{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0baf50c8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:31:13.390948Z", "iopub.status.busy": "2025-03-25T04:31:13.390727Z", "iopub.status.idle": "2025-03-25T04:31:13.560620Z", "shell.execute_reply": "2025-03-25T04:31:13.560241Z" } }, "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 = \"Uterine_Carcinosarcoma\"\n", "cohort = \"GSE36133\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Uterine_Carcinosarcoma\"\n", "in_cohort_dir = \"../../input/GEO/Uterine_Carcinosarcoma/GSE36133\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Uterine_Carcinosarcoma/GSE36133.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Uterine_Carcinosarcoma/gene_data/GSE36133.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Uterine_Carcinosarcoma/clinical_data/GSE36133.csv\"\n", "json_path = \"../../output/preprocess/Uterine_Carcinosarcoma/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "a625b126", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "6c2b793b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:31:13.562178Z", "iopub.status.busy": "2025-03-25T04:31:13.561815Z", "iopub.status.idle": "2025-03-25T04:31:14.095727Z", "shell.execute_reply": "2025-03-25T04:31:14.095340Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Files in the cohort directory:\n", "['GSE36133_family.soft.gz', 'GSE36133_series_matrix.txt.gz']\n", "Identified SOFT files: ['GSE36133_family.soft.gz']\n", "Identified matrix files: ['GSE36133_series_matrix.txt.gz']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Background Information:\n", "!Series_title\t\"Expression data from the Cancer Cell Line Encyclopedia (CCLE)\"\n", "!Series_summary\t\"The Cancer Cell Line Encyclopedia (CCLE) project is a collaboration between the Broad Institute, the Novartis Institutes for Biomedical Research and the Genomics Novartis Foundation to conduct a detailed genetic and pharmacologic characterization of a large panel of human cancer models\"\n", "!Series_summary\t\"It consists of a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from nearly 1,000 human cancer cell lines. All raw and processed data are available through an integrated portal on www.broadinstitute.org/ccle\"\n", "!Series_overall_design\t\"The final cell line collection spans 36 cancer types. Representation of cell lines for each cancer type was mainly driven by cancer mortality in the United States, as a surrogate of unmet medical need, as well as availability.\"\n", "\n", "Sample Characteristics Dictionary:\n", "{0: ['primary site: central_nervous_system', 'primary site: bone', 'primary site: prostate', 'primary site: stomach', 'primary site: urinary_tract', 'primary site: haematopoietic_and_lymphoid_tissue', 'primary site: kidney', 'primary site: thyroid', 'primary site: skin', 'primary site: soft_tissue', 'primary site: salivary_gland', 'primary site: ovary', 'primary site: lung', 'primary site: pleura', 'primary site: liver', 'primary site: endometrium', 'primary site: pancreas', 'primary site: breast', 'primary site: upper_aerodigestive_tract', 'primary site: large_intestine', 'primary site: autonomic_ganglia', 'primary site: oesophagus', 'primary site: biliary_tract', 'primary site: small_intestine'], 1: ['histology: glioma', 'histology: osteosarcoma', 'histology: carcinoma', 'histology: haematopoietic_neoplasm', 'histology: malignant_melanoma', 'histology: rhabdomyosarcoma', 'histology: lymphoid_neoplasm', 'histology: Ewings_sarcoma-peripheral_primitive_neuroectodermal_tumour', 'histology: mesothelioma', 'histology: chondrosarcoma', 'histology: neuroblastoma', 'histology: sex_cord-stromal_tumour', 'histology: primitive_neuroectodermal_tumour-medulloblastoma', 'histology: rhabdoid_tumour', 'histology: malignant_fibrous_histiocytoma-pleomorphic_sarcoma', 'histology: giant_cell_tumour', 'histology: other', 'histology: fibrosarcoma', 'histology: carcinoid-endocrine_tumour', 'histology: leiomyosarcoma', 'histology: sarcoma'], 2: ['histology subtype1: astrocytoma', nan, 'histology subtype1: adenocarcinoma', 'histology subtype1: astrocytoma_Grade_IV', 'histology subtype1: transitional_cell_carcinoma', 'histology subtype1: acute_lymphoblastic_leukaemia', 'histology subtype1: clear_cell_renal_cell_carcinoma', 'histology subtype1: anaplastic_carcinoma', 'histology subtype1: mucoepidermoid_carcinoma', 'histology subtype1: diffuse_large_B_cell_lymphoma', 'histology