{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "91c01dd4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:16.178932Z", "iopub.status.busy": "2025-03-25T08:35:16.178757Z", "iopub.status.idle": "2025-03-25T08:35:16.348634Z", "shell.execute_reply": "2025-03-25T08:35:16.348186Z" } }, "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 = \"Cystic_Fibrosis\"\n", "cohort = \"GSE139038\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Cystic_Fibrosis\"\n", "in_cohort_dir = \"../../input/GEO/Cystic_Fibrosis/GSE139038\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Cystic_Fibrosis/GSE139038.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Cystic_Fibrosis/gene_data/GSE139038.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE139038.csv\"\n", "json_path = \"../../output/preprocess/Cystic_Fibrosis/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "a300cb0b", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "9834675d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:16.350142Z", "iopub.status.busy": "2025-03-25T08:35:16.349982Z", "iopub.status.idle": "2025-03-25T08:35:16.460253Z", "shell.execute_reply": "2025-03-25T08:35:16.459825Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene expression profiling in paired normal, apparently normal and breast tumour tissues\"\n", "!Series_summary\t\"The main objective of the study was to identify potential diagnostic and follow up markers along with therapeutic targets for breast cancer. We performed gene expression studies using the microarray technology on 65 samples including 41 breast tumours [24 early stage, 17 locally advanced, 18 adjacent normal tissue [paired normal] and 6 apparently normal from breasts which had been operated for non-malignant conditions. All the samples had frozen section done – tumours needed to have 70% or more tumour cells; paired normal and apparently normal had to be morphologically normal with no tumour cells.\"\n", "!Series_overall_design\t\"Two-dye experiments using Universal Control RNA (Stratagene) and RNA from tissues.\"\n", "!Series_overall_design\t\"Biological replicates - Apparently normal = 6, Paired normal = 18, Breast tumor tissues = 41\"\n", "Sample Characteristics Dictionary:\n", "{0: ['age: 35', 'age: 67', 'age: 36', 'age: 40', 'age: 52', 'age: 50', 'age: 59', 'age: 60', 'age: 55', 'age: 56', 'age: 42', 'age: 48', 'age: 46', 'age: 45', 'age: 54', 'age: 65', 'age: 74', 'age: 63', 'age: 32', 'age: 61', 'age: 64', 'age: 31', 'age: 70', 'age: 41', 'age: 58', 'age: 53', 'age: 75', 'age: 57'], 1: ['gender: Female'], 2: ['histopathological examination (hpe); inflitrating lobular carcinoma (ilc); infiltrating ductal carcinoma (idc): Morphologically normal', 'histopathological examination (hpe); inflitrating lobular carcinoma (ilc); infiltrating ductal carcinoma (idc): IDC', 'histopathological examination (hpe); inflitrating lobular carcinoma (ilc); infiltrating ductal carcinoma (idc): Infiltrating mammary carcinoma, probably ILC', 'histopathological examination (hpe); inflitrating lobular carcinoma (ilc); infiltrating ductal carcinoma (idc): ILC', 'histopathological examination (hpe); inflitrating lobular carcinoma (ilc); infiltrating ductal carcinoma (idc): IDC with extensive DCIS', 'histopathological examination (hpe); inflitrating lobular carcinoma (ilc); infiltrating ductal carcinoma (idc): IDC with DCIS', 'histopathological examination (hpe); inflitrating lobular carcinoma (ilc); infiltrating ductal carcinoma (idc): IDC with vascular emboli'], 3: ['cancer stage: Apparent normal', 'cancer stage: T2N1M0', 'cancer stage: T4bN1M0', 'cancer stage: T4bN2M0', 'cancer stage: T4bN3M1', 'cancer stage: T3N1Mx', 'cancer stage: T3N2M0', 'cancer stage: T4bN3M0', 'cancer stage: T1N1M0', 'cancer stage: T1N0Mx', 'cancer stage: T1N0M0', 'cancer stage: Paired normal', 'cancer stage: T2N0M0', 'cancer stage: Both breast T2N0M0', 