{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "fd013f4f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:29.371289Z", "iopub.status.busy": "2025-03-25T07:02:29.371112Z", "iopub.status.idle": "2025-03-25T07:02:29.535490Z", "shell.execute_reply": "2025-03-25T07:02:29.535033Z" } }, "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 = \"Breast_Cancer\"\n", "cohort = \"GSE248830\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Breast_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Breast_Cancer/GSE248830\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Breast_Cancer/GSE248830.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Breast_Cancer/gene_data/GSE248830.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Breast_Cancer/clinical_data/GSE248830.csv\"\n", "json_path = \"../../output/preprocess/Breast_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "5e6aedde", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "9688b359", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:29.536782Z", "iopub.status.busy": "2025-03-25T07:02:29.536634Z", "iopub.status.idle": "2025-03-25T07:02:29.553211Z", "shell.execute_reply": "2025-03-25T07:02:29.552835Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Unlocking Molecular mechanisms and identifying druggable targets in matched-paired brain metastasis of Breast and Lung cancers \"\n", "!Series_summary\t\"Introduction: The incidence of brain metastases in cancer patients is increasing, with lung and breast cancer being the most common sources. Despite advancements in targeted therapies, the prognosis remains poor, highlighting the importance to investigate the underlying mechanisms in brain metastases. The aim of this study was to investigate the differences in the molecular mechanisms involved in brain metastasis of breast and lung cancers. In addition, we aimed to identify cancer lineage-specific druggable targets in the brain metastasis. Methods: To that aim, a cohort of 44 FFPE tissue samples, including 22 breast cancer and 22 lung adenocarcinoma (LUAD) and their matched-paired brain metastases were collected. Targeted gene expression profiles of primary tumors were compared to their matched-paired brain metastases samples using nCounter PanCancer IO 360™ Panel of NanoString technologies. Pathway analysis was performed using gene set analysis (GSA) and gene set enrichment analysis (GSEA). The validation was performed by using Immunohistochemistry (IHC) to confirm the expression of immune checkpoint inhibitors. Results: Our results revealed the significant upregulation of cancer-related genes in primary tumors compared to their matched-paired brain metastases (adj. p ≤ 0.05). We found that upregulated differentially expressed genes in breast cancer brain metastasis (BM-BC) and brain metastasis from lung adenocarcinoma (BM-LUAD) were associated with the metabolic stress pathway, particularly related to the glycolysis. Additionally, we found that the upregulated genes in BM-BC and BM-LUAD played roles in immune response regulation, tumor growth, and proliferation. Importantly, we identified high expression of the immune checkpoint VTCN1 in BM-BC, and VISTA, IDO1, NT5E, and HDAC3 in BM-LUAD. Validation using immunohistochemistry further supported these findings. Conclusion: In conclusion, the findings highlight the significance of using matched-paired samples to identify cancer lineage-specific therapies that may improve brain metastasis patients outcomes.\"\n", "!Series_overall_design\t\"RNA was extracted from FFPE samples of (primary LUAD and their matched paired brain metastasis n=22, primary BC and their matched paired brain metastasis n=22)\"\n", "Sample Characteristics Dictionary:\n", "{0: ['age at diagnosis: 49', 'age at diagnosis: 44', 'age at diagnosis: 41', 'age at diagnosis: 40', 'age at diagnosis: 48', 'age at diagnosis: 42', 'age at diagnosis: 47', 'age at diagnosis: 53', 'age at diagnosis: 74', 'age at diagnosis: 58', 'age at diagnosis: 51', 'age at diagnosis: 55', 'age at diagnosis: 46', 'age at diagnosis: 59', 'age at diagnosis: 50', 'age at diagnosis: 57', 'age at diagnosis: 60', 'age at diagnosis: 69', 'age at diagnosis: n.a.', 'age at diagnosis: 65', 'age at diagnosis: 37', 'age at diagnosis: 63', 'age at diagnosis: 70', 'age at diagnosis: 66', 'age at diagnosis: 