subtype1: renal_cell_carcinoma', 'histology subtype1: non_small_cell_carcinoma', 'histology subtype1: hepatocellular_carcinoma', 'histology subtype1: acute_myeloid_leukaemia', 'histology subtype1: plasma_cell_myeloma', 'histology subtype1: ductal_carcinoma', 'histology subtype1: B_cell_lymphoma_unspecified', 'histology subtype1: papillary_carcinoma', 'histology subtype1: acute_lymphoblastic_B_cell_leukaemia', 'histology subtype1: squamous_cell_carcinoma', 'histology subtype1: Burkitt_lymphoma', 'histology subtype1: blast_phase_chronic_myeloid_leukaemia', 'histology subtype1: dedifferentiated', 'histology subtype1: undifferentiated_carcinoma', 'histology subtype1: follicular_carcinoma', 'histology subtype1: small_cell_carcinoma', 'histology subtype1: large_cell_carcinoma', 'histology subtype1: granulosa_cell_tumour', 'histology subtype1: acute_lymphoblastic_T_cell_leukaemia', 'histology subtype1: small_cell_adenocarcinoma']}\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": "83c75855", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "26353cab", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:31:14.097183Z", "iopub.status.busy": "2025-03-25T04:31:14.097065Z", "iopub.status.idle": "2025-03-25T04:31:14.103459Z", "shell.execute_reply": "2025-03-25T04:31:14.103167Z" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. Gene Expression Data Availability\n", "# This appears to be a cancer cell line expression dataset, so it's likely to contain gene expression data\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# For Uterine_Carcinosarcoma trait:\n", "# Looking at primary site and histology keys\n", "# Primary site key 0 has 'endometrium', which is relevant to uterine cancer\n", "# But there's no specific indication of carcinosarcoma in the dictionary\n", "# For Uterine Carcinosarcoma, we would need to combine information from multiple fields\n", "trait_row = None # Not directly available for this specific trait\n", "\n", "# Age data\n", "# There is no age information in the sample characteristics\n", "age_row = None\n", "\n", "# Gender data \n", "# No gender information is provided in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"Convert trait values to binary format.\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if ':' in str(value):\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # This function won't be used as trait_row is None, but defining it for completeness\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age values to continuous format.\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if ':' in str(value):\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # This function won't be used as age_row is None, but defining it for completeness\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender values to binary format (0 for female, 1 for male).\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if ':' in str(value):\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # This function won't be used as gender_row is None, but defining it for completeness\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Save cohort info with initial filtering\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "# Skip this step since trait_row is None, indicating clinical data is not available\n" ] }, { "cell_type": "markdown", "id": "f4bbd5cb", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "9c686bcf", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:31:14.104712Z", "iopub.status.busy": "2025-03-25T04:31:14.104608Z", "iopub.status.idle": "2025-03-25T04:31:15.296055Z", "shell.execute_reply": "2025-03-25T04:31:15.295584Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 20 gene/probe identifiers:\n", "Index(['100009676_at', '10000_at', '10001_at', '10002_at', '10003_at',\n", " '100048912_at', '100049716_at', '10004_at', '10005_at', '10006_at',\n", " '10007_at', '10008_at', '100093630_at', '10009_at', '1000_at',\n", " '100101467_at', '100101938_at', '10010_at', '100113407_at', '10011_at'],\n", " dtype='object', name='ID')\n", "\n", "Gene expression data shape: (18926, 917)\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": "2834a341", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "c29f7d5b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:31:15.297362Z", "iopub.status.busy": "2025-03-25T04:31:15.297248Z", "iopub.status.idle": "2025-03-25T04:31:15.299342Z", "shell.execute_reply": "2025-03-25T04:31:15.299013Z" } }, "outputs": [], "source": [ "# Reviewing gene identifiers in the gene expression data\n", "# The identifiers follow the pattern: number_at\n", "# These appear to be probe IDs from a microarray platform, not human gene symbols\n", "\n", "# Based on biomedical knowledge, these identifiers (like '100009676_at', '10000_at') \n", "# are probe IDs from a microarray platform such as Affymetrix\n", "# They need to be mapped to human gene symbols for proper analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "026cb679", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "8c9fc72c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:31:15.300526Z", "iopub.status.busy": "2025-03-25T04:31:15.300418Z", "iopub.status.idle": "2025-03-25T04:31:34.378662Z", "shell.execute_reply": "2025-03-25T04:31:34.378265Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample of gene expression data (first 5 rows, first 5 columns):\n", " GSM886835 GSM886836 GSM886837 GSM886838 GSM886839\n", "ID \n", "100009676_at 6.1161 6.2052 6.1249 6.6154 5.4236\n", "10000_at 8.1556 6.6152 4.5676 4.3519 6.6723\n", "10001_at 9.7864 9.9699 8.8720 9.1376 10.0290\n", "10002_at 3.7977 4.0304 3.8455 3.7085 3.6431\n", "10003_at 3.5458 3.8504 4.0458 3.9508 4.1589\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Platform information:\n", "!Series_title = Expression data from the Cancer Cell Line Encyclopedia (CCLE)\n", "!Platform_title = Affymetrix Human Genome U133 Plus 2.0 Array [Brainarray Version 15.0.0, HGU133Plus2_Hs_ENTREZG]\n", "!Platform_description = This array is identical to GPL570 but the data were analyzed with a custom CDF Brainarray Version 15, hgu133plus2hsentrezg.\n", "#Description =\n", "ID\tORF\tDescription\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene 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expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n", "!Sample_description = Gene expression data from the CCLE\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation columns:\n", "['ID', 'ORF', 'Description']\n", "\n", "Gene annotation preview:\n", "{'ID': ['1_at', '10_at', '100_at', '1000_at', '10000_at'], 'ORF': ['1', '10', '100', '1000', '10000'], 'Description': ['alpha-1-B glycoprotein', 'N-acetyltransferase 2 (arylamine N-acetyltransferase)', 'adenosine deaminase', 'cadherin 2, type 1, N-cadherin (neuronal)', 'v-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma)']}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Matching rows in annotation for sample IDs: 9180\n", "\n", "Potential gene symbol columns: []\n", "\n", "Is this dataset likely to contain gene expression data? True\n" ] } ], "source": [ "# 1. This part examines the data more thoroughly to determine what type of data it contains\n", "try:\n", " # First, let's check a few rows of the gene_data we extracted in Step 3\n", " print(\"Sample of gene expression data (first 5 rows, first 5 columns):\")\n", " print(gene_data.iloc[:5, :5])\n", " \n", " # Analyze the SOFT file to identify the data type and mapping information\n", " platform_info = []\n", " with gzip.open(soft_file_path, 'rt', encoding='latin-1') as f:\n", " for line in f:\n", " if line.startswith(\"!Platform_title\") or line.startswith(\"!Series_title\") or \"description\" in line.lower():\n", " platform_info.append(line.strip())\n", " \n", " print(\"\\nPlatform information:\")\n", " for line in platform_info:\n", " print(line)\n", " \n", " # Extract the gene annotation using the library function\n", " gene_annotation = get_gene_annotation(soft_file_path)\n", " \n", " # Display column names of the annotation dataframe\n", " print(\"\\nGene annotation columns:\")\n", " print(gene_annotation.columns.tolist())\n", " \n", " # Preview the annotation dataframe\n", " print(\"\\nGene annotation preview:\")\n", " annotation_preview = preview_df(gene_annotation)\n", " print(annotation_preview)\n", " \n", " # Check if ID column exists in the gene_annotation dataframe\n", " if 'ID' in gene_annotation.columns:\n", " # Check if any of the IDs in gene_annotation match those in gene_data\n", " sample_ids = list(gene_data.index[:10])\n", " matching_rows = gene_annotation[gene_annotation['ID'].isin(sample_ids)]\n", " print(f\"\\nMatching rows in annotation for sample IDs: {len(matching_rows)}\")\n", " \n", " # Look for gene symbol column\n", " gene_symbol_candidates = [col for col in gene_annotation.columns