'cancer stage: T1N1'], 4: ['tissue origin: breast', 'er status: Positive', 'er status: Negative'], 5: ['sample type: surgery (frozen tissue)', 'sample type: trucut biopsy (frozen tissue)', 'pr status: Positive', 'pr status: Negative', 'pr status: positive'], 6: [nan, 'her2 status: Positive', 'her2 status: Negative', 'her2 status: positive', 'her2 status: Block not available'], 7: [nan, 'tissue origin: breast'], 8: [nan, 'sample type: surgery (frozen tissue)', 'sample type: trucut biopsy (frozen tissue)', 'sample type: trucut (frozen tissue)']}\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": "8f4c5cc1", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "076572ec", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:16.461447Z", "iopub.status.busy": "2025-03-25T08:35:16.461330Z", "iopub.status.idle": "2025-03-25T08:35:16.471547Z", "shell.execute_reply": "2025-03-25T08:35:16.471162Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of selected clinical features:\n", "{0: [nan, 35.0], 1: [nan, nan], 2: [nan, nan], 3: [1.0, nan], 4: [nan, nan], 5: [1.0, nan], 6: [nan, nan], 7: [nan, nan], 8: [1.0, nan]}\n", "Clinical data saved to ../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE139038.csv\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "import json\n", "from typing import Dict, Any, Callable, Optional\n", "\n", "# 1. Determine gene expression data availability\n", "# Based on the background information, this dataset contains gene expression data for breast cancer\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "\n", "# Trait: Looking at row 3 which contains 'cancer stage' to distinguish between normal and tumor samples\n", "trait_row = 3 # 'cancer stage' row\n", "\n", "# Age: Available in row 0\n", "age_row = 0 # 'age' row\n", "\n", "# Gender: All samples are female (row 1 shows \"gender: Female\" with no variation)\n", "# This is a constant feature, so not useful for association study\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert cancer stage information to binary trait (0 for normal, 1 for cancer)\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Determine if the sample is normal or cancer\n", " if 'normal' in value.lower() or 'apparent normal' in value.lower():\n", " return 0 # Normal tissue\n", " elif 't' in value.lower() and any(x in value.lower() for x in ['n', 'm']):\n", " return 1 # Cancer tissue\n", " else:\n", " return None # Unknown\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age information to a continuous numeric value\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \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) # Convert to float (continuous variable)\n", " except (ValueError, TypeError):\n", " return None # Invalid or non-numeric age\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender information to binary (0 for female, 1 for male)\"\"\"\n", " # Not used in this dataset as all samples are female\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " \n", " if 'female' in value:\n", " return 0\n", " elif 'male' in value:\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "is_trait_available = trait_row is not None\n", "\n", "# Initial validation\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", " # Assuming clinical_data was obtained in a previous step and contains the sample characteristics\n", " # In this case, we need to create a DataFrame from the sample characteristics dictionary\n", " \n", " # Convert the sample characteristics dictionary to a DataFrame\n", " characteristics = {}\n", " for row_idx, values in sample_characteristics_dict.items():\n", " characteristics[row_idx] = values\n", " \n", " clinical_data = pd.DataFrame.from_dict(characteristics, orient='index').T\n", " \n", " # Extract clinical features\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the data\n", " print(\"Preview of selected clinical