{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "879df130", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:40.573778Z", "iopub.status.busy": "2025-03-25T04:01:40.573676Z", "iopub.status.idle": "2025-03-25T04:01:40.750517Z", "shell.execute_reply": "2025-03-25T04:01:40.750170Z" } }, "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 = \"Stomach_Cancer\"\n", "cohort = \"GSE130823\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Stomach_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Stomach_Cancer/GSE130823\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Stomach_Cancer/GSE130823.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Stomach_Cancer/gene_data/GSE130823.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Stomach_Cancer/clinical_data/GSE130823.csv\"\n", "json_path = \"../../output/preprocess/Stomach_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "367bd76e", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "0ceab183", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:40.751958Z", "iopub.status.busy": "2025-03-25T04:01:40.751812Z", "iopub.status.idle": "2025-03-25T04:01:41.039899Z", "shell.execute_reply": "2025-03-25T04:01:41.039534Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Files in the cohort directory:\n", "['GSE130823_family.soft.gz', 'GSE130823_series_matrix.txt.gz']\n", "Identified SOFT files: ['GSE130823_family.soft.gz']\n", "Identified matrix files: ['GSE130823_series_matrix.txt.gz']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Background Information:\n", "!Series_title\t\"Dissecting Expression Profiles of Gastric Precancerous Lesions and Early Gastric Cancer to Explore Crucial Molecules in Intestinal-type Gastric Cancer Tumorigenesis\"\n", "!Series_summary\t\"To investigate the changes in molecular expression, biological processes, stemness, immune microenvironment, tumor hallmark activities and co-expression relationships during intestinal-type gastric cancer carcinogenesis and to excavate the prognostic information contained in the carcinogenesis process. RNA expression profiles of ninety-four gastroscope biopsy samples with different stages of precancerous lesions or early gastric cancers and their paired controls were detected by Agilent Microarray.\"\n", "!Series_overall_design\t\"RNA expression profiles of ninety-four gastroscope biopsy samples with different stages of precancerous lesions or early gastric cancers and their paired controls were detected by Agilent Microarray.\"\n", "\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: gastric'], 1: ['gender: Female', 'gender: Male'], 2: ['age: 74', 'age: 61', 'age: 54', 'age: 60', 'age: 63', 'age: 58', 'age: 44', 'age: 56', 'age: 59', 'age: 55', 'age: 46', 'age: 71', 'age: 77', 'age: 62', 'age: 65', 'age: 69', 'age: 66', 'age: 73', 'age: 57', 'age: 78', 'age: 38', 'age: 68', 'age: 42', 'age: 43']}\n" ] } ], "source": [ "# 1. Let's first list the directory contents to understand what files are available\n", "import os\n", "\n", "print(\"Files in the cohort directory:\")\n", "files = os.listdir(in_cohort_dir)\n", "print(files)\n", "\n", "# Adapt file identification to handle different naming patterns\n", "soft_files = [f for f in files if 'soft' in f.lower() or '.soft' in f.lower() or '_soft' in f.lower()]\n", "matrix_files = [f for f in files if 'matrix' in f.lower() or '.matrix' in f.lower() or '_matrix' in f.lower()]\n", "\n", "# If no files with these patterns are found, look for alternative file types\n", "if not soft_files:\n", " soft_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n", "if not matrix_files:\n", " matrix_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n", "\n", "print(\"Identified SOFT files:\", soft_files)\n", "print(\"Identified matrix files:\", matrix_files)\n", "\n", "# Use the first files found, if any\n", "if len(soft_files) > 0 and len(matrix_files) > 0:\n", " soft_file = os.path.join(in_cohort_dir, soft_files[0])\n", " matrix_file = os.path.join(in_cohort_dir, matrix_files[0])\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(\"\\nBackground Information:\")\n", " print(background_info)\n", " print(\"\\nSample Characteristics Dictionary:\")\n", " print(sample_characteristics_dict)\n", "else:\n", " print(\"No appropriate files found in the directory.\")\n" ] }, { "cell_type": "markdown", "id": "7f903d72", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "99cfae34", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:41.041321Z", "iopub.status.busy": "2025-03-25T04:01:41.041054Z", "iopub.status.idle": "2025-03-25T04:01:41.045294Z", "shell.execute_reply": "2025-03-25T04:01:41.044997Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical feature extraction skipped because trait data is not available.