64'], 1: ['Sex: female', 'Sex: male'], 2: ['histology: TNBC', 'histology: ER+ PR+ HER2-', 'histology: Unknown', 'histology: ER- PR- HER2+', 'histology: ER+ PR-HER2+', 'histology: ER+ PR- HER2-', 'histology: ER- PR+ HER2-', 'histology: adenocaricnoma'], 3: ['smoking status: n.a', 'smoking status: former-smoker', 'smoking status: smoker', 'smoking status: Never smoking', 'smoking status: unknown', 'smoking status: former-roker'], 4: ['treatment after surgery of bm: surgery + chemotherpy', 'treatment after surgery of bm: surgery + chemotherpy + Radiotherapy', 'treatment after surgery of bm: surgery + chemotherapy + Radiotherapy', 'treatment after surgery of bm: surgery', 'treatment after surgery of bm: surgery + chemotherapy + Radiotherapy', 'treatment after surgery of bm: surgery + chemotherapy', 'treatment after surgery of bm: surgery + chemotherpy + Radiotherapy', 'treatment after surgery of bm: surgery + chemotheapy + Radiotherapy', 'treatment after surgery of bm: Chemoterapy', 'treatment after surgery of bm: Radiotherapy & Chemoterapy', 'treatment after surgery of bm: Radiotherapy', 'treatment after surgery of bm: Other', 'treatment after surgery of bm: Surgery & Chemotherapy & Radiotherapy', 'treatment after surgery of bm: surgery & Radiotherapy', 'treatment after surgery of bm: surgery & Radiochemotherapy', 'treatment after surgery of bm: No treatment', 'treatment after surgery of bm: WBRT', 'treatment after surgery of bm: SRT']}\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": "345abae8", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "d14a6b01", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:29.554176Z", "iopub.status.busy": "2025-03-25T07:02:29.554062Z", "iopub.status.idle": "2025-03-25T07:02:29.579894Z", "shell.execute_reply": "2025-03-25T07:02:29.579353Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of selected clinical data:\n", "{'GSM7920782': [1.0, 49.0, 0.0], 'GSM7920783': [1.0, 44.0, 0.0], 'GSM7920784': [nan, 41.0, 0.0], 'GSM7920785': [1.0, 40.0, 0.0], 'GSM7920786': [1.0, 48.0, 0.0], 'GSM7920787': [nan, 42.0, 0.0], 'GSM7920788': [1.0, 47.0, 0.0], 'GSM7920789': [1.0, 53.0, 0.0], 'GSM7920790': [1.0, 41.0, 0.0], 'GSM7920791': [1.0, 74.0, 0.0], 'GSM7920792': [1.0, 58.0, 0.0], 'GSM7920793': [1.0, 51.0, 0.0], 'GSM7920794': [1.0, 55.0, 0.0], 'GSM7920795': [nan, 46.0, 0.0], 'GSM7920796': [1.0, 46.0, 0.0], 'GSM7920797': [1.0, 48.0, 0.0], 'GSM7920798': [1.0, 44.0, 0.0], 'GSM7920799': [1.0, 49.0, 0.0], 'GSM7920800': [1.0, 59.0, 0.0], 'GSM7920801': [1.0, 50.0, 0.0], 'GSM7920802': [1.0, 74.0, 0.0], 'GSM7920803': [1.0, 46.0, 0.0], 'GSM7920804': [0.0, 40.0, 0.0], 'GSM7920805': [0.0, 57.0, 1.0], 'GSM7920806': [0.0, 60.0, 1.0], 'GSM7920807': [0.0, 55.0, 0.0], 'GSM7920808': [0.0, 69.0, 0.0], 'GSM7920809': [0.0, nan, 0.0], 'GSM7920810': [0.0, nan, 1.0], 'GSM7920811': [0.0, 57.0, 1.0], 'GSM7920812': [0.0, nan, 0.0], 'GSM7920813': [0.0, 65.0, 1.0], 'GSM7920814': [0.0, 37.0, 1.0], 'GSM7920815': [0.0, 46.0, 0.0], 'GSM7920816': [0.0, 63.0, 1.0], 'GSM7920817': [0.0, 60.0, 1.0], 'GSM7920818': [0.0, 58.0, 0.0], 'GSM7920819': [0.0, 70.0, 0.0], 'GSM7920820': [0.0, 66.0, 0.0], 'GSM7920821': [0.0, 64.0, 1.0], 'GSM7920822': [0.0, 60.0, 1.0], 'GSM7920823': [0.0, 50.0, 0.0], 'GSM7920824': [0.0, 66.0, 1.0], 'GSM7920825': [0.0, 74.0, 1.0]}\n", "Clinical data saved to ../../output/preprocess/Breast_Cancer/clinical_data/GSE248830.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information, this appears to be gene expression data\n", "# The summary mentions \"Targeted gene expression profiles\" using nCounter PanCancer IO 360™ Panel\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "\n", "# 2.1 Data Availability\n", "# For trait, we can use histology which contains breast cancer information\n", "trait_row = 2 # histology\n", "age_row = 0 # age at diagnosis\n", "gender_row = 1 # Sex\n", "\n", "# 2.2 Data Type Conversion Functions\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert breast cancer histology to binary (1 for breast cancer, 0 for others)\"\"\"\n", " if value is None or 'unknown' in value.lower():\n", " return None\n", " \n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # If it's adenocarcinoma (lung cancer), it's not breast cancer\n", " if 'adenocaricnoma' in value.lower():\n", " return 0\n", " \n", " # If it contains any breast