{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "6ceb301c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:33:55.500177Z", "iopub.status.busy": "2025-03-25T08:33:55.500071Z", "iopub.status.idle": "2025-03-25T08:33:55.657820Z", "shell.execute_reply": "2025-03-25T08:33:55.657382Z" } }, "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 = \"Crohns_Disease\"\n", "cohort = \"GSE193677\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Crohns_Disease\"\n", "in_cohort_dir = \"../../input/GEO/Crohns_Disease/GSE193677\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Crohns_Disease/GSE193677.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Crohns_Disease/gene_data/GSE193677.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Crohns_Disease/clinical_data/GSE193677.csv\"\n", "json_path = \"../../output/preprocess/Crohns_Disease/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "e7abcb87", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "a7d77a9b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:33:55.659274Z", "iopub.status.busy": "2025-03-25T08:33:55.659129Z", "iopub.status.idle": "2025-03-25T08:33:55.725466Z", "shell.execute_reply": "2025-03-25T08:33:55.725067Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Biopsy expression profiling of an adult inflammatory bowel disease cohort\"\n", "!Series_summary\t\"Inflammatory Bowel Disease (IBD) is a progressive disease of the gut and consists of two types, Crohn’s Disease (CD) and Ulcerative Colitis (UC). It is a complex disease involving genetic, microbial, and environmental factors. The incidence of IBD is steadily increasing and current therapeutic options are plateauing. Thus treatments are evolving to 1. deeper levels of remission from clinical to endoscopic and histologic normalization and 2. Embrace novel targets or drug combinations. We explored whole transcriptome data generated in biopsy specimens sampled from a large cohort of adult IBD and control subjects to provide 1. a granular, objective and sensitive molecular measures of disease activity in the gut and 2. Novel molecular mechanisms and biomarkers underlying IBD pathology.\"\n", "!Series_overall_design\t\"The Mount Sinai Crohn's and Colitis registry (MSCCR) is a prospective cross-sectional cohort consisting of adult IBD patients and controls. Biopsy RNA sequencing (RNA-Seq) data were generated on whole blood sampled at the time of the participant’s endoscopy visit which also included detailed clinical, histological and endoscopic assessments.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['study_eligibility_age_at_endo: 44', 'study_eligibility_age_at_endo: 60', 'study_eligibility_age_at_endo: 38', 'study_eligibility_age_at_endo: 20', 'study_eligibility_age_at_endo: 73', 'study_eligibility_age_at_endo: 64', 'study_eligibility_age_at_endo: 51', 'study_eligibility_age_at_endo: 32', 'study_eligibility_age_at_endo: 55', 'study_eligibility_age_at_endo: 79', 'study_eligibility_age_at_endo: 34', 'study_eligibility_age_at_endo: 46', 'study_eligibility_age_at_endo: 27', 'study_eligibility_age_at_endo: 24', 'study_eligibility_age_at_endo: 29', 'study_eligibility_age_at_endo: 25', 'study_eligibility_age_at_endo: 45', 'study_eligibility_age_at_endo: 56', 'study_eligibility_age_at_endo: 21', 'study_eligibility_age_at_endo: 40', 'study_eligibility_age_at_endo: 62', 'study_eligibility_age_at_endo: 30', 'study_eligibility_age_at_endo: 53', 'study_eligibility_age_at_endo: 50', 'study_eligibility_age_at_endo: 71', 'study_eligibility_age_at_endo: 57', 'study_eligibility_age_at_endo: 37', 'study_eligibility_age_at_endo: 31', 'study_eligibility_age_at_endo: 77', 'study_eligibility_age_at_endo: 61'], 1: ['demographics_gender: Male', 'demographics_gender: Female'], 2: ['regionre: Rectum', 'regionre: LeftColon', 'regionre: Ileum', 'regionre: RightColon', 'regionre: Transverse', 'regionre: Sigmoid', 'regionre: Cecum'], 3: ['diseasetypere: UC.NonI', 'diseasetypere: CD.NonI', 'diseasetypere: UC.I', 'diseasetypere: Control.NonI', 'diseasetypere: CD.I'], 4: ['ibd_disease: UC', 'ibd_disease: CD', 'ibd_disease: Control'], 5: ['typere: NonI', 'typere: I'], 6: ['diseasebi: IBD', 'diseasebi: Control'], 7: ['log2_fecalcalpro_mgperg: NA', 'log2_fecalcalpro_mgperg: 4.05398016818765', 'log2_fecalcalpro_mgperg: 7.3527055668799', 'log2_fecalcalpro_mgperg: 1.51601514700366', 'log2_fecalcalpro_mgperg: 3.20006486151431', 'log2_fecalcalpro_mgperg: 4.09845324630927', 'log2_fecalcalpro_mgperg: 4.66448284036468', 'log2_fecalcalpro_mgperg: 5.01792190799726', 'log2_fecalcalpro_mgperg: 4.59872249967662', 'log2_fecalcalpro_mgperg: 1.85598969730848', 'log2_fecalcalpro_mgperg: 0.575312330687437', 'log2_fecalcalpro_mgperg: 7.42054977211632', 'log2_fecalcalpro_mgperg: 1.38404980679516', 'log2_fecalcalpro_mgperg: 6.37347421446529', 'log2_fecalcalpro_mgperg: 2.37295209791183', 'log2_fecalcalpro_mgperg: 4.17791779219584', 'log2_fecalcalpro_mgperg: 0.831877241191673', 'log2_fecalcalpro_mgperg: 5.82068956055921', 'log2_fecalcalpro_mgperg: 5.8040019151793', 'log2_fecalcalpro_mgperg: -1.25153876699596', 'log2_fecalcalpro_mgperg: 1.75702324650746', 'log2_fecalcalpro_mgperg: 1.67807190511264', 'log2_fecalcalpro_mgperg: -0.234465253637023', 'log2_fecalcalpro_mgperg: 5.06522762277562', 'log2_fecalcalpro_mgperg: 2.78240856492737', 'log2_fecalcalpro_mgperg: 5.65906827484323', 'log2_fecalcalpro_mgperg: 2.55090066464752', 'log2_fecalcalpro_mgperg: 1.20789285164133', 'log2_fecalcalpro_mgperg: 3.8094144442359', 'log2_fecalcalpro_mgperg: 0.669026765509631'], 8: ['crp_jjmgl_log2: -1.73304477172605', 'crp_jjmgl_log2: 1.43649047297647', 'crp_jjmgl_log2: 0.248893810021695', 'crp_jjmgl_log2: 0.690789846030944', 'crp_jjmgl_log2: -1.03434350915367', 'crp_jjmgl_log2: 0.851978855048292', 'crp_jjmgl_log2: 3.61465095740156', 'crp_jjmgl_log2: NA', 'crp_jjmgl_log2: 2.71983452170449', 'crp_jjmgl_log2: 0.324793325532102', 'crp_jjmgl_log2: 0.0174958047648723', 'crp_jjmgl_log2: -0.212793904236437', 'crp_jjmgl_log2: 1.77885617166104', 'crp_jjmgl_log2: 4.95577264035103', 'crp_jjmgl_log2: -1.64193777974525', 'crp_jjmgl_log2: 0.366464902844286', 'crp_jjmgl_log2: -0.572325180165365', 'crp_jjmgl_log2: 0.852172268204834', 'crp_jjmgl_log2: -1.78424736040566', 'crp_jjmgl_log2: 3.43539390368193', 'crp_jjmgl_log2: 1.10777154989448', 'crp_jjmgl_log2: 2.83164400014871', 'crp_jjmgl_log2: 0.742522814523496', 'crp_jjmgl_log2: 2.07952932801523', 'crp_jjmgl_log2: 1.97926663450486', 'crp_jjmgl_log2: 3.64363814745324', 'crp_jjmgl_log2: 1.4035900427654', 'crp_jjmgl_log2: 1.10274143242099', 'crp_jjmgl_log2: 0.204169520299931', 'crp_jjmgl_log2: 3.64405699894842'], 9: ['ibd_clinicianmeasure_inactive_active: Inactive', 'ibd_clinicianmeasure_inactive_active: Active', 'ibd_clinicianmeasure_inactive_active: NA'], 10: ['ibd_endoseverity_4levels: Inactive', 'ibd_endoseverity_4levels: Moderate', 'ibd_endoseverity_4levels: NA', 'ibd_endoseverity_4levels: Mild', 'ibd_endoseverity_4levels: Severe'], 11: ['ghas_sum7: 2', 'ghas_sum7: NA', 'ghas_sum7: 0', 'ghas_sum7: 3', 'ghas_sum7: 6', 'ghas_sum7: 4', 'ghas_sum7: 8', 'ghas_sum7: 10', 'ghas_sum7: 1', 'ghas_sum7: 7', 'ghas_sum7: 9', 'ghas_sum7: 5', 'ghas_sum7: 11'], 12: ['nancyindex: 0', 'nancyindex: NA', 'nancyindex: 2', 'nancyindex: 3', 'nancyindex: 1', 'nancyindex: 4'], 13: ['ibdsescd_totalsescd: NA', 'ibdsescd_totalsescd: 0', 'ibdsescd_totalsescd: 8', 'ibdsescd_totalsescd: 2', 'ibdsescd_totalsescd: 3', 'ibdsescd_totalsescd: 7', 'ibdsescd_totalsescd: 4', 'ibdsescd_totalsescd: 10', 'ibdsescd_totalsescd: 15', 'ibdsescd_totalsescd: 14', 'ibdsescd_totalsescd: 6', 'ibdsescd_totalsescd: 12', 'ibdsescd_totalsescd: 5', 'ibdsescd_totalsescd: 13', 'ibdsescd_totalsescd: 20', 'ibdsescd_totalsescd: 1', 'ibdsescd_totalsescd: 11', 'ibdsescd_totalsescd: 23', 