\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains RNA expression profiles \n", "# detected by Agilent Microarray, which indicates gene expression data is available\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# From the sample characteristics dictionary:\n", "# - trait: Not explicitly available as a separate category\n", "# - age: Available at key 2\n", "# - gender: Available at key 1\n", "\n", "# Looking at the background information, this study focuses on gastric cancer,\n", "# but we don't have a clear indicator of cancer status in the provided characteristics.\n", "# Therefore, we cannot determine trait status at this stage.\n", "trait_row = None # Cannot determine cancer status from available sample characteristics\n", "age_row = 2 # Age information is at key 2\n", "gender_row = 1 # Gender information is at key 1\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"\n", " Convert cancer status to binary trait values.\n", " Not implemented as we cannot determine trait status.\n", " \"\"\"\n", " return None # Cannot determine trait status\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age string to numerical value.\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " try:\n", " return int(value)\n", " except:\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender string to binary (0 for female, 1 for male).\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " \n", " if 'female' in value:\n", " return 0\n", " elif 'male' in value:\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Since trait_row is None, set is_trait_available to False\n", "is_trait_available = False\n", "\n", "# Validate and save cohort info\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. Skip clinical feature extraction since trait data is not available (trait_row is None)\n", "print(\"Clinical feature extraction skipped because trait data is not available.\")\n" ] }, { "cell_type": "markdown", "id": "05d72dc8", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "2de0bb50", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:41.046443Z", "iopub.status.busy": "2025-03-25T04:01:41.046337Z", "iopub.status.idle": "2025-03-25T04:01:41.609954Z", "shell.execute_reply": "2025-03-25T04:01:41.609482Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 20 gene/probe identifiers:\n", "Index(['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107',\n", " '(+)E1A_r60_a135', '(+)E1A_r60_a20', '(+)E1A_r60_a22', '(+)E1A_r60_a97',\n", " '(+)E1A_r60_n11', '(+)E1A_r60_n9', '3xSLv1', 'A_19_P00315452',\n", " 'A_19_P00315459', 'A_19_P00315482', 'A_19_P00315492', 'A_19_P00315493',\n", " 'A_19_P00315502', 'A_19_P00315506', 'A_19_P00315518', 'A_19_P00315519'],\n", " dtype='object', name='ID')\n", "\n", "Gene expression data shape: (50739, 94)\n" ] } ], "source": [ "# Use the helper function to get the proper file paths\n", "soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# Extract gene expression data\n", "try:\n", " gene_data = get_genetic_data(matrix_file_path)\n", " \n", " # Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", " \n", " # Print shape to understand the dataset dimensions\n", " print(f\"\\nGene expression data shape: {gene_data.shape}\")\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "3be6ec43", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "8478029b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:41.611403Z", "iopub.status.busy": "2025-03-25T04:01:41.611286Z", "iopub.status.idle": "2025-03-25T04:01:41.613908Z", "shell.execute_reply": "2025-03-25T04:01:41.613514Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping required: True\n", "These appear to be Agilent microarray probe IDs, not standard human gene symbols.\n" ] } ], "source": [ "# The gene identifiers observed appear to be microarray probe IDs (like \"(+)E1A_r60_1\", \"A_19_P00315452\"), not standard human gene symbols.\n", "# These look like Agilent microarray probe identifiers which need to be mapped to human gene symbols.\n", "\n", "requires_gene_mapping = True\n", "\n", "# Print the conclusion for clarity\n", "print(f\"Gene mapping required: {requires_gene_mapping}\")\n", "print(\"These appear to be Agilent microarray probe IDs, not standard human gene symbols.