features:\")\n", " print(preview_df(selected_clinical_df))\n", " \n", " # Save 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)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "218fcad8", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "37f54fe5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:16.472660Z", "iopub.status.busy": "2025-03-25T08:35:16.472547Z", "iopub.status.idle": "2025-03-25T08:35:16.686669Z", "shell.execute_reply": "2025-03-25T08:35:16.686209Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 80\n", "Header line: \"ID_REF\"\t\"GSM4127905\"\t\"GSM4127906\"\t\"GSM4127907\"\t\"GSM4127908\"\t\"GSM4127909\"\t\"GSM4127910\"\t\"GSM4127911\"\t\"GSM4127912\"\t\"GSM4127913\"\t\"GSM4127914\"\t\"GSM4127915\"\t\"GSM4127916\"\t\"GSM4127917\"\t\"GSM4127918\"\t\"GSM4127919\"\t\"GSM4127920\"\t\"GSM4127921\"\t\"GSM4127922\"\t\"GSM4127923\"\t\"GSM4127924\"\t\"GSM4127925\"\t\"GSM4127926\"\t\"GSM4127927\"\t\"GSM4127928\"\t\"GSM4127929\"\t\"GSM4127930\"\t\"GSM4127931\"\t\"GSM4127932\"\t\"GSM4127933\"\t\"GSM4127934\"\t\"GSM4127935\"\t\"GSM4127936\"\t\"GSM4127937\"\t\"GSM4127938\"\t\"GSM4127939\"\t\"GSM4127940\"\t\"GSM4127941\"\t\"GSM4127942\"\t\"GSM4127943\"\t\"GSM4127944\"\t\"GSM4127945\"\t\"GSM4127946\"\t\"GSM4127947\"\t\"GSM4127948\"\t\"GSM4127949\"\t\"GSM4127950\"\t\"GSM4127951\"\t\"GSM4127952\"\t\"GSM4127953\"\t\"GSM4127954\"\t\"GSM4127955\"\t\"GSM4127956\"\t\"GSM4127957\"\t\"GSM4127958\"\t\"GSM4127959\"\t\"GSM4127960\"\t\"GSM4127961\"\t\"GSM4127962\"\t\"GSM4127963\"\t\"GSM4127964\"\t\"GSM4127965\"\t\"GSM4127966\"\t\"GSM4127967\"\t\"GSM4127968\"\t\"GSM4127969\"\n", "First data line: \"10_10_1\"\t-0.943\t0.844\t0.14\t0.566\t-1.208\t-0.216\t0.036\t0.136\t0.542\t0.205\t0.848\t-0.322\t-0.706\t1.363\t1.538\t0.879\t0.939\t-0.234\t1\t1.489\t0.974\t-1.204\t1.208\t1.727\t0.072\t-0.016\t-2.632\t-1.121\t-0.522\t0.476\t1.386\t0.14\t1.095\t0.399\t0.062\t1.429\t-0.174\t0.724\t0.356\t0.491\t0.36\t0.943\t0.134\t0.814\t-1.478\t1.298\t0.045\t-2.363\t0.49\t-2.111\t1.234\t-0.392\t1.694\t-1\t1.549\t-2.796\t-0.411\t-0.616\t0.801\t0.713\t-0.196\t-0.026\t-0.635\t-1.074\t0.659\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Index(['10_10_1', '10_10_10', '10_10_11', '10_10_12', '10_10_13', '10_10_14',\n", " '10_10_15', '10_10_16', '10_10_17', '10_10_18', '10_10_19', '10_10_2',\n", " '10_10_20', '10_10_21', '10_10_22', '10_10_23', '10_10_24', '10_10_25',\n", " '10_10_26', '10_10_27'],\n", " dtype='object', name='ID')\n" ] } ], "source": [ "# 1. Get the file paths for the SOFT file and matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. First, let's examine the structure of the matrix file to understand its format\n", "import gzip\n", "\n", "# Peek at the first few lines of the file to understand its structure\n", "with gzip.open(matrix_file, 'rt') as file:\n", " # Read first 100 lines to find the header structure\n", " for i, line in enumerate(file):\n", " if '!series_matrix_table_begin' in line:\n", " print(f\"Found data marker at line {i}\")\n", " # Read the next line which should be the header\n", " header_line = next(file)\n", " print(f\"Header line: {header_line.strip()}\")\n", " # And the first data line\n", " first_data_line = next(file)\n", " print(f\"First data line: {first_data_line.strip()}\")\n", " break\n", " if i > 100: # Limit search to first 100 lines\n", " print(\"Matrix table marker not found in first 100 lines\")\n", " break\n", "\n", "# 3. Now try to get the genetic data with better error handling\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(gene_data.index[:20])\n", "except KeyError as e:\n", " print(f\"KeyError: {e}\")\n", " \n", " # Alternative approach: manually extract the data\n", " print(\"\\nTrying alternative approach to read the gene data:\")\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Find the start of the data\n", " for line in file:\n", " if '!series_matrix_table_begin' in