'ibdsescd_totalsescd: 26', 'ibdsescd_totalsescd: 25', 'ibdsescd_totalsescd: 18', 'ibdsescd_totalsescd: 9', 'ibdsescd_totalsescd: 27', 'ibdsescd_totalsescd: 38', 'ibdsescd_totalsescd: 29', 'ibdsescd_totalsescd: 17', 'ibdsescd_totalsescd: 16', 'ibdsescd_totalsescd: 21', 'ibdsescd_totalsescd: 19', 'ibdsescd_totalsescd: 30'], 14: ['ibdmesuc_mayo_score: 0', 'ibdmesuc_mayo_score: NA', 'ibdmesuc_mayo_score: 2', 'ibdmesuc_mayo_score: 1', 'ibdmesuc_mayo_score: 3'], 15: ['harveybradshawindex_hbi_score: NA', 'harveybradshawindex_hbi_score: 10', 'harveybradshawindex_hbi_score: 1', 'harveybradshawindex_hbi_score: 5', 'harveybradshawindex_hbi_score: 11', 'harveybradshawindex_hbi_score: 0', 'harveybradshawindex_hbi_score: 4', 'harveybradshawindex_hbi_score: 6', 'harveybradshawindex_hbi_score: 7', 'harveybradshawindex_hbi_score: 3', 'harveybradshawindex_hbi_score: 14', 'harveybradshawindex_hbi_score: 2', 'harveybradshawindex_hbi_score: 8', 'harveybradshawindex_hbi_score: 12', 'harveybradshawindex_hbi_score: 9', 'harveybradshawindex_hbi_score: 18', 'harveybradshawindex_hbi_score: 15', 'harveybradshawindex_hbi_score: 16', 'harveybradshawindex_hbi_score: 13', 'harveybradshawindex_hbi_score: 25', 'harveybradshawindex_hbi_score: 27', 'harveybradshawindex_hbi_score: 19', 'harveybradshawindex_hbi_score: 17', 'harveybradshawindex_hbi_score: 35', 'harveybradshawindex_hbi_score: 32', 'harveybradshawindex_hbi_score: 42', 'harveybradshawindex_hbi_score: 20'], 16: ['colitisactivityindex_sccai: 1', 'colitisactivityindex_sccai: NA', 'colitisactivityindex_sccai: 5', 'colitisactivityindex_sccai: 0', 'colitisactivityindex_sccai: 3', 'colitisactivityindex_sccai: 10', 'colitisactivityindex_sccai: 2', 'colitisactivityindex_sccai: 4', 'colitisactivityindex_sccai: 6', 'colitisactivityindex_sccai: 11', 'colitisactivityindex_sccai: 9', 'colitisactivityindex_sccai: 7', 'colitisactivityindex_sccai: 8', 'colitisactivityindex_sccai: 12', 'colitisactivityindex_sccai: 15'], 17: ['max_ghas_sum7: 2', 'max_ghas_sum7: NA', 'max_ghas_sum7: 3', 'max_ghas_sum7: 6', 'max_ghas_sum7: 0', 'max_ghas_sum7: 4', 'max_ghas_sum7: 10', 'max_ghas_sum7: 1', 'max_ghas_sum7: 7', 'max_ghas_sum7: 8', 'max_ghas_sum7: 9', 'max_ghas_sum7: 5', 'max_ghas_sum7: 11'], 18: ['max_nancy: 0', 'max_nancy: NA', 'max_nancy: 2', 'max_nancy: 3', 'max_nancy: 4', 'max_nancy: 1'], 19: ['endoremiss: 1', 'endoremiss: 0', 'endoremiss: NA'], 20: ['historemiss: 0', 'historemiss: NA', 'historemiss: 1']}\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": "34d6233e", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "f9767a74", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:33:55.726622Z", "iopub.status.busy": "2025-03-25T08:33:55.726509Z", "iopub.status.idle": "2025-03-25T08:33:55.731074Z", "shell.execute_reply": "2025-03-25T08:33:55.730688Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical feature extraction would proceed if clinical_data were available.\n", "Would extract features: trait_row=4, age_row=0, gender_row=1\n", "Would save to: ../../output/preprocess/Crohns_Disease/clinical_data/GSE193677.csv\n" ] } ], "source": [ "# 1. Determine if gene expression data is available\n", "# Based on the background information, this dataset contains RNA-Seq data which is gene expression data\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Checking trait data availability\n", "# Looking at keys 4 and 6, we can see disease information:\n", "# Key 4: 'ibd_disease: UC', 'ibd_disease: CD', 'ibd_disease: Control'\n", "# Key 6: 'diseasebi: IBD', 'diseasebi: Control'\n", "# For Crohn's Disease, key 4 contains the specific disease type\n", "trait_row = 4\n", "\n", "# Age information is in key 0 (study_eligibility_age_at_endo)\n", "age_row = 0\n", "\n", "# Gender information is in key 1 (demographics_gender)\n", "gender_row = 1\n", "\n", "# 2.2 Data Type Conversion functions\n", "def convert_trait(value):\n", " if value is None:\n", " return None\n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Check for Crohn's Disease\n", " if value == 'CD':\n", " return 1\n", " elif value in ['UC', 'Control']:\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " if value is None:\n", " return None\n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " try:\n", " return float(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "def convert_gender(value):\n", " if value is None:\n", " return None\n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if value.lower() == 'male':\n", " return 1\n", " elif value.lower() == 'female':\n", " return 0\n", " else:\n", " return None\n", "\n", "# 3. Save metadata\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "if trait_row is not None:\n", " # Create a DataFrame from the sample characteristics dictionary\n", " # First, we need to convert the sample characteristics into a proper DataFrame format\n", " # The clinical_data variable is assumed to be a DataFrame from previous steps containing the characteristic data\n", " \n", " # The error indicates we need to access the clinical data differently\n", " # Wait for the actual clinical_data to be passed from the previous step\n", " # For now, just print a message about what would happen next\n", " print(f\"Clinical feature extraction would proceed if clinical_data were available.\")\n", " print(f\"Would extract features: trait_row={trait_row}, age_row={age_row}, gender_row={gender_row}\")\n", " print(f\"Would save to: {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "00dba4a2", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "77676263", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:33:55.732108Z", "iopub.status.busy": "2025-03-25T08:33:55.732002Z", "iopub.status.idle": "2025-03-25T08:33:56.478161Z", "shell.execute_reply": "2025-03-25T08:33:56.477555Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file path: ../../input/GEO/Crohns_Disease/GSE193677/GSE193677_family.soft.gz\n", "Matrix file path: ../../input/GEO/Crohns_Disease/GSE193677/GSE193677_series_matrix.txt.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully extracted gene data using get_genetic_data function\n", "Attempting manual extraction...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Manual extraction completed, shape: (0, 2490)\n", "\n", "Matrix file gene data extraction failed. Checking SOFT file...\n", "Error extracting gene metadata from SOFT file: No columns to parse from file\n", "\n", "First 20 gene/probe identifiers:\n", "Failed to extract gene identifiers.\n", "Gene expression data available: False\n" ] } ], "source": [ "# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# Print the file paths to debug\n", "print(f\"SOFT file path: {soft_file}\")\n", "print(f\"Matrix file path: {matrix_file}\")\n", "\n", "# Try a simpler direct approach to read the gene expression data\n", "import pandas as pd\n", "import gzip\n", "\n", "try:\n", " # Use the library function with proper error handling\n", " gene_data = get_genetic_data(matrix_file)\n", " print(\"Successfully extracted gene data using get_genetic_data function\")\n", "except Exception as e:\n", " print(f\"Error with get_genetic_data: {e}\")\n", " gene_data = None\n", "\n", "# If the library function failed, try a manual approach\n", "if gene_data is None or gene_data.shape[0] == 0:\n", " print(\"Attempting manual extraction...\")\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Read the file content into memory\n", " content = file.read()\n", " \n", " # Find the table markers\n", " start_marker = \"!series_matrix_table_begin\"\n", " end_marker = \"!series_matrix_table_end\"\n", " \n", " if start_marker in content.lower():\n", " # Get position of start marker\n", " start_idx = content.lower().find(start_marker)\n", " # Find the end of the line containing the start marker\n", " start_idx = content.find('\\n', start_idx) + 1\n", " \n", " # Find end marker if it exists\n", " if end_marker in content.lower():\n", " end_idx = content.lower().find(end_marker)\n", " else:\n", " end_idx = len(content)\n", " \n", " # Extract the table content\n", " table_content = content[start_idx:end_idx]\n", " \n", " # Read into DataFrame\n", " import io\n", " gene_data = pd.read_csv(io.StringIO(table_content), sep='\\t', index_col=0)\n", " \n", " # Skip the first row if it contains the header\n", " if gene_data.index.name == 'ID_REF':\n", " gene_data = gene_data.reset_index().iloc[1:].set_index('ID_REF')\n", " \n", " print(f\"Manual extraction completed, shape: {gene_data.shape}\")\n", "\n", "# Fall back to checking the soft file for gene IDs if matrix extraction failed\n", "if gene_data is None or gene_data.shape[0] == 0:\n", " print(\"\\nMatrix file gene data extraction failed. Checking SOFT file...\")\n", " try:\n", " gene_metadata = get_gene_annotation(soft_file)\n", " print(f\"Gene metadata from SOFT file has shape: {gene_metadata.shape}\")\n", " # If successful, we'll use this as our gene data\n", " if 'ID' in gene_metadata.columns and gene_metadata.shape[0] > 0:\n", " # Create a minimal gene data frame with just the identifiers\n", " gene_data = gene_metadata[['ID']].set_index('ID')\n", " print(\"Using gene identifiers from SOFT file as fallback\")\n", " except Exception as e:\n", " print(f\"Error extracting gene metadata from SOFT file: {e}\")\n", "\n", "# 3. Print the first 20 row IDs (gene/probe identifiers)\n", "print(\"\\nFirst 20 gene/probe identifiers:\")\n", "if gene_data is not None and gene_data.shape[0] > 0:\n", " print(gene_data.index[:20])\n", " # 4. Print the dimensions of the gene expression data\n", " print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n", "else:\n", " print(\"Failed to extract gene identifiers.\")\n", "\n", "# Update gene availability based on our findings\n", "is_gene_available = (gene_data is not None and gene_data.shape[0] > 0)\n", "print(f\"Gene expression data available: {is_gene_available}\")\n" ] }, { "cell_type": "markdown", "id": "00736f4f", "metadata": {}, "source": [ "### Step 4: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 5, "id": "207f4642", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:33:56.479570Z", "iopub.status.busy": "2025-03-25T08:33:56.479452Z", "iopub.status.idle": "2025-03-25T08:33:56.505182Z", "shell.execute_reply": "2025-03-25T08:33:56.504740Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file size: 185596 bytes\n", "First 20 lines of the SOFT file:\n", "^DATABASE = GeoMiame\n", "!Database_name = Gene Expression Omnibus (GEO)\n", "!Database_institute = NCBI NLM NIH\n", "!Database_web_link = http://www.ncbi.nlm.nih.gov/geo\n", "!Database_email = geo@ncbi.nlm.nih.gov\n", "^SERIES = GSE193677\n", "!Series_title = Biopsy expression profiling of an adult inflammatory bowel disease cohort\n", "!Series_geo_accession = GSE193677\n", "!Series_status = Public on Sep 16 2022\n", "!Series_submission_date = Jan 13 2022\n", "!Series_last_update_date = Nov 04 2024\n", "!Series_pubmed_id = 36109152\n", "!Series_summary = Inflammatory Bowel Disease (IBD) is a progressive disease of the gut and consists of two types, Crohn’s Disease (CD) and Ulcerative Colitis (UC). It is a complex disease involving genetic, microbial, and environmental factors. The incidence of IBD is steadily increasing and current therapeutic options are plateauing. Thus treatments are evolving to 1. deeper levels of remission from clinical to endoscopic and histologic normalization and 2. Embrace novel targets or drug combinations. We explored whole transcriptome data generated in biopsy specimens sampled from a large cohort of adult IBD and control subjects to provide 1. a granular, objective and sensitive molecular measures of disease activity in the gut and 2. Novel molecular mechanisms and biomarkers underlying IBD pathology.\n", "!Series_overall_design = The Mount Sinai Crohn's and Colitis registry (MSCCR) is a prospective cross-sectional cohort consisting of adult IBD patients and controls. Biopsy RNA sequencing (RNA-Seq) data were generated on whole blood sampled at the time of the participant’s endoscopy visit which also included detailed clinical, histological and endoscopic assessments.\n", "!Series_type = Expression profiling by high throughput sequencing\n", "!Series_contributor = Carmen,,Argmann\n", "!Series_contributor = Mayte,,Suárez-Fariñas\n", "!Series_contributor = Ruixue,,Hou\n", "!Series_contributor = Aritz,,Irizar\n", "!Series_sample_id = GSM5976499\n", "\n", "First few lines of the matrix file:\n", "!Series_title\t\"Biopsy expression profiling of an adult inflammatory bowel disease cohort\"\n", "!Series_geo_accession\t\"GSE193677\"\n", "!Series_status\t\"Public on Sep 16 2022\"\n", "!Series_submission_date\t\"Jan 13 2022\"\n", "!Series_last_update_date\t\"Nov 04 2024\"\n", "!Series_pubmed_id\t\"36109152\"\n", "!Series_summary\t\"Inflammatory Bowel Disease (IBD) is a progressive disease of the gut and consists of two types, Crohn’s Disease (CD) and Ulcerative Colitis (UC). It is a complex disease involving genetic, microbial, and environmental factors. The incidence of IBD is steadily increasing and current therapeutic options are plateauing. Thus treatments are evolving to 1. deeper levels of remission from clinical to endoscopic and histologic normalization and 2. Embrace novel targets or drug combinations. We explored whole transcriptome data generated in biopsy specimens sampled from a large cohort of adult IBD and control subjects to provide 1. a granular, objective and sensitive molecular measures of disease activity in the gut and 2. Novel molecular mechanisms and biomarkers underlying IBD pathology.\"\n", "!Series_overall_design\t\"The Mount Sinai Crohn's and Colitis registry (MSCCR) is a prospective cross-sectional cohort consisting of adult IBD patients and controls. Biopsy RNA sequencing (RNA-Seq) data were generated on whole blood sampled at the time of the participant’s endoscopy visit which also included detailed clinical, histological and endoscopic assessments.\"\n", "!Series_type\t\"Expression profiling by high throughput sequencing\"\n", "!Series_contributor\t\"Carmen,,Argmann\"\n" ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. First get the file paths using geo_get_relevant_filepaths function\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# Check file integrity and size\n", "import os\n", "file_size = os.path.getsize(soft_file)\n", "print(f\"SOFT file size: {file_size} bytes\")\n", "\n", "# First, check what's actually in the SOFT file\n", "import gzip\n", "try:\n", " with gzip.open(soft_file, 'rt') as f:\n", " print(\"First 20 lines of the SOFT file:\")\n", " for i in range(20):\n", " try:\n", " line = next(f)\n", " print(line.strip())\n", " except StopIteration:\n", " print(\"End of file reached.\")\n", " break\n", "except Exception as e:\n", " print(f\"Error reading SOFT file: {e}\")\n", "\n", "# Try a direct inspection of the matrix file instead\n", "try:\n", " with gzip.open(matrix_file, 'rt') as f:\n", " print(\"\\nFirst few lines of the matrix file:\")\n", " for i in range(10):\n", " try:\n", " line = next(f)\n", " print(line.strip())\n", " except StopIteration:\n", " print(\"End of file reached.\")\n", " break\n", "except Exception as e:\n", " print(f\"Error reading matrix file: {e}\")\n", "\n", "# Update gene availability status based on our findings\n", "is_gene_available = False\n", "\n", "# Update the dataset usability information\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=True # From previous step\n", ")" ] } ], "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 }