column in the annotation contains probe IDs that match expression data index\n", "# - 'GENE_SYMBOL' column contains the human gene symbols we need to map to\n", "\n", "# 1. Extract the mapping between probe IDs and gene symbols\n", "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')\n", "\n", "# 2. Print a preview of the mapping to verify\n", "print(\"Gene mapping preview:\")\n", "print(gene_mapping.head())\n", "\n", "# 3. Apply the gene mapping to convert probe-level expression to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# 4. Print the dimensions and preview of the gene expression data\n", "print(f\"Gene expression data shape: {gene_data.shape}\")\n", "print(\"Gene expression data preview (first 5 genes, first 5 samples):\")\n", "print(gene_data.iloc[:5, :5])\n", "\n", "# 5. Save the gene expression data to the specified output file\n", "out_gene_dir = os.path.dirname(out_gene_data_file)\n", "os.makedirs(out_gene_dir, 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" ] }, { "cell_type": "markdown", "id": "6a00b120", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "ee7a05e1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:12:19.292028Z", "iopub.status.busy": "2025-03-25T05:12:19.291894Z", "iopub.status.idle": "2025-03-25T05:12:25.902816Z", "shell.execute_reply": "2025-03-25T05:12:25.902279Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (18247, 84)\n", "First few genes with their expression values after normalization:\n", " GSM2878070 GSM2878071 GSM2878072 GSM2878073 GSM2878074 \\\n", "Gene \n", "A1BG 16.675251 18.589095 17.665959 20.260758 17.885257 \n", "A1BG-AS1 8.138944 9.361230 8.513994 9.269932 9.537095 \n", "A1CF 15.194191 15.252929 16.779446 19.487898 15.190126 \n", "A2M 14.936963 14.432973 15.230273 14.494223 15.789791 \n", "A2ML1 10.363561 11.081253 9.592718 10.258384 11.055192 \n", "\n", " GSM2878075 GSM2878076 GSM2878077 GSM2878078 GSM2878079 ... \\\n", "Gene ... \n", "A1BG 23.469022 17.237170 21.554459 18.427697 19.441237 ... \n", "A1BG-AS1 9.026286 8.261324 9.419955 8.954172 8.822408 ... \n", "A1CF 19.816419 15.124037 18.691166 15.129180 15.509904 ... \n", "A2M 15.585660 14.984473 14.855128 15.018056 14.465856 ... \n", "A2ML1 9.692235 9.878473 9.688524 9.687150 9.146547 ... \n", "\n", " GSM2891255 GSM2891256 GSM2891257 GSM2891258 GSM2891259 \\\n", "Gene \n", "A1BG 18.111288 19.849660 16.432489 19.875191 17.694450 \n", "A1BG-AS1 9.421176 8.532598 8.138147 8.611568 8.818973 \n", "A1CF 15.198185 16.659247 15.001932 15.475466 15.330651 \n", "A2M 14.864289 14.535093 13.781049 15.672631 15.351216 \n", "A2ML1 9.459754 10.088872 9.996023 10.241353 13.157317 \n", "\n", " GSM2891260 GSM2891261 GSM2891262 GSM2891263 GSM2891264 \n", "Gene \n", "A1BG 20.178957 19.395664 20.745208 19.434408 18.734236 \n", "A1BG-AS1 7.737613 9.399768 7.667535 9.134555 8.301228 \n", "A1CF 20.560980 15.226321 20.339693 15.531251 15.570731 \n", "A2M 15.361097 14.925851 14.086261 13.401129 14.841858 \n", "A2ML1 9.183133 9.212320 9.285009 10.476271 9.171710 \n", "\n", "[5 rows x 84 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Esophageal_Cancer/gene_data/GSE107754.csv\n", "Raw clinical data shape: (4, 85)\n", "Clinical features:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " GSM2878070 GSM2878071 GSM2878072 GSM2878073 GSM2878074 \\\n", "Esophageal_Cancer 0.0 0.0 0.0 0.0 0.0 \n", "Gender 1.0 0.0 1.0 1.0 0.0 \n", "\n", " GSM2878075 GSM2878076 GSM2878077 GSM2878078 GSM2878079 \\\n", "Esophageal_Cancer 0.0 0.0 0.0 0.0 0.0 \n", "Gender 1.0 0.0 0.0 0.0 0.0 \n", "\n", " ... GSM2891255 GSM2891256 GSM2891257 GSM2891258 \\\n", "Esophageal_Cancer ... 0.0 0.0 0.0 0.0 \n", "Gender ... 1.0 1.0 1.0 1.0 \n", "\n", " GSM2891259 GSM2891260 GSM2891261 GSM2891262 GSM2891263 \\\n", "Esophageal_Cancer 0.0 0.0 0.0 0.0 0.0 \n", "Gender 0.0 1.0 0.0 1.0 1.0 \n", "\n", " GSM2891264 \n", "Esophageal_Cancer 0.0 \n", "Gender 1.0 \n", "\n", "[2 rows x 84 columns]\n", "Clinical features saved to ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE107754.csv\n", "Linked data shape: (84, 18249)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Esophageal_Cancer Gender A1BG A1BG-AS1 A1CF\n", "GSM2878070 0.0 1.0 16.675251 8.138944 15.194191\n", "GSM2878071 0.0 0.0 18.589095 9.361230 15.252929\n", "GSM2878072 0.0 1.0 17.665959 8.513994 16.779446\n", "GSM2878073 0.0 1.0 20.260758 9.269932 19.487898\n", "GSM2878074 0.0 0.0 17.885257 9.537095 15.190126\n", "Missing values before handling:\n", " Trait (Esophageal_Cancer) missing: 0 out of 84\n", " Gender missing: 0 out of 84\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: (84, 18249)\n", "For the feature 'Esophageal_Cancer', the least common label is '1.0' with 4 occurrences. This represents 4.76% of the dataset.\n", "The distribution of the feature 'Esophageal_Cancer' in this dataset is severely biased.\n", "\n", "For the feature 'Gender', the least common label is '1.0' with 35 occurrences. This represents 41.67% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "Data was determined to be unusable or empty and was not saved\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 }