cancer markers, it's breast cancer\n", " if any(marker in value.lower() for marker in ['tnbc', 'er+', 'er-', 'pr+', 'pr-', 'her2+', 'her2-']):\n", " return 1\n", " \n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age to continuous numeric value\"\"\"\n", " if value is None:\n", " return None\n", " \n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if value.lower() in ['n.a', 'n.a.', 'unknown']:\n", " return None\n", " \n", " try:\n", " return float(value)\n", " except ValueError:\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender to binary (0 for female, 1 for male)\"\"\"\n", " if value is None:\n", " return None\n", " \n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " \n", " if value == 'female':\n", " return 0\n", " elif value == 'male':\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine if trait data is available\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort 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=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "if trait_row is not None:\n", " # Extract clinical features\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the extracted clinical data\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of selected clinical data:\")\n", " print(preview)\n", " \n", " # Save the clinical data to CSV\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "1fc1a34f", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "d2caea7d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:29.581408Z", "iopub.status.busy": "2025-03-25T07:02:29.581294Z", "iopub.status.idle": "2025-03-25T07:02:29.596602Z", "shell.execute_reply": "2025-03-25T07:02:29.596141Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/Breast_Cancer/GSE248830/GSE248830_family.soft.gz\n", "Matrix file: ../../input/GEO/Breast_Cancer/GSE248830/GSE248830_series_matrix.txt.gz\n", "Found the matrix table marker at line 58\n", "Gene data shape: (754, 44)\n", "First 20 gene/probe identifiers:\n", "['A2M', 'ACVR1C', 'ADAM12', 'ADGRE1', 'ADM', 'ADORA2A', 'AKT1', 'ALDOA', 'ALDOC', 'ANGPT1', 'ANGPT2', 'ANGPTL4', 'ANLN', 'APC', 'APH1B', 'API5', 'APLNR', 'APOE', 'APOL6', 'AQP9']\n" ] } ], "source": [ "# 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", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# Set gene availability flag\n", "is_gene_available = True # Initially assume gene data is available\n", "\n", "# First check if the matrix file contains the expected marker\n", "found_marker = False\n", "marker_row = None\n", "try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for i, line in enumerate(file):\n", " if \"!series_matrix_table_begin\" in line:\n", " found_marker = True\n", " marker_row = i\n", " print(f\"Found the matrix table marker at line {i}\")\n", " break\n", " \n", " if not found_marker:\n", " print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n", " is_gene_available = False\n", " \n", " # If marker was found, try to extract gene data\n", " if is_gene_available:\n", " try:\n", " # Try using the library function\n", " gene_data = get_genetic_data(matrix_file)\n", " \n", " if gene_data.shape[0] == 0:\n", " print(\"Warning: Extracted gene data has 0 rows.\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " # Print the first 20 gene/probe identifiers\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20].tolist())\n", " except Exception as e:\n", " print(f\"Error extracting gene data with get_genetic_data(): {e}\")\n", " is_gene_available = False\n", " \n", " # If gene data extraction failed, examine file content to diagnose\n", " if not is_gene_available:\n", " print(\"Examining file content to diagnose the issue:\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Print lines around the marker if found\n", " if marker_row is not None:\n", " for i, line in enumerate(file):\n", " if i >= marker_row - 2 and i <= marker_row + 10:\n", " print(f\"Line {i}: {line.strip()[:100]}...\")\n", " if i > marker_row + 10:\n", " break\n", " else:\n", " # If marker not found, print first 10 lines\n", " for i, line in enumerate(file):\n", " if i < 10:\n", " print(f\"Line {i}: {line.strip()[:100]}...\")\n", " else:\n", " break\n", " except Exception as e2:\n", " print(f\"Error examining file: {e2}\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing file: {e}\")\n", " is_gene_available = False\n", "\n", "# Update validation information if gene data extraction failed\n", "if not is_gene_available:\n", " print(\"Gene expression data could not be successfully extracted from this dataset.