{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "855a6e05", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:40.295751Z", "iopub.status.busy": "2025-03-25T05:13:40.295531Z", "iopub.status.idle": "2025-03-25T05:13:40.463227Z", "shell.execute_reply": "2025-03-25T05:13:40.462892Z" } }, "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 = \"GSE66258\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Esophageal_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Esophageal_Cancer/GSE66258\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Esophageal_Cancer/GSE66258.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Esophageal_Cancer/gene_data/GSE66258.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Esophageal_Cancer/clinical_data/GSE66258.csv\"\n", "json_path = \"../../output/preprocess/Esophageal_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "58d4f02c", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "9f0fe125", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:40.464713Z", "iopub.status.busy": "2025-03-25T05:13:40.464561Z", "iopub.status.idle": "2025-03-25T05:13:40.628568Z", "shell.execute_reply": "2025-03-25T05:13:40.628231Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Comprehensive Analysis of Recurrence-Associated Small Non-Coding RNAs in Esophageal Cancer [clinical study, Illumina]\"\n", "!Series_summary\t\"Targeted cancer therapy for squamous cell carcinoma (SCC) has made little progress largely due to a lack of knowledge of the driving genomic alterations. Small non-coding RNAs (sncRNAs) as a potential biomarker and therapeutic target to SCC remain a challenge. We analyzed sncRNAs microarray in 108 fresh frozen specimens of esophageal squamous cell carcinoma (ESCC) as discovery set and assessed associations between sncRNAs and recurrence-free survival. SncRNA signature identified was externally validated in two independent cohorts. We investigated the functional consequences of sncRNA identified and its integrative analysis of complex cancer genomics. We identified 3 recurrence-associated sncRNAs (miR-223, miR-1269a and nc886) from discovery set and proved risk prediction model externally in high and low volume centers. We uncovered through in vitro experiment that nc886 was down-regulated by hypermethylation of its promoter region and influences splicing of pre-mRNAs with minor introns by regulating expression of minor spliceosomal small nuclear RNAs (snRNAs) such as RNU4atac. Integrative analysis from lung SCC data in The Cancer Genome Atlas revealed that patients with lower expression of nc886 had more genetic alterations of TP53, DNA damage response and cell cycle genes. nc886 inhibits minor splicing to suppress expression of certain oncogenes such as PARP1 and E2F family containing minor introns. We present risk prediction model with sncRNAs for ESCC. Among them, nc886 may contribute to complete minor splicing via regulation of minor spliceosomal snRNAs supporting the notion that aberrant alteration in minor splicing might be a key driver of ESCC.\"\n", "!Series_overall_design\t\"Clinical study\"\n", "!Series_overall_design\t\"ESCC tumor samples.