\")\n" ] }, { "cell_type": "markdown", "id": "619b0f09", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "009ff239", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:41.615163Z", "iopub.status.busy": "2025-03-25T04:01:41.615055Z", "iopub.status.idle": "2025-03-25T04:01:50.123686Z", "shell.execute_reply": "2025-03-25T04:01:50.123366Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['GE_BrightCorner', 'DarkCorner', 'A_23_P117082', 'A_33_P3246448', 'A_33_P3318220'], 'SPOT_ID': ['CONTROL', 'CONTROL', 'A_23_P117082', 'A_33_P3246448', 'A_33_P3318220'], 'CONTROL_TYPE': ['pos', 'pos', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, 'NM_015987', 'NM_080671', 'NM_178466'], 'GB_ACC': [nan, nan, 'NM_015987', 'NM_080671', 'NM_178466'], 'LOCUSLINK_ID': [nan, nan, 50865.0, 23704.0, 128861.0], 'GENE_SYMBOL': [nan, nan, 'HEBP1', 'KCNE4', 'BPIFA3'], 'GENE_NAME': [nan, nan, 'heme binding protein 1', 'potassium voltage-gated channel, Isk-related family, member 4', 'BPI fold containing family A, member 3'], 'UNIGENE_ID': [nan, nan, 'Hs.642618', 'Hs.348522', 'Hs.360989'], 'ENSEMBL_ID': [nan, nan, 'ENST00000014930', 'ENST00000281830', 'ENST00000375454'], 'ACCESSION_STRING': [nan, nan, 'ref|NM_015987|ens|ENST00000014930|gb|AF117615|gb|BC016277', 'ref|NM_080671|ens|ENST00000281830|tc|THC2655788', 'ref|NM_178466|ens|ENST00000375454|ens|ENST00000471233|tc|THC2478474'], 'CHROMOSOMAL_LOCATION': [nan, nan, 'chr12:13127906-13127847', 'chr2:223920197-223920256', 'chr20:31812208-31812267'], 'CYTOBAND': [nan, nan, 'hs|12p13.1', 'hs|2q36.1', 'hs|20q11.21'], 'DESCRIPTION': [nan, nan, 'Homo sapiens heme binding protein 1 (HEBP1), mRNA [NM_015987]', 'Homo sapiens potassium voltage-gated channel, Isk-related family, member 4 (KCNE4), mRNA [NM_080671]', 'Homo sapiens BPI fold containing family A, member 3 (BPIFA3), transcript variant 1, mRNA [NM_178466]'], 'GO_ID': [nan, nan, 'GO:0005488(binding)|GO:0005576(extracellular region)|GO:0005737(cytoplasm)|GO:0005739(mitochondrion)|GO:0005829(cytosol)|GO:0007623(circadian rhythm)|GO:0020037(heme binding)', 'GO:0005244(voltage-gated ion channel activity)|GO:0005249(voltage-gated potassium channel activity)|GO:0006811(ion transport)|GO:0006813(potassium ion transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0016324(apical plasma membrane)', 'GO:0005576(extracellular region)|GO:0008289(lipid binding)'], 'SEQUENCE': [nan, nan, 'AAGGGGGAAAATGTGATTTGTGCCTGATCTTTCATCTGTGATTCTTATAAGAGCTTTGTC', 'GCAAGTCTCTCTGCACCTATTAAAAAGTGATGTATATACTTCCTTCTTATTCTGTTGAGT', 'CATTCCATAAGGAGTGGTTCTCGGCAAATATCTCACTTGAATTTGACCTTGAATTGAGAC']}\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "try:\n", " # Use the correct variable name from previous steps\n", " gene_annotation = get_gene_annotation(soft_file_path)\n", " \n", " # 2. Preview the gene annotation dataframe\n", " print(\"Gene annotation preview:\")\n", " print(preview_df(gene_annotation))\n", " \n", "except UnicodeDecodeError as e:\n", " print(f\"Unicode decoding error: {e}\")\n", " print(\"Trying alternative approach...\")\n", " \n", " # Read the file with Latin-1 encoding which is more permissive\n", " import gzip\n", " import pandas as pd\n", " \n", " # Manually read the file line by line with error handling\n", " data_lines = []\n", " with gzip.open(soft_file_path, 'rb') as f:\n", " for line in f:\n", " # Skip lines starting with prefixes we want to filter out\n", " line_str = line.decode('latin-1')\n", " if not line_str.startswith('^') and not line_str.startswith('!') and not line_str.startswith('#'):\n", " data_lines.append(line_str)\n", " \n", " # Create dataframe from collected lines\n", " if data_lines:\n", " gene_data_str = '\\n'.join(data_lines)\n", " gene_annotation = pd.read_csv(pd.io.common.StringIO(gene_data_str), sep='\\t', low_memory=False)\n", " print(\"Gene annotation preview (alternative method):\")\n", " print(preview_df(gene_annotation))\n", " else:\n", " print(\"No valid gene annotation data found after filtering.\")\n", " gene_annotation = pd.DataFrame()\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene annotation data: {e}\")\n", " gene_annotation = pd.DataFrame()\n" ] }, { "cell_type": "markdown", "id": "e0d39bb2", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "4f6155b1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:50.125218Z", "iopub.status.busy": "2025-03-25T04:01:50.125100Z", "iopub.status.idle": "2025-03-25T04:01:50.601088Z", "shell.execute_reply": "2025-03-25T04:01:50.600752Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping dataframe shape: (46204, 2)\n", "First few rows of gene mapping:\n", " ID Gene\n", "2 A_23_P117082 HEBP1\n", "3 A_33_P3246448 KCNE4\n", "4 A_33_P3318220 BPIFA3\n", "5 A_33_P3236322 LOC100129869\n", "6 A_33_P3319925 IRG1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Resulting gene expression data shape: (19847, 94)\n", "First few rows of gene expression data:\n", " GSM3754774 GSM3754775 GSM3754776 GSM3754777 GSM3754778 \\\n", "Gene \n", "A1BG -0.156597 -0.789875 1.512063 0.087390 0.577830 \n", "A1BG-AS1 -0.055273 -1.115357 1.203011 0.361480 0.520860 \n", "A1CF 0.060761 4.167142 0.721337 3.379362 3.042676 \n", "A2M 1.730769 -0.925951 1.529119 0.470597 -0.023590 \n", "A2ML1 0.114604 -0.132678 -0.101793 0.342719 0.013418 \n", "\n", " GSM3754779 GSM3754780 GSM3754781 GSM3754782 GSM3754783 ... \\\n", "Gene ... \n", "A1BG -0.408904 0.921989 -1.117775 -1.007375 -0.585641 ... \n", "A1BG-AS1 -0.628904 0.711314 -0.730740 -0.651767 -1.438113 ... \n", "A1CF 0.437447 0.190483 1.911350 2.402059 4.377823 ... \n", "A2M -0.872226 1.652595 0.143312 -0.682341 -0.867899 ... \n", "A2ML1 -0.206153 0.024301 0.175596 0.225302 -0.076763 ... \n", "\n", " GSM3754858 GSM3754859 GSM3754860 GSM3754861 GSM3754862 \\\n", "Gene \n", "A1BG -0.006824 0.521656 0.265956 1.654205 -0.476906 \n", "A1BG-AS1 -0.172644 0.927442 0.934098 1.588539 -0.742384 \n", "A1CF -2.451899 -3.947608 -0.419664 0.280682 -3.851371 \n", "A2M 1.021807 2.353894 1.703777 1.027817 -0.295080 \n", "A2ML1 0.064888 2.028134 1.118435 0.160938 0.079693 \n", "\n", " GSM3754863 GSM3754864 GSM3754865 GSM3754866 GSM3754867 \n", "Gene \n", "A1BG 1.054766 -0.568381 -0.134526 0.753157 1.452306 \n", "A1BG-AS1 1.480629 0.933561 0.720043 1.538296 1.667827 \n", "A1CF 0.715678 -3.306372 3.026614 3.772440 2.164036 \n", "A2M 0.810758 -0.009463 -0.367532 0.301483 2.315752 \n", "A2ML1 -0.162874 0.046059 -0.137830 -0.234484 -0.075579 \n", "\n", "[5 rows x 94 columns]\n" ] } ], "source": [ "# 1. Observe the data to determine appropriate columns for mapping\n", "# From the gene annotation preview, we can see:\n", "# - 'ID' matches the probe identifiers from the gene expression data (like \"A_33_P3246448\")\n", "# - 'GENE_SYMBOL' contains the human gene symbols (like \"HEBP1\", \"KCNE4\")\n", "\n", "# 2. Extract mapping between probe IDs and gene symbols\n", "# Define the column names for the identifiers and gene symbols\n", "probe_id_col = 'ID'\n", "gene_symbol_col = 'GENE_SYMBOL'\n", "\n", "# Get the mapping dataframe using the helper function\n", "gene_mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)\n", "\n", "# Print mapping info\n", "print(f\"Gene mapping dataframe shape: {gene_mapping_df.shape}\")\n", "print(\"First few rows of gene mapping:\")\n", "print(gene_mapping_df.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping_df)\n", "\n", "# Normalize gene symbols to handle synonyms and case variations\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "\n", "# Print info about the resulting gene expression data\n", "print(f\"\\nResulting gene expression data shape: {gene_data.shape}\")\n", "print(\"First few rows of gene expression data:\")\n", "print(gene_data.head())\n" ] }, { "cell_type": "markdown", "id": "3becef1d", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "df8975f8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:50.602368Z", "iopub.status.busy": "2025-03-25T04:01:50.602246Z", "iopub.status.idle": "2025-03-25T04:01:51.755178Z", "shell.execute_reply": "2025-03-25T04:01:51.754829Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (19847, 94)\n", "First few normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06', 'AAA1']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Stomach_Cancer/gene_data/GSE130823.csv\n", "Trait data availability: Not available\n", "Gene expression data was processed and saved, but no linked data was created due to missing trait information.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the obtained gene expression data\n", "normalized_gene_data = gene_data # It's already normalized in Step 6\n", "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n", "print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\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", "# Check if trait data is available (from Step 2 we determined it was not)\n", "is_trait_available = False\n", "print(f\"Trait data availability: {'Available' if is_trait_available else 'Not available'}\")\n", "\n", "# Since trait data is not available, we cannot create clinical features or linked data\n", "# We'll use the initial validation since we can't perform the final validation without trait data\n", "validate_result = validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=is_trait_available\n", ")\n", "\n", "print(f\"Gene expression data was processed and saved, but no linked data was created due to missing trait information.\")" ] } ], "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 }