\")\n", " # Update the validation record since gene data isn't available\n", " is_trait_available = False # We already determined trait data isn't available in step 2\n", " validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n", " is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n" ] }, { "cell_type": "markdown", "id": "4f74afde", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "d87fae40", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:29.598037Z", "iopub.status.busy": "2025-03-25T07:02:29.597918Z", "iopub.status.idle": "2025-03-25T07:02:29.600218Z", "shell.execute_reply": "2025-03-25T07:02:29.599733Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers from the output above:\n", "# ['A2M', 'ACVR1C', 'ADAM12', 'ADGRE1', 'ADM', 'ADORA2A', 'AKT1', 'ALDOA', 'ALDOC', 'ANGPT1', 'ANGPT2', 'ANGPTL4', 'ANLN', 'APC', 'APH1B', 'API5', 'APLNR', 'APOE', 'APOL6', 'AQP9']\n", "# These appear to be standard human gene symbols rather than probe IDs or other identifiers\n", "# For example: A2M (Alpha-2-Macroglobulin), AKT1 (AKT Serine/Threonine Kinase 1), and APOE (Apolipoprotein E) are well-known gene symbols\n", "\n", "requires_gene_mapping = False\n" ] }, { "cell_type": "markdown", "id": "bcc08b17", "metadata": {}, "source": [ "### Step 5: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 6, "id": "589e8b40", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:29.601696Z", "iopub.status.busy": "2025-03-25T07:02:29.601587Z", "iopub.status.idle": "2025-03-25T07:02:29.876240Z", "shell.execute_reply": "2025-03-25T07:02:29.875764Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (754, 44)\n", "Gene data shape after normalization: (746, 44)\n", "Normalized gene data saved to ../../output/preprocess/Breast_Cancer/gene_data/GSE248830.csv\n", "Extracted clinical data shape: (3, 44)\n", "Preview of clinical data (first 5 samples):\n", " GSM7920782 GSM7920783 GSM7920784 GSM7920785 GSM7920786\n", "Breast_Cancer 1.0 1.0 NaN 1.0 1.0\n", "Age 49.0 44.0 41.0 40.0 48.0\n", "Gender 0.0 0.0 0.0 0.0 0.0\n", "Clinical data saved to ../../output/preprocess/Breast_Cancer/clinical_data/GSE248830.csv\n", "Gene data columns (first 5): ['GSM7920782', 'GSM7920783', 'GSM7920784', 'GSM7920785', 'GSM7920786']\n", "Clinical data columns (first 5): ['GSM7920782', 'GSM7920783', 'GSM7920784', 'GSM7920785', 'GSM7920786']\n", "Found 44 common samples between gene and clinical data\n", "Initial linked data shape: (44, 749)\n", "Preview of linked data (first 5 rows, first 5 columns):\n", " Breast_Cancer Age Gender A2M ACVR1C\n", "GSM7920782 1.0 49.0 0.0 13.210102 5.128485\n", "GSM7920783 1.0 44.0 0.0 13.237969 5.336850\n", "GSM7920784 NaN 41.0 0.0 14.728878 5.611523\n", "GSM7920785 1.0 40.0 0.0 15.399663 5.041711\n", "GSM7920786 1.0 48.0 0.0 12.970464 6.342262\n", "Linked data shape after handling missing values: (41, 749)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "For the feature 'Breast_Cancer', the least common label is '1.0' with 19 occurrences. This represents 46.34% of the dataset.\n", "The distribution of the feature 'Breast_Cancer' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Quartiles for 'Age':\n", " 25%: 48.0\n", " 50% (Median): 55.078947368421055\n", " 75%: 60.0\n", "Min: 37.0\n", "Max: 74.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '1.0' with 12 occurrences. This represents 29.27% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "Linked data saved to ../../output/preprocess/Breast_Cancer/GSE248830.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "try:\n", " # Make sure the directory exists\n", " os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", " \n", " # Use the gene_data variable from the previous step (don't try to load it from file)\n", " print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", " \n", " # Apply normalization to gene symbols\n", " normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", " \n", " # Save the normalized gene data\n", " normalized_gene_data.to_csv(out_gene_data_file)\n", " print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", " \n", " # Use the normalized data for further processing\n", " gene_data = normalized_gene_data\n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error normalizing gene data: {e}\")\n", " is_gene_available = False\n", "\n", "# 2. Load clinical data - respecting the analysis from Step 2\n", "# From Step 2, we determined:\n", "# trait_row = None # No Breast Cancer subtype data available\n", "# age_row = 2\n", "# gender_row = None\n", "is_trait_available = trait_row is not None\n", "\n", "# Skip clinical feature extraction when trait_row is None\n", "if is_trait_available:\n", " try:\n", " # Load the clinical data from file\n", " soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", " background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n", " \n", " # Extract clinical features\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender,\n", " age_row=age_row,\n", " convert_age=convert_age\n", " )\n", " \n", " print(f\"Extracted clinical data shape: {clinical_features.shape}\")\n", " print(\"Preview of clinical data (first 5 samples):\")\n", " print(clinical_features.iloc[:, :5])\n", " \n", " # Save the properly extracted 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", " except Exception as e:\n", " print(f\"Error extracting clinical data: {e}\")\n", " is_trait_available = False\n", "else:\n", " print(f\"No trait data ({trait}) available in this dataset based on previous analysis.\")\n", "\n", "# 3. Link clinical and genetic data if both are available\n", "if is_trait_available and is_gene_available:\n", " try:\n", " # Debug the column names to ensure they match\n", " print(f\"Gene data columns (first 5): {gene_data.columns[:5].tolist()}\")\n", " print(f\"Clinical data columns (first 5): {clinical_features.columns[:5].tolist()}\")\n", " \n", " # Check for common sample IDs\n", " common_samples = set(gene_data.columns).intersection(clinical_features.columns)\n", " print(f\"Found {len(common_samples)} common samples between gene and clinical data\")\n", " \n", " if len(common_samples) > 0:\n", " # Link the clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n", " print(f\"Initial linked data shape: {linked_data.shape}\")\n", " \n", " # Debug the trait values before handling missing values\n", " print(\"Preview of linked data (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # 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", " if linked_data.shape[0] > 0:\n", " # Check for bias in trait and demographic features\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # Validate the data quality and save cohort info\n", " note = \"Dataset contains gene expression data from triple negative breast cancer vs. luminal tumors, but no explicit breast cancer subtype labels in the sample characteristics.\"\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=note\n", " )\n", " \n", " # Save the linked data if it's usable\n", " if is_usable:\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(\"Data not usable for the trait study - not saving final linked data.\")\n", " else:\n", " print(\"After handling missing values, no samples remain.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"No valid samples after handling missing values.\"\n", " )\n", " else:\n", " print(\"No common samples found between gene expression and clinical data.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"No common samples between gene expression and clinical data.\"\n", " )\n", " except Exception as e:\n", " print(f\"Error linking or processing data: {e}\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True, # Assume biased if there's an error\n", " df=pd.DataFrame(), # Empty dataframe for metadata\n", " note=f\"Error in data processing: {str(e)}\"\n", " )\n", "else:\n", " # Create an empty DataFrame for metadata purposes\n", " empty_df = pd.DataFrame()\n", " \n", " # We can't proceed with linking if either trait or gene data is missing\n", " print(\"Cannot proceed with data linking due to missing trait or gene data.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True, # Data is unusable if we're missing components\n", " df=empty_df, # Empty dataframe for metadata\n", " note=\"Dataset contains gene expression data from triple negative breast cancer vs. luminal tumors, but no explicit breast cancer subtype labels in the sample characteristics.\"\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 }