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: esophageal squamous cell carcinoma (ESCC) tumor'], 1: ['sample id: 1', 'sample id: 2', 'sample id: 3', 'sample id: 4', 'sample id: 5', 'sample id: 6', 'sample id: 7', 'sample id: 8', 'sample id: 9', 'sample id: 10', 'sample id: 11', 'sample id: 12', 'sample id: 13', 'sample id: 14', 'sample id: 15', 'sample id: 16', 'sample id: 17', 'sample id: 18', 'sample id: 19', 'sample id: 20', 'sample id: 21', 'sample id: 22', 'sample id: 23', 'sample id: 24', 'sample id: 25', 'sample id: 26', 'sample id: 27', 'sample id: 28', 'sample id: 29', 'sample id: 30']}\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": "cf68a890", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "9879afc9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:40.629820Z", "iopub.status.busy": "2025-03-25T05:13:40.629706Z", "iopub.status.idle": "2025-03-25T05:13:40.636559Z", "shell.execute_reply": "2025-03-25T05:13:40.636283Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Features Preview:\n", "{0: [1.0], 1: [nan]}\n", "Clinical data saved to ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE66258.csv\n" ] } ], "source": [ "# 1. Determine if gene expression data is available based on background information\n", "# This dataset is about small non-coding RNAs (sncRNAs) microarray in esophageal squamous cell carcinoma\n", "# Since it focuses on sncRNAs rather than gene expression, we'll set is_gene_available to False\n", "is_gene_available = False\n", "\n", "# 2. Check for trait (Esophageal Cancer), age, and gender data availability\n", "# Looking at the sample characteristics dictionary, we have:\n", "# Key 0: 'tissue: esophageal squamous cell carcinoma (ESCC) tumor'\n", "# Key 1: Sample IDs\n", "\n", "# 2.1 Data Availability\n", "# For trait data: Key 0 indicates these are all ESCC tumor samples\n", "trait_row = 0 # All samples are ESCC\n", "\n", "# Age and gender are not explicitly provided in the sample characteristics\n", "age_row = None # Age data not available\n", "gender_row = None # Gender data not available\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"Convert trait values to binary (1 for cancer, 0 for control)\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # This dataset contains only ESCC tumor samples, so all are cases\n", " if \"esophageal squamous cell carcinoma\" in value.lower() or \"escc\" in value.lower():\n", " return 1\n", " else:\n", " return None # For any unexpected values\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age values to continuous integers\"\"\"\n", " # Not used since age data is not available\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender values to binary (0 for female, 1 for male)\"\"\"\n", " # Not used since gender data is not available\n", " return None\n", "\n", "# 3. Save initial metadata about dataset usability\n", "is_trait_available = trait_row is not None\n", "initial_validation = 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", " # Assuming clinical_data is available from previous step\n", " clinical_data = pd.DataFrame({\n", " 0: ['tissue: esophageal squamous cell carcinoma (ESCC) tumor'] * 30,\n", " 1: [f'sample id: {i+1}' for i in range(30)]\n", " })\n", " \n", " # Extract clinical features\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", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical Features Preview:\")\n", " print(preview)\n", " \n", " # Save clinical data to CSV\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=True)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "07860351", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "27ff5664", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:40.637584Z", "iopub.status.busy": "2025-03-25T05:13:40.637474Z", "iopub.status.idle": "2025-03-25T05:13:40.944609Z", "shell.execute_reply": "2025-03-25T05:13:40.944222Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 67\n", "Header line: \"ID_REF\"\t\"GSM1618105\"\t\"GSM1618106\"\t\"GSM1618107\"\t\"GSM1618108\"\t\"GSM1618109\"\t\"GSM1618110\"\t\"GSM1618111\"\t\"GSM1618112\"\t\"GSM1618113\"\t\"GSM1618114\"\t\"GSM1618115\"\t\"GSM1618116\"\t\"GSM1618117\"\t\"GSM1618118\"\t\"GSM1618119\"\t\"GSM1618120\"\t\"GSM1618121\"\t\"GSM1618122\"\t\"GSM1618123\"\t\"GSM1618124\"\t\"GSM1618125\"\t\"GSM1618126\"\t\"GSM1618127\"\t\"GSM1618128\"\t\"GSM1618129\"\t\"GSM1618130\"\t\"GSM1618131\"\t\"GSM1618132\"\t\"GSM1618133\"\t\"GSM1618134\"\t\"GSM1618135\"\t\"GSM1618136\"\t\"GSM1618137\"\t\"GSM1618138\"\t\"GSM1618139\"\t\"GSM1618140\"\t\"GSM1618141\"\t\"GSM1618142\"\t\"GSM1618143\"\t\"GSM1618144\"\t\"GSM1618145\"\t\"GSM1618146\"\t\"GSM1618147\"\t\"GSM1618148\"\t\"GSM1618149\"\t\"GSM1618150\"\t\"GSM1618151\"\t\"GSM1618152\"\t\"GSM1618153\"\t\"GSM1618154\"\t\"GSM1618155\"\t\"GSM1618156\"\t\"GSM1618157\"\t\"GSM1618158\"\t\"GSM1618159\"\t\"GSM1618160\"\t\"GSM1618161\"\t\"GSM1618162\"\t\"GSM1618163\"\t\"GSM1618164\"\t\"GSM1618165\"\t\"GSM1618166\"\t\"GSM1618167\"\t\"GSM1618168\"\t\"GSM1618169\"\t\"GSM1618170\"\t\"GSM1618171\"\t\"GSM1618172\"\t\"GSM1618173\"\t\"GSM1618174\"\t\"GSM1618175\"\t\"GSM1618176\"\t\"GSM1618177\"\t\"GSM1618178\"\t\"GSM1618179\"\t\"GSM1618180\"\t\"GSM1618181\"\t\"GSM1618182\"\t\"GSM1618183\"\t\"GSM1618184\"\t\"GSM1618185\"\t\"GSM1618186\"\t\"GSM1618187\"\t\"GSM1618188\"\t\"GSM1618189\"\t\"GSM1618190\"\t\"GSM1618191\"\t\"GSM1618192\"\t\"GSM1618193\"\t\"GSM1618194\"\t\"GSM1618195\"\t\"GSM1618196\"\t\"GSM1618197\"\t\"GSM1618198\"\t\"GSM1618199\"\t\"GSM1618200\"\t\"GSM1618201\"\t\"GSM1618202\"\t\"GSM1618203\"\t\"GSM1618204\"\t\"GSM1618205\"\t\"GSM1618206\"\t\"GSM1618207\"\t\"GSM1618208\"\t\"GSM1618209\"\t\"GSM1618210\"\t\"GSM1618211\"\t\"GSM1618212\"\n", "First data line: \"ILMN_1343291\"\t14.214\t13.769\t13.896\t14.047\t13.682\t12.003\t13.543\t14.246\t13.975\t13.827\t14.173\t13.506\t13.806\t13.896\t14.088\t13.817\t13.706\t14.291\t14.121\t13.817\t14.14\t14.291\t14.2\t14.23\t14.246\t14.23\t14.11\t13.817\t14.076\t13.939\t14.11\t13.884\t13.519\t13.75\t14.173\t13.939\t14.088\t13.561\t13.633\t13.375\t13.75\t14.009\t14.291\t14.265\t13.999\t14.2\t13.706\t13.513\t13.871\t14.318\t14.123\t12.664\t13.488\t13.112\t14.101\t6.428\t13.127\t13.603\t13.509\t14.242\t14.32\t14.389\t14.389\t14.32\t12.744\t14.265\t14.291\t14.32\t13.873\t13.939\t13.175\t14.019\t14.149\t13.999\t13.859\t13.975\t13.796\t14.047\t14.131\t14.149\t13.362\t13.414\t13.975\t13.796\t13.838\t13.202\t13.656\t14.121\t14.066\t13.285\t13.698\t14.149\t14.214\t13.838\t14.265\t13.656\t14.199\t13.487\t10.351\t13.702\t6.513\t14.172\t13.389\t14.32\t13.026\t13.289\t13.947\t14.32\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n", " 'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n", " 