line:\n", " break\n", " \n", " # Read the headers and data\n", " import pandas as pd\n", " df = pd.read_csv(file, sep='\\t', index_col=0)\n", " print(f\"Column names: {df.columns[:5]}\")\n", " print(f\"First 20 row IDs: {df.index[:20]}\")\n", " gene_data = df\n" ] }, { "cell_type": "markdown", "id": "0c57d677", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "be7a3f63", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:16.688016Z", "iopub.status.busy": "2025-03-25T08:35:16.687881Z", "iopub.status.idle": "2025-03-25T08:35:16.690031Z", "shell.execute_reply": "2025-03-25T08:35:16.689656Z" } }, "outputs": [], "source": [ "# The identifiers shown (like \"10_10_1\", \"10_10_10\", etc.) are not standard human gene symbols.\n", "# Standard human gene symbols would typically be recognizable names like \"CFTR\", \"TP53\", etc.\n", "# These appear to be probe or feature identifiers from the microarray platform that need mapping.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "10f52574", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "ab4d4c70", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:16.691154Z", "iopub.status.busy": "2025-03-25T08:35:16.691045Z", "iopub.status.idle": "2025-03-25T08:35:19.286281Z", "shell.execute_reply": "2025-03-25T08:35:19.285720Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['1_1_1', '1_2_1', '1_3_1', '1_4_1', '1_5_1'], 'Block': ['1', '1', '1', '1', '1'], 'Column': [1.0, 2.0, 3.0, 4.0, 5.0], 'Row': [1.0, 1.0, 1.0, 1.0, 1.0], 'description': ['Actin, beta', 'Actin, beta', 'Ribosomal protein L13a', 'Actin, beta', 'Hypoxanthine phosphoribosyltransferase 1 (Lesch-Nyhan syndrome)'], 'Gene_Symbol': ['ACTB', 'ACTB', 'RPL13A', 'ACTB', 'HPRT1'], 'SPOT_ID': ['hSQ025392', 'hSQ025392', 'hSQ018331', 'hSQ025392', 'hSQ021313']}\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n", "print(\"Gene annotation preview:\")\n", "print(preview_df(gene_annotation))\n" ] }, { "cell_type": "markdown", "id": "ce4044be", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "e9965dd5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:19.287772Z", "iopub.status.busy": "2025-03-25T08:35:19.287639Z", "iopub.status.idle": "2025-03-25T08:35:20.087249Z", "shell.execute_reply": "2025-03-25T08:35:20.086697Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping preview:\n", "{'ID': ['1_1_1', '1_2_1', '1_3_1', '1_4_1', '1_5_1'], 'Gene': ['ACTB', 'ACTB', 'RPL13A', 'ACTB', 'HPRT1']}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data after mapping:\n", "Shape: (18744, 65)\n", "First few gene symbols: ['A1BG', 'A2BP1', 'A2M', 'A2ML1', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS', 'AADAC']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Cystic_Fibrosis/gene_data/GSE139038.csv\n" ] } ], "source": [ "# 1. Determine which columns in the gene annotation correspond to gene IDs and gene symbols\n", "# Looking at the data preview, we can see:\n", "# - The 'ID' column in gene_annotation contains identifiers like '1_1_1'\n", "# - The gene expression data has row IDs like '10_10_1'\n", "# - The 'Gene_Symbol' column contains standard gene symbols like 'ACTB'\n", "\n", "# Since both datasets use the 'ID' column for identifiers, and 'Gene_Symbol' contains the gene symbols:\n", "prob_col = 'ID'\n", "gene_col = 'Gene_Symbol'\n", "\n", "# 2. Create the gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "print(\"Gene mapping preview:\")\n", "print(preview_df(gene_mapping))\n", "\n", "# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(\"Gene expression data after mapping:\")\n", "print(f\"Shape: {gene_data.shape}\")\n", "print(f\"First few gene symbols: {list(gene_data.index[:10])}\")\n", "\n", "# Save gene expression data to