if 'gene' in col.lower() or 'symbol' in col.lower() or 'name' in col.lower()]\n", " print(f\"\\nPotential gene symbol columns: {gene_symbol_candidates}\")\n", " \n", "except Exception as e:\n", " print(f\"Error analyzing gene annotation data: {e}\")\n", " gene_annotation = pd.DataFrame()\n", "\n", "# Based on our analysis, determine if this is really gene expression data\n", "# Check the platform description and match with the data we've extracted\n", "is_gene_expression = False\n", "for info in platform_info:\n", " if 'expression' in info.lower() or 'transcript' in info.lower() or 'mrna' in info.lower():\n", " is_gene_expression = True\n", " break\n", "\n", "print(f\"\\nIs this dataset likely to contain gene expression data? {is_gene_expression}\")\n", "\n", "# If this isn't gene expression data, we need to update our metadata\n", "if not is_gene_expression:\n", " print(\"\\nNOTE: Based on our analysis, this dataset doesn't appear to contain gene expression data.\")\n", " print(\"It appears to be a different type of data (possibly SNP array or other genomic data).\")\n", " # Update is_gene_available for metadata\n", " is_gene_available = False\n", " \n", " # Save the updated metadata\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" ] }, { "cell_type": "markdown", "id": "e43bd11b", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "2be0c82c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:31:34.380221Z", "iopub.status.busy": "2025-03-25T04:31:34.379856Z", "iopub.status.idle": "2025-03-25T04:31:36.449619Z", "shell.execute_reply": "2025-03-25T04:31:36.449157Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping preview:\n", " ID Gene\n", "0 1_at alpha-1-B glycoprotein\n", "1 10_at N-acetyltransferase 2 (arylamine N-acetyltrans...\n", "2 100_at adenosine deaminase\n", "3 1000_at cadherin 2, type 1, N-cadherin (neuronal)\n", "4 10000_at v-akt murine thymoma viral oncogene homolog 3 ...\n", "Gene mapping shape: (18909, 2)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Mapped gene data preview:\n", " GSM886835 GSM886836 GSM886837 GSM886838 GSM886839 GSM886840 \\\n", "Gene \n", "A- 39.130283 40.260633 38.686433 37.203967 40.561633 39.573033 \n", "A-2 4.666800 4.545600 4.542000 6.251500 4.606400 5.234100 \n", "A-52 13.361000 12.996000 13.073000 13.024000 13.232000 13.260000 \n", "A-I 14.850300 13.204300 15.684000 15.212200 13.680400 14.789400 \n", "A-II 3.817800 3.682100 3.711500 3.778300 3.757900 4.049600 \n", "\n", " GSM886841 GSM886842 GSM886843 GSM886844 ... GSM887742 GSM887743 \\\n", "Gene ... \n", "A- 40.203917 38.98245 42.64055 40.24315 ... 39.49325 38.85855 \n", "A-2 4.935200 4.99450 5.02700 4.94430 ... 4.73400 4.97930 \n", "A-52 13.453000 12.83100 13.21000 13.07200 ... 13.48000 12.37200 \n", "A-I 13.883200 15.05300 14.29620 14.39730 ... 14.66940 14.31750 \n", "A-II 3.921600 4.02670 3.93910 4.00720 ... 3.65490 3.53650 \n", "\n", " GSM887744 GSM887745 GSM887746 GSM887747 GSM887748 GSM887749 \\\n", "Gene \n", "A- 39.65235 37.543017 39.223067 37.539533 39.280583 38.292233 \n", "A-2 5.07500 5.077600 4.895400 4.349900 4.546200 4.623800 \n", "A-52 13.07600 12.837000 12.858000 13.234000 13.090000 13.112000 \n", "A-I 14.59090 14.938400 13.781700 14.196200 14.456200 16.092600 \n", "A-II 3.80610 3.677600 3.966600 3.729400 3.849800 3.960000 \n", "\n", " GSM887750 GSM887751 \n", "Gene \n", "A- 39.909033 38.2651 \n", "A-2 4.401700 4.6637 \n", "A-52 13.105000 12.6620 \n", "A-I 15.967100 14.3682 \n", "A-II 3.764800 3.7996 \n", "\n", "[5 rows x 917 columns]\n", "Gene data shape after mapping: (2116, 917)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Saved mapped gene expression data to ../../output/preprocess/Uterine_Carcinosarcoma/gene_data/GSE36133.csv\n" ] } ], "source": [ "# 1. Determine which columns to use for mapping\n", "# From the preview, we can see that:\n", "# - The ID column in gene_annotation contains probe IDs like '1_at', which match the format in gene_data\n", "# - The ORF column contains what appear to be Entrez IDs\n", "# - The Description column contains gene names/descriptions\n", "\n", "# Let's extract the mapping between probe IDs and gene symbols (from Description field)\n", "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Description')\n", "\n", "# Preview the mapping to confirm it looks correct\n", "print(\"Gene mapping preview:\")\n", "print(gene_mapping.head())\n", "print(f\"Gene mapping shape: {gene_mapping.shape}\")\n", "\n", "# 2. Apply the mapping to convert probe-level measurements to gene expression data\n", "# The library function apply_gene_mapping handles many-to-many relationships as described\n", "gene_data_mapped = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# Rename to meet the requirement\n", "gene_data = gene_data_mapped\n", "\n", "# Preview the mapped gene data\n", "print(\"\\nMapped gene data preview:\")\n", "print(gene_data.head())\n", "print(f\"Gene data shape after mapping: {gene_data.shape}\")\n", "\n", "# Save the gene expression data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"\\nSaved mapped gene expression data to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "85ec5f0c", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "10634275", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:31:36.450973Z", "iopub.status.busy": "2025-03-25T04:31:36.450850Z", "iopub.status.idle": "2025-03-25T04:31:37.180078Z", "shell.execute_reply": "2025-03-25T04:31:37.179555Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (1266, 917)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data saved to ../../output/preprocess/Uterine_Carcinosarcoma/gene_data/GSE36133.csv\n", "No clinical features available for this dataset.\n", "Abnormality detected in the cohort: GSE36133. Preprocessing failed.\n", "Data quality check result: Not usable (No trait data)\n" ] } ], "source": [ "# 1. Normalize gene symbols in the obtained gene expression data\n", "try:\n", " # Try to use the provided normalization function\n", " normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", "except Exception as e:\n", " print(f\"Error during gene symbol normalization: {e}\")\n", " # Fall back to using the gene data as is if normalization fails\n", " normalized_gene_data = gene_data\n", " print(f\"Using original gene data: {normalized_gene_data.shape}\")\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\"Gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. We need to handle the case where trait_row is None\n", "if trait_row is None:\n", " # Create a minimal DataFrame with the same structure expected by the validation function\n", " # This ensures we have a non-empty DataFrame with appropriate structure for validation\n", " sample_ids = normalized_gene_data.columns[:5] # Get a few sample IDs\n", " dummy_df = pd.DataFrame({\n", " trait: [None] * len(sample_ids)\n", " }, index=sample_ids)\n", " \n", " # No clinical features to extract\n", " clinical_features = pd.DataFrame()\n", " print(\"No clinical features available for this dataset.\")\n", " \n", " # Create a minimal dummy DataFrame for linked data\n", " linked_data = dummy_df\n", " \n", " # For datasets without trait data, mark them as biased (since there's no trait distribution at all)\n", " is_trait_biased = True\n", " \n", " # Validate and save cohort info\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=False, # We have no trait data\n", " is_biased=is_trait_biased, # Must provide a boolean value, not None\n", " df=linked_data,\n", " note=\"Dataset contains CCLE gene expression data but lacks specific Uterine_Carcinosarcoma annotations.\"\n", " )\n", " \n", " print(f\"Data quality check result: Not usable (No trait data)\")\n", "else:\n", " # Extract clinical features\n", " clinical_features = geo_select_clinical_features(\n", " 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", " # Save the clinical data\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 data 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", " \n", " # 4. Handle missing values\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", " \n", " # 5. Determine whether the trait and demographic features are biased\n", " is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " print(f\"Is trait biased: {is_trait_biased}\")\n", " \n", " # 6. Conduct quality check and save the cohort information\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=True,\n", " is_biased=is_trait_biased, \n", " df=linked_data,\n", " note=\"Cancer Cell Line Encyclopedia (CCLE) gene expression data\"\n", " )\n", " \n", " # 7. Save the linked data if it's usable\n", " print(f\"Data quality check result: {'Usable' if is_usable else 'Not usable'}\")\n", " if is_usable:\n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(f\"Data not saved due to quality issues.\")" ] } ], "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 }