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n", " 'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n", " 'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\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": "886c1c94", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "d799eee5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:40.945892Z", "iopub.status.busy": "2025-03-25T05:13:40.945765Z", "iopub.status.idle": "2025-03-25T05:13:40.947752Z", "shell.execute_reply": "2025-03-25T05:13:40.947452Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers in the gene expression data\n", "# The identifiers start with \"ILMN_\" which indicates they are Illumina probe IDs\n", "# These are not standard human gene symbols and will need to be mapped\n", "# Illumina probe IDs are specific to Illumina microarray platforms and need to be\n", "# converted to gene symbols for cross-platform analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "28a5937d", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "a4224010", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:40.949027Z", "iopub.status.busy": "2025-03-25T05:13:40.948912Z", "iopub.status.idle": "2025-03-25T05:13:41.863499Z", "shell.execute_reply": "2025-03-25T05:13:41.863117Z" } }, "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 = GSE66258\n", "Line 6: !Series_title = Comprehensive Analysis of Recurrence-Associated Small Non-Coding RNAs in Esophageal Cancer [clinical study, Illumina]\n", "Line 7: !Series_geo_accession = GSE66258\n", "Line 8: !Series_status = Public on Jun 30 2016\n", "Line 9: !Series_submission_date = Feb 24 2015\n", "Line 10: !Series_last_update_date = Aug 13 2018\n", "Line 11: !Series_pubmed_id = 27507904\n", "Line 12: !Series_summary = Targeted cancer therapy for squamous cell carcinoma (SCC) has made little progress largely due to a lack of knowledge of the driving genomic alterations. Small non-coding RNAs (sncRNAs) as a potential biomarker and therapeutic target to SCC remain a challenge. We analyzed sncRNAs microarray in 108 fresh frozen specimens of esophageal squamous cell carcinoma (ESCC) as discovery set and assessed associations between sncRNAs and recurrence-free survival. SncRNA signature identified was externally validated in two independent cohorts. We investigated the functional consequences of sncRNA identified and its integrative analysis of complex cancer genomics. We identified 3 recurrence-associated sncRNAs (miR-223, miR-1269a and nc886) from discovery set and proved risk prediction model externally in high and low volume centers. We uncovered through in vitro experiment that nc886 was down-regulated by hypermethylation of its promoter region and influences splicing of pre-mRNAs with minor introns by regulating expression of minor spliceosomal small nuclear RNAs (snRNAs) such as RNU4atac. Integrative analysis from lung SCC data in The Cancer Genome Atlas revealed that patients with lower expression of nc886 had more genetic alterations of TP53, DNA damage response and cell cycle genes. nc886 inhibits minor splicing to suppress expression of certain oncogenes such as PARP1 and E2F family containing minor introns. We present risk prediction model with sncRNAs for ESCC. Among them, nc886 may contribute to complete minor splicing via regulation of minor spliceosomal snRNAs supporting the notion that aberrant alteration in minor splicing might be a key driver of ESCC.\n", "Line 13: !Series_overall_design = Clinical study\n", "Line 14: !Series_overall_design = ESCC tumor samples.\n", "Line 15: !Series_type = Expression profiling by array\n", "Line 16: !Series_contributor = Hyun-Sung,,Lee\n", "Line 17: !Series_contributor = Ju-Seog,,Lee\n", "Line 18: !Series_sample_id = GSM1618105\n", "Line 19: !Series_sample_id = GSM1618106\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Species': [nan, nan, nan, nan, nan], 'Source': [nan, nan, nan, nan, nan], 'Search_Key': [nan, nan, nan, nan, nan], 'Transcript': [nan, nan, nan, nan, nan], 'ILMN_Gene': [nan, nan, nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan, nan, nan], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Unigene_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': [nan, nan, nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB'], 'Protein_Product': [nan, nan, nan, nan, 'thrB'], 'Probe_Id': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5090180, 6510136, 7560739, 1450438, 1240647], 'Probe_Type': [nan, nan, nan, nan, nan], 'Probe_Start': [nan, nan, nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT', 'TCTGTCACTGTCAGGAAAGTGGTAAAACTGCAACTCAATTACTGCAATGC', 'CTTGTGCCTGAGCTGTCAAAAGTAGAGCACGTCGCCGAGATGAAGGGCGC'], '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': [nan, nan, nan, nan, nan], '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': [nan, nan, nan, nan, nan]}\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": "c38c8422", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "fe6076ee", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:41.864871Z", "iopub.status.busy": "2025-03-25T05:13:41.864757Z", "iopub.status.idle": "2025-03-25T05:13:42.045382Z", "shell.execute_reply": "2025-03-25T05:13:42.045014Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "First 10 genes after mapping:\n", "Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2',\n", " 'A4GALT', 'A4GNT'],\n", " dtype='object', name='Gene')\n", "\n", "Gene expression data preview:\n", "{'GSM1618105': [13.117999999999999, 19.637999999999998, 19.191, 25.614, 6.718], 'GSM1618106': [13.093, 19.525, 19.544, 25.91, 7.365], 'GSM1618107': [13.135, 19.463, 19.384, 25.54, 7.696], 'GSM1618108': [12.937000000000001, 19.386, 19.560000000000002, 25.838, 6.733], 'GSM1618109': [13.074, 19.311, 19.516, 25.313000000000002, 6.61], 'GSM1618110': [15.734000000000002, 20.281, 20.258000000000003, 26.335, 6.445], 'GSM1618111': [13.414, 20.435000000000002, 19.477, 25.869, 6.401], 'GSM1618112': [13.55, 19.451, 19.393, 25.701999999999998, 7.105], 'GSM1618113': [13.66, 19.472, 19.541, 26.006, 6.604], 'GSM1618114': [12.947, 19.445999999999998, 19.651, 25.905, 7.724], 'GSM1618115': [13.169, 19.484, 19.418, 25.808, 7.036], 'GSM1618116': [13.131, 19.275, 19.697000000000003, 26.349, 6.842], 'GSM1618117': [13.463999999999999, 19.512999999999998, 19.574, 26.022000000000002, 8.286], 'GSM1618118': [13.216, 19.597, 19.493, 25.791, 6.885], 'GSM1618119': [13.325, 19.614, 19.494999999999997, 25.601, 6.863], 'GSM1618120': [13.117, 19.358, 19.374, 25.871000000000002, 6.839], 'GSM1618121': [12.966000000000001, 19.469, 19.281, 25.777, 6.55], 'GSM1618122': [13.16, 19.583, 19.4, 25.607, 7.603], 'GSM1618123': [13.02, 19.472, 19.596, 25.907, 7.164], 'GSM1618124': [13.017, 19.368, 19.249, 25.826, 7.151], 'GSM1618125': [13.1, 19.59, 19.505, 25.857, 6.828], 'GSM1618126': [12.945, 19.384, 19.787, 25.561999999999998, 7.397], 'GSM1618127': [12.911, 19.515, 19.384, 25.871000000000002, 6.818], 'GSM1618128': [12.947, 19.431, 19.688000000000002, 25.563, 7.006], 'GSM1618129': [12.897, 19.746000000000002, 19.418, 25.79, 7.132], 'GSM1618130': [13.238, 19.38, 19.376, 25.607, 7.011], 'GSM1618131': [13.215, 19.496, 19.563, 25.584, 7.317], 'GSM1618132': [13.227, 19.595, 19.399, 25.628999999999998, 7.967], 'GSM1618133': [13.313, 19.48, 19.902, 25.971, 7.636], 'GSM1618134': [13.247, 19.557, 19.575, 26.011, 7.228], 'GSM1618135': [13.15, 19.417, 19.353, 25.732, 6.711], 'GSM1618136': [13.149000000000001, 19.517, 19.37, 25.724, 7.126], 'GSM1618137': [13.165, 19.496000000000002, 19.740000000000002, 26.143, 6.637], 'GSM1618138': [13.184999999999999, 19.526, 19.405, 25.841, 6.592], 'GSM1618139': [13.193, 19.509, 19.262999999999998, 25.809, 6.891], 'GSM1618140': [13.251000000000001, 19.28, 19.107, 25.835, 6.746], 'GSM1618141': [13.256, 19.542, 19.369, 25.727, 6.896], 'GSM1618142': [13.261, 20.233, 19.27, 25.755000000000003, 7.044], 'GSM1618143': [13.248999999999999, 19.715, 19.52, 26.058, 6.733], 'GSM1618144': [13.161000000000001, 19.635, 20.626, 25.729, 7.099], 'GSM1618145': [13.144, 19.286, 19.55, 25.759, 6.432], 'GSM1618146': [13.133, 19.517, 19.303, 25.916, 6.747], 'GSM1618147': [13.467, 19.517, 19.323999999999998, 26.134999999999998, 7.059], 'GSM1618148': [13.2, 19.41, 19.716, 25.903, 7.489], 'GSM1618149': [13.285, 19.698999999999998, 19.383, 25.836, 7.605], 'GSM1618150': [13.186, 19.633, 19.198, 25.769, 7.138], 'GSM1618151': [13.356, 19.253, 19.66, 26.003999999999998, 6.651], 'GSM1618152': [13.711, 19.951999999999998, 19.358, 25.786, 6.547], 'GSM1618153': [12.989, 19.307, 19.424, 25.686, 7.453], 'GSM1618154': [13.128, 19.256, 19.406, 25.907, 6.931], 'GSM1618155': [13.219000000000001, 19.479, 19.605, 25.792, 6.661], 'GSM1618156': [15.003, 21.85, 20.048000000000002, 26.899, 6.437], 'GSM1618157': [13.132, 19.617, 19.387999999999998, 26.405, 7.528], 'GSM1618158': [13.73, 19.371, 19.289, 25.872, 6.795], 'GSM1618159': [13.155, 19.444, 19.658, 25.83, 7.26], 'GSM1618160': [13.293, 23.796, 20.0, 29.368000000000002, 6.463], 'GSM1618161': [13.794, 19.772, 19.68, 26.235999999999997, 6.803], 'GSM1618162': [13.574, 19.675, 20.416, 25.663, 8.016], 'GSM1618163': [13.831, 19.632, 19.314999999999998, 25.704, 6.395], 'GSM1618164': [13.205, 19.301, 19.423, 25.762999999999998, 6.463], 'GSM1618165': [13.177, 19.473, 19.816, 25.546, 6.928], 'GSM1618166': [13.315000000000001, 19.578, 19.613, 25.773, 6.928], 'GSM1618167': [13.343, 19.603, 19.618, 25.641, 7.279], 'GSM1618168': [13.181000000000001, 19.506, 19.56, 25.721, 8.209], 'GSM1618169': [15.019, 19.557000000000002, 19.725, 26.601, 6.421], 'GSM1618170': [13.043, 19.404, 19.415, 25.595, 6.811], 'GSM1618171': [13.327, 19.473, 19.259, 25.618, 7.23], 'GSM1618172': [13.122, 19.536, 19.366, 25.778, 6.952], 'GSM1618173': [13.894, 19.573, 19.225, 25.7, 6.653], 'GSM1618174': [13.346, 19.361, 19.356, 25.829, 6.863], 'GSM1618175': [13.197, 19.418, 19.456, 25.65, 7.272], 'GSM1618176': [13.285, 19.616, 19.369, 25.721, 6.809], 'GSM1618177': [13.135000000000002, 19.592, 19.416, 26.003999999999998, 7.645], 'GSM1618178': [13.676, 19.674, 19.544, 25.834, 6.955], 'GSM1618179': [13.338999999999999, 19.406, 19.454, 25.959, 6.893], 'GSM1618180': [13.415, 19.448, 19.646, 25.857, 7.521], 'GSM1618181': [13.18, 19.334, 19.246000000000002, 25.6, 6.539], 'GSM1618182': [13.308, 19.61, 19.304000000000002, 25.987000000000002, 6.631], 'GSM1618183': [13.221, 19.487000000000002, 19.323, 25.706, 7.653], 'GSM1618184': [13.086, 19.264, 19.458, 25.672, 7.092], 'GSM1618185': [13.704, 19.421999999999997, 19.664, 25.662, 7.765], 'GSM1618186': [13.524000000000001, 19.669, 19.353, 25.724, 6.643], 'GSM1618187': [13.443, 19.585, 19.356, 25.725, 6.832], 'GSM1618188': [13.216000000000001, 19.518, 19.323, 25.615, 8.286], 'GSM1618189': [13.619, 19.767, 19.627, 25.844, 6.494], 'GSM1618190': [13.558, 19.832, 19.511, 26.146, 6.669], 'GSM1618191': [14.736, 19.471, 19.497, 26.186, 6.955], 'GSM1618192': [13.341000000000001, 19.381999999999998, 19.316, 25.666, 8.011], 'GSM1618193': [13.233, 19.535, 19.526, 25.647, 6.861], 'GSM1618194': [14.234, 19.525, 20.104, 25.613, 6.598], 'GSM1618195': [13.306999999999999, 19.716, 19.485, 25.594, 7.109], 'GSM1618196': [13.185, 19.527, 19.438000000000002, 25.792, 7.608], 'GSM1618197': [13.321000000000002, 19.69, 19.435000000000002, 25.619999999999997, 6.991], 'GSM1618198': [13.07, 19.564, 19.281, 25.931, 6.626], 'GSM1618199': [13.182, 19.476, 19.426, 25.567, 7.097], 'GSM1618200': [13.522, 19.949, 19.428, 25.674, 6.909], 'GSM1618201': [13.102, 19.431, 19.336, 25.906, 6.756], 'GSM1618202': [13.185, 19.408, 19.524, 25.657, 7.656], 'GSM1618203': [14.084, 24.728, 19.489, 26.09, 6.421], 'GSM1618204': [15.442, 19.872, 21.82, 26.362000000000002, 6.561], 'GSM1618205': [12.912, 20.447000000000003, 19.713, 26.216, 7.629], 'GSM1618206': [13.213000000000001, 19.445, 19.603, 25.875999999999998, 6.994], 'GSM1618207': [13.847999999999999, 20.12, 19.807, 25.953, 7.575], 'GSM1618208': [13.219, 19.652, 19.6, 25.983999999999998, 7.517], 'GSM1618209': [13.843, 19.755, 20.938, 25.719, 6.517], 'GSM1618210': [13.176, 19.633, 19.256, 25.884, 6.579], 'GSM1618211': [13.225999999999999, 19.673, 19.65, 25.734, 6.817], 'GSM1618212': [13.251, 19.645, 19.173000000000002, 25.826, 6.627]}\n" ] } ], "source": [ "# 1. Identify the columns in gene annotation that correspond to probe ID and gene symbol\n", "# Based on the gene annotation preview, 'ID' contains Illumina probe IDs (ILMN_*) and 'Symbol' contains gene symbols\n", "\n", "# 2. Create a gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n", "\n", "# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# 4. Show the first few genes to verify mapping\n", "print(\"\\nFirst 10 genes after mapping:\")\n", "print(gene_data.index[:10])\n", "\n", "# 5. Preview some gene expression values\n", "print(\"\\nGene expression data