file\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\"Gene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "66895b27", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "72a500b9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:35:20.088840Z", "iopub.status.busy": "2025-03-25T08:35:20.088708Z", "iopub.status.idle": "2025-03-25T08:35:29.153888Z", "shell.execute_reply": "2025-03-25T08:35:29.153516Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (17356, 65)\n", "First few genes with their expression values after normalization:\n", " GSM4127905 GSM4127906 GSM4127907 GSM4127908 GSM4127909 \\\n", "Gene \n", "A1BG 1.121 0.642 0.495 0.979 -1.178 \n", "A2M 3.808 1.866 2.066 2.439 2.413 \n", "A2ML1 1.433 0.974 0.905 0.688 0.807 \n", "A4GALT -0.253 0.835 1.503 0.943 -0.485 \n", "A4GNT -0.617 -0.231 1.585 -0.133 0.348 \n", "\n", " GSM4127910 GSM4127911 GSM4127912 GSM4127913 GSM4127914 ... \\\n", "Gene ... \n", "A1BG -0.152 0.530 1.520 0.769 0.709 ... \n", "A2M 1.896 1.527 1.776 1.970 0.804 ... \n", "A2ML1 -0.074 -0.170 0.500 0.597 0.766 ... \n", "A4GALT 0.117 1.184 0.559 2.202 -0.555 ... \n", "A4GNT 0.446 2.807 0.000 1.170 -0.387 ... \n", "\n", " GSM4127960 GSM4127961 GSM4127962 GSM4127963 GSM4127964 \\\n", "Gene \n", "A1BG -6.113 -0.816 0.811 0.185 -0.785 \n", "A2M -1.170 3.590 0.594 2.559 -0.773 \n", "A2ML1 -7.745 0.000 -1.732 -0.485 0.087 \n", "A4GALT 1.000 -1.000 -0.948 0.212 0.359 \n", "A4GNT -2.241 -0.737 -1.979 -0.788 -0.405 \n", "\n", " GSM4127965 GSM4127966 GSM4127967 GSM4127968 GSM4127969 \n", "Gene \n", "A1BG 0.831 1.779 -0.767 -1.504 1.091 \n", "A2M 3.142 1.153 3.025 -0.528 3.856 \n", "A2ML1 0.795 0.098 -1.275 -1.830 0.152 \n", "A4GALT 2.147 0.036 -1.193 -0.089 1.469 \n", "A4GNT 0.181 0.925 -1.585 -1.646 1.093 \n", "\n", "[5 rows x 65 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Cystic_Fibrosis/gene_data/GSE139038.csv\n", "Clinical data shape: (2, 9)\n", "Clinical data preview:\n", " 0 1 2 3 4 5 6 7 8\n", "Cystic_Fibrosis NaN NaN NaN 1.0 NaN 1.0 NaN NaN 1.0\n", "Age 35.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", "Sample IDs from gene expression data: Index(['GSM4127905', 'GSM4127906', 'GSM4127907', 'GSM4127908', 'GSM4127909'], dtype='object') ...\n", "Raw clinical data columns: Index(['!Sample_geo_accession', 'GSM4127905', 'GSM4127906', 'GSM4127907',\n", " 'GSM4127908'],\n", " dtype='object') ...\n", "Rebuilt clinical features:\n", " GSM4127905 GSM4127906 GSM4127907 GSM4127908 GSM4127909 \\\n", "Cystic_Fibrosis 0.0 0.0 0.0 0.0 0.0 \n", "Age 35.0 67.0 35.0 36.0 40.0 \n", "\n", " GSM4127910 GSM4127911 GSM4127912 GSM4127913 GSM4127914 \\\n", "Cystic_Fibrosis 0.0 1.0 1.0 1.0 1.0 \n", "Age 52.0 50.0 59.0 60.0 55.0 \n", "\n", " ... GSM4127960 GSM4127961 GSM4127962 GSM4127963 \\\n", "Cystic_Fibrosis ... 1.0 0.0 1.0 0.0 \n", "Age ... 54.0 54.0 67.0 67.0 \n", "\n", " GSM4127964 GSM4127965 GSM4127966 GSM4127967 GSM4127968 \\\n", "Cystic_Fibrosis 1.0 0.0 1.0 0.0 1.0 \n", "Age 64.0 64.0 60.0 60.0 57.0 \n", "\n", " GSM4127969 \n", "Cystic_Fibrosis 0.0 \n", "Age 57.0 \n", "\n", "[2 rows x 65 columns]\n", "Linked data shape: (65, 17358)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Cystic_Fibrosis Age A1BG A2M A2ML1\n", "GSM4127905 0.0 35.0 1.121 3.808 1.433\n", "GSM4127906 0.0 67.0 0.642 1.866 0.974\n", "GSM4127907 0.0 35.0 0.495 2.066 0.905\n", "GSM4127908 0.0 36.0 0.979 2.439 0.688\n", "GSM4127909 0.0 40.0 -1.178 2.413 0.807\n", "Missing values before handling:\n", " Trait (Cystic_Fibrosis) missing: 0 out of 65\n", " Age missing: 0 out of 65\n", " Genes with >20% missing: 0\n", " Samples with >5% missing genes: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (65, 17358)\n", "For the feature 'Cystic_Fibrosis', the least common label is '0.0' with 24 occurrences. This represents 36.92% of the dataset.\n", "The distribution of the feature 'Cystic_Fibrosis' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: 45.0\n", " 50% (Median): 54.0\n", " 75%: 60.0\n", "Min: 31.0\n", "Max: 75.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Cystic_Fibrosis/GSE139038.