preview:\")\n", "print(preview_df(gene_data))\n" ] }, { "cell_type": "markdown", "id": "3688febe", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "d7e484d3", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:13:42.046787Z", "iopub.status.busy": "2025-03-25T05:13:42.046665Z", "iopub.status.idle": "2025-03-25T05:13:49.883480Z", "shell.execute_reply": "2025-03-25T05:13:49.883022Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (20259, 108)\n", "First few genes with their expression values after normalization:\n", " GSM1618105 GSM1618106 GSM1618107 GSM1618108 GSM1618109 \\\n", "Gene \n", "A1BG 13.118 13.093 13.135 12.937 13.074 \n", "A1BG-AS1 6.474 6.624 6.533 6.457 6.475 \n", "A1CF 19.638 19.525 19.463 19.386 19.311 \n", "A2M 11.702 10.902 9.623 9.762 10.640 \n", "A2ML1 6.394 6.540 6.461 7.809 7.221 \n", "\n", " GSM1618110 GSM1618111 GSM1618112 GSM1618113 GSM1618114 ... \\\n", "Gene ... \n", "A1BG 15.734 13.414 13.550 13.660 12.947 ... \n", "A1BG-AS1 6.433 6.564 6.397 6.637 6.387 ... \n", "A1CF 20.281 20.435 19.451 19.472 19.446 ... \n", "A2M 9.601 9.410 10.049 8.556 9.729 ... \n", "A2ML1 6.699 7.991 7.482 7.434 7.918 ... \n", "\n", " GSM1618203 GSM1618204 GSM1618205 GSM1618206 GSM1618207 \\\n", "Gene \n", "A1BG 14.084 15.442 12.912 13.213 13.848 \n", "A1BG-AS1 6.648 7.317 6.522 6.428 6.513 \n", "A1CF 24.728 19.872 20.447 19.445 20.120 \n", "A2M 9.440 7.235 7.006 9.211 8.566 \n", "A2ML1 7.659 6.412 6.416 8.816 7.073 \n", "\n", " GSM1618208 GSM1618209 GSM1618210 GSM1618211 GSM1618212 \n", "Gene \n", "A1BG 13.219 13.843 13.176 13.226 13.251 \n", "A1BG-AS1 6.398 6.444 6.455 6.529 6.506 \n", "A1CF 19.652 19.755 19.633 19.673 19.645 \n", "A2M 9.390 9.210 9.626 9.540 9.821 \n", "A2ML1 6.361 6.374 8.099 6.893 6.446 \n", "\n", "[5 rows x 108 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Esophageal_Cancer/gene_data/GSE66258.csv\n", "Raw clinical data shape: (2, 109)\n", "Clinical features:\n", " GSM1618105 GSM1618106 GSM1618107 GSM1618108 GSM1618109 \\\n", "Esophageal_Cancer 1.0 1.0 1.0 1.0 1.0 \n", "\n", " GSM1618110 GSM1618111 GSM1618112 GSM1618113 GSM1618114 \\\n", "Esophageal_Cancer 1.0 1.0 1.0 1.0 1.0 \n", "\n", " ... GSM1618203 GSM1618204 GSM1618205 GSM1618206 \\\n", "Esophageal_Cancer ... 1.0 1.0 1.0 1.0 \n", "\n", " GSM1618207 GSM1618208 GSM1618209 GSM1618210 GSM1618211 \\\n", "Esophageal_Cancer 1.0 1.0 1.0 1.0 1.0 \n", "\n", " GSM1618212 \n", "Esophageal_Cancer 1.0 \n", "\n", "[1 rows x 108 columns]\n", "Clinical features saved to ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE66258.csv\n", "Linked data shape: (108, 20260)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Esophageal_Cancer A1BG A1BG-AS1 A1CF A2M\n", "GSM1618105 1.0 13.118 6.474 19.638 11.702\n", "GSM1618106 1.0 13.093 6.624 19.525 10.902\n", "GSM1618107 1.0 13.135 6.533 19.463 9.623\n", "GSM1618108 1.0 12.937 6.457 19.386 9.762\n", "GSM1618109 1.0 13.074 6.475 19.311 10.640\n", "Missing values before handling:\n", " Trait (Esophageal_Cancer) missing: 0 out of 108\n", " Genes with >20% missing: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Samples with >5% missing genes: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (108, 20260)\n", "Quartiles for 'Esophageal_Cancer':\n", " 25%: 1.0\n", " 50% (Median): 1.0\n", " 75%: 1.0\n", "Min: 1.0\n", "Max: 1.0\n", "The distribution of the feature 'Esophageal_Cancer' in this dataset is severely biased.\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 }