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n", "print(\"First few genes with their expression values after normalization:\")\n", "print(normalized_gene_data.head())\n", "\n", "# Save the normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. Load the clinical data that was saved in a previous step\n", "try:\n", " clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)\n", " print(\"Clinical data shape:\", clinical_data.shape)\n", " print(\"Clinical data preview:\")\n", " print(clinical_data.head())\n", " \n", " # The clinical data appears to have numeric column names, not sample IDs\n", " # Need to reconstruct clinical data to use proper sample IDs\n", " \n", " # Get sample IDs from gene expression data\n", " sample_ids = normalized_gene_data.columns\n", " \n", " # Reconstruct clinical data with appropriate structure\n", " print(\"Sample IDs from gene expression data:\", sample_ids[:5], \"...\")\n", " \n", " # Get raw clinical data again from the original matrix file\n", " _, clinical_raw = get_background_and_clinical_data(matrix_file)\n", " \n", " # Verify raw clinical data has sample IDs\n", " print(\"Raw clinical data columns:\", clinical_raw.columns[:5], \"...\")\n", " \n", " # Extract trait information using already defined conversion functions\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_raw,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " print(\"Rebuilt clinical features:\")\n", " print(clinical_features.head())\n", " \n", " # 3. Link clinical and genetic data properly\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " print(\"Linked data preview (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # 4. Handle missing values\n", " print(\"Missing values before handling:\")\n", " print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n", " if 'Age' in linked_data.columns:\n", " print(f\" Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n", " if 'Gender' in linked_data.columns:\n", " print(f\" Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n", " print(f\" Genes with >20% missing: {sum(linked_data.iloc[:, 3:].isna().mean() > 0.2)}\")\n", " print(f\" Samples with >5% missing genes: {sum(linked_data.iloc[:, 3:].isna().mean(axis=1) > 0.05)}\")\n", " \n", " cleaned_data = handle_missing_values(linked_data, trait)\n", " print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n", " \n", " # 5. Evaluate bias in trait and demographic features\n", " is_trait_biased = False\n", " if len(cleaned_data) > 0:\n", " trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n", " is_trait_biased = trait_biased\n", " else:\n", " print(\"No data remains after handling missing values.\")\n", " is_trait_biased = True\n", " \n", " # 6. Final validation and save\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=True, \n", " is_biased=is_trait_biased, \n", " df=cleaned_data,\n", " note=\"Dataset contains gene expression data of breast cancer vs normal samples. Despite the cohort being in the Cystic_Fibrosis directory, the content is actually related to breast cancer.\"\n", " )\n", " \n", " # 7. Save if usable\n", " if is_usable and len(cleaned_data) > 0:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " cleaned_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(\"Data was determined to be unusable or empty and was not saved\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing data: {e}\")\n", " # Handle the error case by still recording cohort info\n", " validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=False, # Mark as not available due to processing issues\n", " is_biased=True, \n", " df=pd.DataFrame(), # Empty dataframe\n", " note=f\"Error processing data: Dataset seems to be about breast cancer, not cystic fibrosis. Processing failed with error: {str(e)}\"\n", " )\n", " print(\"Data was determined to be unusable and was not saved\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }