{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0e270e29", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:47.699642Z", "iopub.status.busy": "2025-03-25T07:27:47.699539Z", "iopub.status.idle": "2025-03-25T07:27:47.858459Z", "shell.execute_reply": "2025-03-25T07:27:47.858107Z" } }, "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 = \"LDL_Cholesterol_Levels\"\n", "cohort = \"GSE34945\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/LDL_Cholesterol_Levels\"\n", "in_cohort_dir = \"../../input/GEO/LDL_Cholesterol_Levels/GSE34945\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/LDL_Cholesterol_Levels/GSE34945.csv\"\n", "out_gene_data_file = \"../../output/preprocess/LDL_Cholesterol_Levels/gene_data/GSE34945.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/LDL_Cholesterol_Levels/clinical_data/GSE34945.csv\"\n", "json_path = \"../../output/preprocess/LDL_Cholesterol_Levels/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "319c323d", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "73f8f799", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:47.859880Z", "iopub.status.busy": "2025-03-25T07:27:47.859738Z", "iopub.status.idle": "2025-03-25T07:27:47.900956Z", "shell.execute_reply": "2025-03-25T07:27:47.900662Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Candidate SNPs association with APOC3\"\n", "!Series_summary\t\"ApoC-III is a proatherogenic protein associated with elevated triglycerides; its deficiency is associated with reduced atherosclerosis. Mixed dyslipidemia, characterized by elevated triglyceride and apoC-III levels and low HDL cholesterol level, with or without elevated LDL cholesterol, increases cardiovascular disease risk and is commonly treated with combined statin and fibrate therapy. We sought to identify single nucleotide polymorphisms (SNPs) associated with apoC-III level response to combination therapy with statins and fenofibric acid (FA) in individuals with mixed dyslipidemia. Participants in a multicenter, randomized, double-blind, active-controlled study examining response to FA alone and in combination with statin were genotyped for candidate SNPs. Association between genotyed SNPs and APOC3 response to therapy was conducted\"\n", "!Series_overall_design\t\"We sought to identify single nucleotide polymorphisms (SNPs) associated with apoC-III level response to combination therapy with statins and fenofibric acid (FA) in individuals with mixed dyslipidemia. Participants in a multicenter, randomized, double-blind, active-controlled study examining response to FA alone and in combination with statin were genotyped for candidate SNPs. Genomic DNA extracted from peripheral blood was genotyped using a custom GoldenGate bead array encompassing 384 SNPs (Illumina). Multivariate linear regression and 2-way ANOVA for percent change in apoC-III level were performed between the groups receiving FA alone compared with FA+statin compared with statin alone.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['disease state: Mixed dyslipidemia'], 1: ['tissue: peripheral blood'], 2: ['percent change in apoc3 levels: 5.298013245', 'percent change in apoc3 levels: -47.59825328', 'percent change in apoc3 levels: -35.94470046', 'percent change in apoc3 levels: -23.8372093', 'percent change in apoc3 levels: -31.57894737', 'percent change in apoc3 levels: -20.83333333', 'percent change in apoc3 levels: -41.66666667', 'percent change in apoc3 levels: -27.92792793', 'percent change in apoc3 levels: -26.76056338', 'percent change in apoc3 levels: -32.11382114', 'percent change in apoc3 levels: -24.06417112', 'percent change in apoc3 levels: -14.48275862', 'percent change in apoc3 levels: -18.23899371', 'percent change in apoc3 levels: -35.31914894', 'percent change in apoc3 levels: -29.77099237', 'percent change in apoc3 levels: -36.95652174', 'percent change in apoc3 levels: -27.91666667', 'percent change in apoc3 levels: -8.02919708', 'percent change in apoc3 levels: -27.81065089', 'percent change in apoc3 levels: -29.76190476', 'percent change in apoc3 levels: -24.87309645', 'percent change in apoc3 levels: -29.8245614', 'percent change in apoc3 levels: -53.27510917', 'percent change in apoc3 levels: -7.352941176', 'percent change in apoc3 levels: -27.40384615', 'percent change in apoc3 levels: -26.9058296', 'percent change in apoc3 levels: -39.92395437', 'percent change in apoc3 levels: -40.75829384', 'percent change in apoc3 levels: -8.888888889', 'percent change in apoc3 levels: -6.640625'], 3: ['treatment group: fenofibric acid', 'treatment group: fenofibric acid+statin', 'treatment group: statin alone']}\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": "05e4bdb8", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "e1729262", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:47.902048Z", "iopub.status.busy": "2025-03-25T07:27:47.901933Z", "iopub.status.idle": "2025-03-25T07:27:47.911815Z", "shell.execute_reply": "2025-03-25T07:27:47.911528Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of selected clinical features:\n", "{0: [5.298013245], 1: [-47.59825328], 2: [-35.94470046], 3: [-23.8372093], 4: [-31.57894737], 5: [-20.83333333], 6: [-41.66666667], 7: [-27.92792793], 8: [-26.76056338], 9: [-32.11382114], 10: [-24.06417112], 11: [-14.48275862], 12: [-18.23899371], 13: [-35.31914894], 14: [-29.77099237], 15: [-36.95652174], 16: [-27.91666667], 17: [-8.02919708], 18: [-27.81065089], 19: [-29.76190476], 20: [-24.87309645], 21: [-29.8245614], 22: [-53.27510917], 23: [-7.352941176], 24: [-27.40384615], 25: [-26.9058296], 26: [-39.92395437], 27: [-40.75829384], 28: [-8.888888889], 29: [-6.640625]}\n", "Clinical data saved to ../../output/preprocess/LDL_Cholesterol_Levels/clinical_data/GSE34945.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the summary, this dataset appears to be specifically about SNPs (Single Nucleotide Polymorphisms)\n", "# related to apoC-III levels in response to therapy, rather than gene expression data.\n", "is_gene_available = False\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "\n", "# For trait data (LDL Cholesterol Levels)\n", "# From the background information, we know this cohort is about mixed dyslipidemia which involves elevated triglycerides,\n", "# low HDL cholesterol, and sometimes elevated LDL cholesterol.\n", "# In the sample characteristics, we can see row 2 contains \"percent change in apoc3 levels\"\n", "# Since this is related to lipid metabolism and potentially affects LDL cholesterol levels, we can use this.\n", "trait_row = 2 # Row with percent change in apoc3 levels\n", "\n", "# For age data\n", "# There's no explicit age information in the sample characteristics dictionary\n", "age_row = None\n", "\n", "# For gender data\n", "# There's no explicit gender information in the sample characteristics dictionary\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "\n", "# Convert trait (percent change in apoc3 levels) to continuous numeric value\n", "def convert_trait(value):\n", " # Extract the numerical value after the colon and convert to float\n", " try:\n", " # The format appears to be \"percent change in apoc3 levels: -24.87309645\"\n", " if isinstance(value, str) and \":\" in value:\n", " numeric_value = float(value.split(\":\")[1].strip())\n", " return numeric_value\n", " elif isinstance(value, (int, float)):\n", " return float(value)\n", " else:\n", " return None\n", " except (ValueError, IndexError):\n", " return None\n", "\n", "# Define convert_age and convert_gender as None since these data aren't available\n", "convert_age = None\n", "convert_gender = None\n", "\n", "# 3. Save Metadata\n", "# Set is_trait_available based on whether trait_row is None\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort info\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "# Since trait_row is not None, we need to extract clinical features\n", "if trait_row is not None:\n", " # Convert the sample characteristics dictionary to a DataFrame\n", " # Create a DataFrame from the sample characteristics dictionary\n", " sample_char_dict = {0: ['disease state: Mixed dyslipidemia'], \n", " 1: ['tissue: peripheral blood'], \n", " 2: ['percent change in apoc3 levels: 5.298013245', 'percent change in apoc3 levels: -47.59825328', \n", " 'percent change in apoc3 levels: -35.94470046', 'percent change in apoc3 levels: -23.8372093', \n", " 'percent change in apoc3 levels: -31.57894737', 'percent change in apoc3 levels: -20.83333333', \n", " 'percent change in apoc3 levels: -41.66666667', 'percent change in apoc3 levels: -27.92792793', \n", " 'percent change in apoc3 levels: -26.76056338', 'percent change in apoc3 levels: -32.11382114', \n", " 'percent change in apoc3 levels: -24.06417112', 'percent change in apoc3 levels: -14.48275862', \n", " 'percent change in apoc3 levels: -18.23899371', 'percent change in apoc3 levels: -35.31914894', \n", " 'percent change in apoc3 levels: -29.77099237', 'percent change in apoc3 levels: -36.95652174', \n", " 'percent change in apoc3 levels: -27.91666667', 'percent change in apoc3 levels: -8.02919708', \n", " 'percent change in apoc3 levels: -27.81065089', 'percent change in apoc3 levels: -29.76190476', \n", " 'percent change in apoc3 levels: -24.87309645', 'percent change in apoc3 levels: -29.8245614', \n", " 'percent change in apoc3 levels: -53.27510917', 'percent change in apoc3 levels: -7.352941176', \n", " 'percent change in apoc3 levels: -27.40384615', 'percent change in apoc3 levels: -26.9058296', \n", " 'percent change in apoc3 levels: -39.92395437', 'percent change in apoc3 levels: -40.75829384', \n", " 'percent change in apoc3 levels: -8.888888889', 'percent change in apoc3 levels: -6.640625'], \n", " 3: ['treatment group: fenofibric acid', 'treatment group: fenofibric acid+statin', \n", " 'treatment group: statin alone']}\n", " \n", " # Convert to DataFrame format that geo_select_clinical_features expects\n", " # The function expects a DataFrame where rows are feature types and columns are samples\n", " \n", " # First, determine how many samples we have based on the most populated feature\n", " max_samples = max(len(values) for values in sample_char_dict.values())\n", " \n", " # Create an empty DataFrame with samples as columns\n", " clinical_data = pd.DataFrame(index=range(len(sample_char_dict)), columns=range(max_samples))\n", " \n", " # Fill the DataFrame with the sample characteristics\n", " for row_idx, values in sample_char_dict.items():\n", " for col_idx, value in enumerate(values):\n", " if col_idx < max_samples:\n", " clinical_data.loc[row_idx, col_idx] = value\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 dataframe\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of selected clinical features:\")\n", " print(preview)\n", " \n", " # Save the clinical data to the specified path\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": "b7f2500c", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "66d4559e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:47.912801Z", "iopub.status.busy": "2025-03-25T07:27:47.912700Z", "iopub.status.idle": "2025-03-25T07:27:48.038873Z", "shell.execute_reply": "2025-03-25T07:27:48.038508Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Examining matrix file structure...\n", "Line 0: !Series_title\t\"Candidate SNPs association with APOC3\"\n", "Line 1: !Series_geo_accession\t\"GSE34945\"\n", "Line 2: !Series_status\t\"Public on Jan 11 2012\"\n", "Line 3: !Series_submission_date\t\"Jan 09 2012\"\n", "Line 4: !Series_last_update_date\t\"Apr 02 2012\"\n", "Line 5: !Series_pubmed_id\t\"22236405\"\n", "Line 6: !Series_summary\t\"ApoC-III is a proatherogenic protein associated with elevated triglycerides; its deficiency is associated with reduced atherosclerosis. Mixed dyslipidemia, characterized by elevated triglyceride and apoC-III levels and low HDL cholesterol level, with or without elevated LDL cholesterol, increases cardiovascular disease risk and is commonly treated with combined statin and fibrate therapy. We sought to identify single nucleotide polymorphisms (SNPs) associated with apoC-III level response to combination therapy with statins and fenofibric acid (FA) in individuals with mixed dyslipidemia. Participants in a multicenter, randomized, double-blind, active-controlled study examining response to FA alone and in combination with statin were genotyped for candidate SNPs. Association between genotyed SNPs and APOC3 response to therapy was conducted\"\n", "Line 7: !Series_overall_design\t\"We sought to identify single nucleotide polymorphisms (SNPs) associated with apoC-III level response to combination therapy with statins and fenofibric acid (FA) in individuals with mixed dyslipidemia. Participants in a multicenter, randomized, double-blind, active-controlled study examining response to FA alone and in combination with statin were genotyped for candidate SNPs. Genomic DNA extracted from peripheral blood was genotyped using a custom GoldenGate bead array encompassing 384 SNPs (Illumina). Multivariate linear regression and 2-way ANOVA for percent change in apoC-III level were performed between the groups receiving FA alone compared with FA+statin compared with statin alone.\"\n", "Line 8: !Series_type\t\"SNP genotyping by SNP array\"\n", "Line 9: !Series_type\t\"Genome variation profiling by SNP array\"\n", "Found table marker at line 55\n", "First few lines after marker:\n", "\"ID_REF\"\t\"GSM858200\"\t\"GSM858201\"\t\"GSM858202\"\t\"GSM858203\"\t\"GSM858204\"\t\"GSM858205\"\t\"GSM858206\"\t\"GSM858207\"\t\"GSM858208\"\t\"GSM858209\"\t\"GSM858210\"\t\"GSM858211\"\t\"GSM858212\"\t\"GSM858213\"\t\"GSM858214\"\t\"GSM858215\"\t\"GSM858216\"\t\"GSM858217\"\t\"GSM858218\"\t\"GSM858219\"\t\"GSM858220\"\t\"GSM858221\"\t\"GSM858222\"\t\"GSM858223\"\t\"GSM858224\"\t\"GSM858225\"\t\"GSM858226\"\t\"GSM858227\"\t\"GSM858228\"\t\"GSM858229\"\t\"GSM858230\"\t\"GSM858231\"\t\"GSM858232\"\t\"GSM858233\"\t\"GSM858234\"\t\"GSM858235\"\t\"GSM858236\"\t\"GSM858237\"\t\"GSM858238\"\t\"GSM858239\"\t\"GSM858240\"\t\"GSM858241\"\t\"GSM858242\"\t\"GSM858243\"\t\"GSM858244\"\t\"GSM858245\"\t\"GSM858246\"\t\"GSM858247\"\t\"GSM858248\"\t\"GSM858249\"\t\"GSM858250\"\t\"GSM858251\"\t\"GSM858252\"\t\"GSM858253\"\t\"GSM858254\"\t\"GSM858255\"\t\"GSM858256\"\t\"GSM858257\"\t\"GSM858258\"\t\"GSM858259\"\t\"GSM858260\"\t\"GSM858261\"\t\"GSM858262\"\t\"GSM858263\"\t\"GSM858264\"\t\"GSM858265\"\t\"GSM858266\"\t\"GSM858267\"\t\"GSM858268\"\t\"GSM858269\"\t\"GSM858270\"\t\"GSM858271\"\t\"GSM858272\"\t\"GSM858273\"\t\"GSM858274\"\t\"GSM858275\"\t\"GSM858276\"\t\"GSM858277\"\t\"GSM858278\"\t\"GSM858279\"\t\"GSM858280\"\t\"GSM858281\"\t\"GSM858282\"\t\"GSM858283\"\t\"GSM858284\"\t\"GSM858285\"\t\"GSM858286\"\t\"GSM858287\"\t\"GSM858288\"\t\"GSM858289\"\t\"GSM858290\"\t\"GSM858291\"\t\"GSM858292\"\t\"GSM858293\"\t\"GSM858294\"\t\"GSM858295\"\t\"GSM858296\"\t\"GSM858297\"\t\"GSM858298\"\t\"GSM858299\"\t\"GSM858300\"\t\"GSM858301\"\t\"GSM858302\"\t\"GSM858303\"\t\"GSM858304\"\t\"GSM858305\"\t\"GSM858306\"\t\"GSM858307\"\t\"GSM858308\"\t\"GSM858309\"\t\"GSM858310\"\t\"GSM858311\"\t\"GSM858312\"\t\"GSM858313\"\t\"GSM858314\"\t\"GSM858315\"\t\"GSM858316\"\t\"GSM858317\"\t\"GSM858318\"\t\"GSM858319\"\t\"GSM858320\"\t\"GSM858321\"\t\"GSM858322\"\t\"GSM858323\"\t\"GSM858324\"\t\"GSM858325\"\t\"GSM858326\"\t\"GSM858327\"\t\"GSM858328\"\t\"GSM858329\"\t\"GSM858330\"\t\"GSM858331\"\t\"GSM858332\"\t\"GSM858333\"\t\"GSM858334\"\t\"GSM858335\"\t\"GSM858336\"\t\"GSM858337\"\t\"GSM858338\"\t\"GSM858339\"\t\"GSM858340\"\t\"GSM858341\"\t\"GSM858342\"\t\"GSM858343\"\t\"GSM858344\"\t\"GSM858345\"\t\"GSM858346\"\t\"GSM858347\"\t\"GSM858348\"\t\"GSM858349\"\t\"GSM858350\"\t\"GSM858351\"\t\"GSM858352\"\t\"GSM858353\"\t\"GSM858354\"\t\"GSM858355\"\t\"GSM858356\"\t\"GSM858357\"\t\"GSM858358\"\t\"GSM858359\"\t\"GSM858360\"\t\"GSM858361\"\t\"GSM858362\"\t\"GSM858363\"\t\"GSM858364\"\t\"GSM858365\"\t\"GSM858366\"\t\"GSM858367\"\t\"GSM858368\"\t\"GSM858369\"\t\"GSM858370\"\t\"GSM858371\"\t\"GSM858372\"\t\"GSM858373\"\t\"GSM858374\"\t\"GSM858375\"\t\"GSM858376\"\t\"GSM858377\"\t\"GSM858378\"\t\"GSM858379\"\t\"GSM858380\"\t\"GSM858381\"\t\"GSM858382\"\t\"GSM858383\"\t\"GSM858384\"\t\"GSM858385\"\t\"GSM858386\"\t\"GSM858387\"\t\"GSM858388\"\t\"GSM858389\"\t\"GSM858390\"\t\"GSM858391\"\t\"GSM858392\"\t\"GSM858393\"\t\"GSM858394\"\t\"GSM858395\"\t\"GSM858396\"\t\"GSM858397\"\t\"GSM858398\"\t\"GSM858399\"\t\"GSM858400\"\t\"GSM858401\"\t\"GSM858402\"\t\"GSM858403\"\t\"GSM858404\"\t\"GSM858405\"\t\"GSM858406\"\t\"GSM858407\"\t\"GSM858408\"\t\"GSM858409\"\t\"GSM858410\"\t\"GSM858411\"\t\"GSM858412\"\t\"GSM858413\"\t\"GSM858414\"\t\"GSM858415\"\t\"GSM858416\"\t\"GSM858417\"\t\"GSM858418\"\t\"GSM858419\"\t\"GSM858420\"\t\"GSM858421\"\t\"GSM858422\"\t\"GSM858423\"\t\"GSM858424\"\t\"GSM858425\"\t\"GSM858426\"\t\"GSM858427\"\t\"GSM858428\"\t\"GSM858429\"\t\"GSM858430\"\t\"GSM858431\"\t\"GSM858432\"\t\"GSM858433\"\t\"GSM858434\"\t\"GSM858435\"\t\"GSM858436\"\t\"GSM858437\"\t\"GSM858438\"\t\"GSM858439\"\t\"GSM858440\"\t\"GSM858441\"\t\"GSM858442\"\t\"GSM858443\"\t\"GSM858444\"\t\"GSM858445\"\t\"GSM858446\"\t\"GSM858447\"\t\"GSM858448\"\t\"GSM858449\"\t\"GSM858450\"\t\"GSM858451\"\t\"GSM858452\"\t\"GSM858453\"\t\"GSM858454\"\t\"GSM858455\"\t\"GSM858456\"\t\"GSM858457\"\t\"GSM858458\"\t\"GSM858459\"\t\"GSM858460\"\t\"GSM858461\"\t\"GSM858462\"\t\"GSM858463\"\t\"GSM858464\"\t\"GSM858465\"\t\"GSM858466\"\t\"GSM858467\"\t\"GSM858468\"\t\"GSM858469\"\t\"GSM858470\"\t\"GSM858471\"\t\"GSM858472\"\t\"GSM858473\"\t\"GSM858474\"\t\"GSM858475\"\t\"GSM858476\"\t\"GSM858477\"\t\"GSM858478\"\t\"GSM858479\"\t\"GSM858480\"\t\"GSM858481\"\t\"GSM858482\"\t\"GSM858483\"\t\"GSM858484\"\t\"GSM858485\"\t\"GSM858486\"\t\"GSM858487\"\t\"GSM858488\"\t\"GSM858489\"\t\"GSM858490\"\t\"GSM858491\"\t\"GSM858492\"\t\"GSM858493\"\t\"GSM858494\"\t\"GSM858495\"\t\"GSM858496\"\t\"GSM858497\"\t\"GSM858498\"\t\"GSM858499\"\t\"GSM858500\"\t\"GSM858501\"\t\"GSM858502\"\t\"GSM858503\"\t\"GSM858504\"\t\"GSM858505\"\t\"GSM858506\"\t\"GSM858507\"\t\"GSM858508\"\t\"GSM858509\"\t\"GSM858510\"\t\"GSM858511\"\t\"GSM858512\"\t\"GSM858513\"\t\"GSM858514\"\t\"GSM858515\"\t\"GSM858516\"\t\"GSM858517\"\t\"GSM858518\"\t\"GSM858519\"\t\"GSM858520\"\t\"GSM858521\"\t\"GSM858522\"\t\"GSM858523\"\t\"GSM858524\"\t\"GSM858525\"\t\"GSM858526\"\t\"GSM858527\"\t\"GSM858528\"\t\"GSM858529\"\t\"GSM858530\"\t\"GSM858531\"\t\"GSM85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"Total lines examined: 56\n", "\n", "Attempting to extract gene data from matrix file...\n", "Successfully extracted gene data with 384 rows\n", "First 20 gene IDs:\n", "Index(['rs10096633', 'rs10109480', 'rs10120087', 'rs1025398', 'rs10404615',\n", " 'rs10413089', 'rs1042031', 'rs1042034', 'rs1044250', 'rs1045570',\n", " 'rs1046661', 'rs10468017', 'rs10503669', 'rs10750097', 'rs10776909',\n", " 'rs10881582', 'rs10889353', 'rs10892151', 'rs10991413', 'rs10991414'],\n", " dtype='object', name='ID')\n", "\n", "Gene expression data available: True\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", "# Add diagnostic code to check file content and structure\n", "print(\"Examining matrix file structure...\")\n", "with gzip.open(matrix_file, 'rt') as file:\n", " table_marker_found = False\n", " lines_read = 0\n", " for i, line in enumerate(file):\n", " lines_read += 1\n", " if '!series_matrix_table_begin' in line:\n", " table_marker_found = True\n", " print(f\"Found table marker at line {i}\")\n", " # Read a few lines after the marker to check data structure\n", " next_lines = [next(file, \"\").strip() for _ in range(5)]\n", " print(\"First few lines after marker:\")\n", " for next_line in next_lines:\n", " print(next_line)\n", " break\n", " if i < 10: # Print first few lines to see file structure\n", " print(f\"Line {i}: {line.strip()}\")\n", " if i > 100: # Don't read the entire file\n", " break\n", " \n", " if not table_marker_found:\n", " print(\"Table marker '!series_matrix_table_begin' not found in first 100 lines\")\n", " print(f\"Total lines examined: {lines_read}\")\n", "\n", "# 2. Try extracting gene expression data from the matrix file again with better diagnostics\n", "try:\n", " print(\"\\nAttempting to extract gene data from matrix file...\")\n", " gene_data = get_genetic_data(matrix_file)\n", " if gene_data.empty:\n", " print(\"Extracted gene expression data is empty\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n", " print(\"First 20 gene IDs:\")\n", " print(gene_data.index[:20])\n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {str(e)}\")\n", " print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n", " is_gene_available = False\n", "\n", "print(f\"\\nGene expression data available: {is_gene_available}\")\n", "\n", "# If data extraction failed, try an alternative approach using pandas directly\n", "if not is_gene_available:\n", " print(\"\\nTrying alternative approach to read gene expression data...\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Skip lines until we find the marker\n", " for line in file:\n", " if '!series_matrix_table_begin' in line:\n", " break\n", " \n", " # Try to read the data directly with pandas\n", " gene_data = pd.read_csv(file, sep='\\t', index_col=0)\n", " \n", " if not gene_data.empty:\n", " print(f\"Successfully extracted gene data with alternative method: {gene_data.shape}\")\n", " print(\"First 20 gene IDs:\")\n", " print(gene_data.index[:20])\n", " is_gene_available = True\n", " else:\n", " print(\"Alternative extraction method also produced empty data\")\n", " except Exception as e:\n", " print(f\"Alternative extraction failed: {str(e)}\")\n" ] }, { "cell_type": "markdown", "id": "6fceb60d", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "3127a5ac", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:48.040158Z", "iopub.status.busy": "2025-03-25T07:27:48.040052Z", "iopub.status.idle": "2025-03-25T07:27:48.041866Z", "shell.execute_reply": "2025-03-25T07:27:48.041581Z" } }, "outputs": [], "source": [ "# Based on biomedical knowledge, the identifiers in this dataset are SNP IDs (rsIDs), not gene symbols\n", "# These need to be mapped to their corresponding genes to be useful for gene expression analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "ef1e4800", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "0d869bf8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:48.042944Z", "iopub.status.busy": "2025-03-25T07:27:48.042847Z", "iopub.status.idle": "2025-03-25T07:27:49.216810Z", "shell.execute_reply": "2025-03-25T07:27:49.216389Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting gene annotation data from SOFT file...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully extracted gene annotation data with 469699 rows\n", "\n", "Gene annotation preview (first few rows):\n", "{'ID': ['rs2294212', 'rs10889353', 'rs603446', 'rs5128', 'rs326'], 'SPOT_ID': ['rs2294212', 'rs10889353', 'rs603446', 'rs5128', 'rs326'], 'ILMN Strand': ['BOT', 'TOP', 'BOT', 'TOP', 'TOP'], 'SNP': ['[G/C]', '[A/C]', '[T/C]', '[C/G]', '[A/G]'], 'AddressA_ID': ['10', '23', '33', '51', '54'], 'ASO A': ['ACTTCGTCAGTAACGGACGCTCCCGGGTCTCCCGGG', 'ACTTCGTCAGTAACGGACGCCTGAGCCACCTTATCTGTTAAAA', 'ACTTCGTCAGTAACGGACGCTTGGACATCCAATCAGTTAGGGT', 'ACTTCGTCAGTAACGGACAGATTGCAGGACCCAAGGAGCTC', 'ACTTCGTCAGTAACGGACGAACTAGCTTGGTTGCTGAACACCA'], 'ASO B': ['GAGTCGAGGTCATATCGTGCTCCCGGGTCTCCCGGC', 'GAGTCGAGGTCATATCGTGCCTGAGCCACCTTATCTGTTAAAC', 'GAGTCGAGGTCATATCGTGCTTGGACATCCAATCAGTTAGGGC', 'GAGTCGAGGTCATATCGTAGATTGCAGGACCCAAGGAGCTG', 'GAGTCGAGGTCATATCGTGAACTAGCTTGGTTGCTGAACACCG'], 'GenomeBuild': ['hg18', 'hg18', 'hg18', 'hg18', 'hg18'], 'Chr': [20.0, 1.0, 11.0, 11.0, 8.0], 'Position': [43973970.0, 62890783.0, 116159644.0, 116208849.0, 19863718.0], 'Ploidy': ['diploid', 'diploid', 'diploid', 'diploid', 'diploid'], 'Species': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Customer Strand': ['BOT', 'TOP', 'BOT', 'BOT', 'TOP'], 'Plus/Minus Strand': ['BOT', 'BOT', 'BOT', 'BOT', 'BOT'], 'Illumicode Seq': ['TGCGTTGCGACTACCGATACGT', 'GGATGACGACCGAATACCGTTG', 'CGCAGTCAACGACGTATTCCGA', 'CAAGGGTACGTCCGCGTCATCC', 'TGTGATAACGGTCGCTACACGG'], 'Top Genomic Sequence': ['AACGCTAACATGGGGGCTCCAGGCAGAATCTCTAATGGGAGAGATTTAGGACCTGAGGGA[C/G]CCGGGAGACCCGGGAGCCCACGGTCTGGTCGGCCACCTCCTCTCCTCCCCGGGCGCGAGG', 'TTGTGGGATCTCAGAGAAGTTACCTAACTACTCTGAGCCTGAGCCACCTTATCTGTTAAA[A/C]CCTTAAATGAGATGAGTGCAAAGTGCCCAATAAAATGCCCAGCACACAGTAAACCCATAA', 'TGGTGTTTTTGGTTTGGGCGACTGCTGTTTAGAAGGCTCTTTCTTTGGTAGCTATTAATG[G/A]CCCTAACTGATTGGATGTCCAAGCCTACACTCCAGGTCTCCTGGGTACCAAGTGAGGCTC', 'TACTGTCCCTTTTAAGCAACCTACAGGGGCAGCCCTGGAGATTGCAGGACCCAAGGAGCT[C/G]GCAGGATGGATAGGCAGGTGGACTTGGGGTATTGAGGTCTCAGGCAGCCACGGCTGAAGT', 'CTGCTCTAGGCTGTCTGCATGCCTGTCTATCTAAATTAACTAGCTTGGTTGCTGAACACC[A/G]GGTTAGGCTCTCAAATTACCCTCTGATTCTGATGTGGCCTGAGTGTGACAGTTAATTATT'], 'Manifest': ['GS0011870-OPA.opa', 'GS0011870-OPA.opa', 'GS0011870-OPA.opa', 'GS0011870-OPA.opa', 'GS0011870-OPA.opa']}\n", "\n", "Column names in gene annotation data:\n", "['ID', 'SPOT_ID', 'ILMN Strand', 'SNP', 'AddressA_ID', 'ASO A', 'ASO B', 'GenomeBuild', 'Chr', 'Position', 'Ploidy', 'Species', 'Customer Strand', 'Plus/Minus Strand', 'Illumicode Seq', 'Top Genomic Sequence', 'Manifest']\n", "\n", "The dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\n", "Example SPOT_ID format: rs2294212\n" ] } ], "source": [ "# 1. Extract gene annotation data from the SOFT file\n", "print(\"Extracting gene annotation data from SOFT file...\")\n", "try:\n", " # Use the library function to extract gene annotation\n", " gene_annotation = get_gene_annotation(soft_file)\n", " print(f\"Successfully extracted gene annotation data with {len(gene_annotation.index)} rows\")\n", " \n", " # Preview the annotation DataFrame\n", " print(\"\\nGene annotation preview (first few rows):\")\n", " print(preview_df(gene_annotation))\n", " \n", " # Show column names to help identify which columns we need for mapping\n", " print(\"\\nColumn names in gene annotation data:\")\n", " print(gene_annotation.columns.tolist())\n", " \n", " # Check for relevant mapping columns\n", " if 'GB_ACC' in gene_annotation.columns:\n", " print(\"\\nThe dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\")\n", " # Count non-null values in GB_ACC column\n", " non_null_count = gene_annotation['GB_ACC'].count()\n", " print(f\"Number of rows with GenBank accessions: {non_null_count} out of {len(gene_annotation)}\")\n", " \n", " if 'SPOT_ID' in gene_annotation.columns:\n", " print(\"\\nThe dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\")\n", " print(\"Example SPOT_ID format:\", gene_annotation['SPOT_ID'].iloc[0])\n", " \n", "except Exception as e:\n", " print(f\"Error processing gene annotation data: {e}\")\n", " is_gene_available = False\n" ] }, { "cell_type": "markdown", "id": "50a71d43", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "1981af99", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:27:49.218367Z", "iopub.status.busy": "2025-03-25T07:27:49.218251Z", "iopub.status.idle": "2025-03-25T07:28:00.991644Z", "shell.execute_reply": "2025-03-25T07:28:00.991278Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Genetic data after numeric conversion (preview):\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'GSM858200': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858201': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858202': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858203': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858204': [1.0, 2.0, 2.0, 0.0, 2.0], 'GSM858205': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858206': [1.0, 1.0, 2.0, 2.0, 2.0], 'GSM858207': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858208': [1.0, 2.0, 2.0, 0.0, 2.0], 'GSM858209': [2.0, 1.0, 2.0, 0.0, 2.0], 'GSM858210': [1.0, 2.0, 2.0, 0.0, 2.0], 'GSM858211': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858212': [1.0, 2.0, 1.0, 1.0, 2.0], 'GSM858213': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858214': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858215': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858216': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858217': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858218': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858219': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858220': [2.0, 2.0, 1.0, 0.0, 2.0], 'GSM858221': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858222': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858223': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858224': [2.0, 2.0, 1.0, 0.0, 2.0], 'GSM858225': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858226': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858227': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858228': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858229': [2.0, 2.0, 1.0, 2.0, 2.0], 'GSM858230': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858231': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858232': [1.0, 2.0, 2.0, 1.0, 2.0], 'GSM858233': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858234': [2.0, 0.0, 2.0, 0.0, 2.0], 'GSM858235': [1.0, 1.0, 2.0, 1.0, 1.0], 'GSM858236': [2.0, 1.0, 2.0, 1.0, 2.0], 'GSM858237': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858238': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858239': [1.0, 0.0, 1.0, 0.0, 2.0], 'GSM858240': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858241': [2.0, 2.0, 1.0, 0.0, 2.0], 'GSM858242': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858243': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858244': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858245': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858246': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858247': [2.0, 2.0, 1.0, 1.0, 2.0], 'GSM858248': [1.0, 2.0, 2.0, 1.0, 2.0], 'GSM858249': [2.0, 1.0, 2.0, 0.0, 2.0], 'GSM858250': [1.0, 1.0, 2.0, 2.0, 2.0], 'GSM858251': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858252': [2.0, 2.0, 1.0, 1.0, 2.0], 'GSM858253': [1.0, 2.0, 2.0, 1.0, 2.0], 'GSM858254': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858255': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858256': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858257': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858258': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858259': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858260': [2.0, 1.0, 1.0, 1.0, 2.0], 'GSM858261': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858262': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858263': [2.0, 2.0, 1.0, 2.0, 2.0], 'GSM858264': [2.0, 2.0, 2.0, nan, 2.0], 'GSM858265': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858266': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858267': [2.0, 2.0, 1.0, 1.0, 2.0], 'GSM858268': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858269': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858270': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858271': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858272': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858273': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858274': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858275': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858276': [1.0, 2.0, 2.0, 1.0, 2.0], 'GSM858277': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858278': [2.0, 0.0, 1.0, 1.0, 2.0], 'GSM858279': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858280': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858281': [2.0, 2.0, 1.0, 0.0, 2.0], 'GSM858282': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858283': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858284': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858285': [2.0, 2.0, 1.0, 0.0, 2.0], 'GSM858286': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858287': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858288': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858289': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858290': [0.0, 1.0, 2.0, 1.0, 2.0], 'GSM858291': [2.0, 1.0, 2.0, 0.0, 2.0], 'GSM858292': [2.0, 1.0, 1.0, 0.0, 2.0], 'GSM858293': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858294': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858295': [2.0, 2.0, 1.0, 1.0, 2.0], 'GSM858296': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858297': [2.0, 2.0, 1.0, 1.0, 2.0], 'GSM858298': [2.0, 2.0, 1.0, 0.0, 2.0], 'GSM858299': [2.0, 1.0, 2.0, 0.0, 2.0], 'GSM858300': [2.0, 2.0, 1.0, 1.0, 2.0], 'GSM858301': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858302': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858303': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858304': [1.0, 2.0, 2.0, 0.0, 2.0], 'GSM858305': [1.0, 2.0, 2.0, 1.0, 2.0], 'GSM858306': [2.0, 2.0, 1.0, 0.0, 2.0], 'GSM858307': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858308': [1.0, 1.0, 2.0, 1.0, 2.0], 'GSM858309': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858310': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858311': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858312': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858313': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858314': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858315': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858316': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858317': [2.0, 2.0, 1.0, 1.0, 2.0], 'GSM858318': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858319': [2.0, 1.0, 2.0, 1.0, 2.0], 'GSM858320': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858321': [2.0, 1.0, 2.0, 0.0, 2.0], 'GSM858322': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858323': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858324': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858325': [2.0, 1.0, 2.0, 1.0, 2.0], 'GSM858326': [1.0, 2.0, 2.0, 0.0, 2.0], 'GSM858327': [0.0, 1.0, 2.0, 1.0, 2.0], 'GSM858328': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858329': [2.0, 1.0, 0.0, 1.0, 2.0], 'GSM858330': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858331': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858332': [2.0, 1.0, 2.0, 2.0, 2.0], 'GSM858333': [2.0, 2.0, 2.0, nan, 2.0], 'GSM858334': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858335': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858336': [2.0, 1.0, 2.0, 0.0, 2.0], 'GSM858337': [2.0, 2.0, 1.0, 2.0, 2.0], 'GSM858338': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858339': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858340': [2.0, 2.0, 1.0, 0.0, 2.0], 'GSM858341': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858342': [2.0, 1.0, 2.0, 1.0, 2.0], 'GSM858343': [2.0, 1.0, 2.0, 1.0, 2.0], 'GSM858344': [2.0, 1.0, 2.0, 2.0, 2.0], 'GSM858345': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858346': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858347': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858348': [1.0, 2.0, 2.0, 2.0, 2.0], 'GSM858349': [2.0, 2.0, 1.0, 2.0, 2.0], 'GSM858350': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858351': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858352': [2.0, 2.0, 1.0, 1.0, 2.0], 'GSM858353': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858354': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858355': [0.0, 2.0, 1.0, 1.0, 2.0], 'GSM858356': [1.0, 2.0, 2.0, 1.0, 2.0], 'GSM858357': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858358': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858359': [1.0, 2.0, 2.0, 1.0, 2.0], 'GSM858360': [1.0, 2.0, 1.0, 2.0, 2.0], 'GSM858361': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858362': [2.0, 1.0, 2.0, 0.0, 2.0], 'GSM858363': [2.0, 1.0, 1.0, 1.0, 2.0], 'GSM858364': [1.0, 1.0, 2.0, 0.0, 2.0], 'GSM858365': [1.0, 2.0, 1.0, 1.0, 2.0], 'GSM858366': [2.0, 1.0, 1.0, 0.0, 2.0], 'GSM858367': [2.0, 1.0, 2.0, 0.0, 2.0], 'GSM858368': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858369': [1.0, 2.0, 2.0, 0.0, nan], 'GSM858370': [1.0, 2.0, 1.0, 1.0, 2.0], 'GSM858371': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858372': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858373': [1.0, 2.0, 2.0, 0.0, 2.0], 'GSM858374': [2.0, 2.0, 1.0, 1.0, 2.0], 'GSM858375': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858376': [1.0, 2.0, 2.0, 1.0, 2.0], 'GSM858377': [1.0, 1.0, 2.0, 1.0, 2.0], 'GSM858378': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858379': [2.0, 2.0, 1.0, 0.0, 2.0], 'GSM858380': [1.0, 2.0, 1.0, 1.0, 2.0], 'GSM858381': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858382': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858383': [1.0, 1.0, 2.0, 1.0, 2.0], 'GSM858384': [1.0, 2.0, 1.0, 0.0, 2.0], 'GSM858385': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858386': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858387': [2.0, 0.0, 2.0, 0.0, 2.0], 'GSM858388': [2.0, 2.0, 2.0, 1.0, 2.0], 'GSM858389': [2.0, 2.0, 1.0, 1.0, 2.0], 'GSM858390': [2.0, 0.0, 1.0, 1.0, 2.0], 'GSM858391': [2.0, 2.0, 1.0, 0.0, 2.0], 'GSM858392': [2.0, 2.0, 2.0, 2.0, 2.0], 'GSM858393': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858394': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858395': [2.0, 1.0, 2.0, 1.0, 2.0], 'GSM858396': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858397': [2.0, 1.0, 2.0, 0.0, 2.0], 'GSM858398': [2.0, 2.0, 2.0, 0.0, 2.0], 'GSM858399': [0.0, 2.0, 1.0, 0.0, 2.0]}\n", "\n", "Examining the annotation data to find how SNPs map to genes...\n", "\n", "Annotation subset:\n", "{'ID': ['rs2294212', 'rs10889353', 'rs603446', 'rs5128', 'rs326'], 'Chr': [20.0, 1.0, 11.0, 11.0, 8.0], 'Position': [43973970.0, 62890783.0, 116159644.0, 116208849.0, 19863718.0]}\n", "\n", "Creating custom mapping for SNPs based on available information...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Created mapping for SNPs to genes:\n", "{'ID': ['rs2294212', 'rs10889353', 'rs603446', 'rs5128', 'rs326'], 'Gene': ['Unknown_rs2294212', 'Unknown_rs10889353', 'Unknown_rs603446', 'Unknown_rs5128', 'Unknown_rs326']}\n", "\n", "Gene data after mapping:\n", "{'GSM858200': [2.0], 'GSM858201': [2.0], 'GSM858202': [2.0], 'GSM858203': [2.0], 'GSM858204': [2.0], 'GSM858205': [2.0], 'GSM858206': [2.0], 'GSM858207': [2.0], 'GSM858208': [2.0], 'GSM858209': [2.0], 'GSM858210': [2.0], 'GSM858211': [2.0], 'GSM858212': [2.0], 'GSM858213': [2.0], 'GSM858214': [2.0], 'GSM858215': [2.0], 'GSM858216': [2.0], 'GSM858217': [1.0], 'GSM858218': [2.0], 'GSM858219': [2.0], 'GSM858220': [2.0], 'GSM858221': [2.0], 'GSM858222': [2.0], 'GSM858223': [2.0], 'GSM858224': [2.0], 'GSM858225': [2.0], 'GSM858226': [2.0], 'GSM858227': [2.0], 'GSM858228': [2.0], 'GSM858229': [2.0], 'GSM858230': [2.0], 'GSM858231': [2.0], 'GSM858232': [2.0], 'GSM858233': [2.0], 'GSM858234': [2.0], 'GSM858235': [2.0], 'GSM858236': [2.0], 'GSM858237': [2.0], 'GSM858238': [2.0], 'GSM858239': [2.0], 'GSM858240': [2.0], 'GSM858241': [1.0], 'GSM858242': [2.0], 'GSM858243': [2.0], 'GSM858244': [2.0], 'GSM858245': [2.0], 'GSM858246': [2.0], 'GSM858247': [2.0], 'GSM858248': [2.0], 'GSM858249': [2.0], 'GSM858250': [2.0], 'GSM858251': [2.0], 'GSM858252': [2.0], 'GSM858253': [2.0], 'GSM858254': [2.0], 'GSM858255': [2.0], 'GSM858256': [2.0], 'GSM858257': [2.0], 'GSM858258': [2.0], 'GSM858259': [2.0], 'GSM858260': [2.0], 'GSM858261': [2.0], 'GSM858262': [2.0], 'GSM858263': [2.0], 'GSM858264': [2.0], 'GSM858265': [2.0], 'GSM858266': [2.0], 'GSM858267': [2.0], 'GSM858268': [2.0], 'GSM858269': [2.0], 'GSM858270': [2.0], 'GSM858271': [2.0], 'GSM858272': [2.0], 'GSM858273': [2.0], 'GSM858274': [2.0], 'GSM858275': [2.0], 'GSM858276': [2.0], 'GSM858277': [2.0], 'GSM858278': [2.0], 'GSM858279': [2.0], 'GSM858280': [2.0], 'GSM858281': [2.0], 'GSM858282': [2.0], 'GSM858283': [2.0], 'GSM858284': [2.0], 'GSM858285': [2.0], 'GSM858286': [2.0], 'GSM858287': [2.0], 'GSM858288': [2.0], 'GSM858289': [2.0], 'GSM858290': [2.0], 'GSM858291': [2.0], 'GSM858292': [2.0], 'GSM858293': [2.0], 'GSM858294': [2.0], 'GSM858295': [2.0], 'GSM858296': [2.0], 'GSM858297': [2.0], 'GSM858298': [2.0], 'GSM858299': [2.0], 'GSM858300': [2.0], 'GSM858301': [2.0], 'GSM858302': [2.0], 'GSM858303': [2.0], 'GSM858304': [2.0], 'GSM858305': [2.0], 'GSM858306': [2.0], 'GSM858307': [2.0], 'GSM858308': [2.0], 'GSM858309': [2.0], 'GSM858310': [2.0], 'GSM858311': [2.0], 'GSM858312': [2.0], 'GSM858313': [2.0], 'GSM858314': [2.0], 'GSM858315': [1.0], 'GSM858316': [1.0], 'GSM858317': [2.0], 'GSM858318': [2.0], 'GSM858319': [2.0], 'GSM858320': [2.0], 'GSM858321': [2.0], 'GSM858322': [2.0], 'GSM858323': [2.0], 'GSM858324': [2.0], 'GSM858325': [2.0], 'GSM858326': [2.0], 'GSM858327': [2.0], 'GSM858328': [2.0], 'GSM858329': [2.0], 'GSM858330': [2.0], 'GSM858331': [2.0], 'GSM858332': [2.0], 'GSM858333': [2.0], 'GSM858334': [2.0], 'GSM858335': [2.0], 'GSM858336': [2.0], 'GSM858337': [2.0], 'GSM858338': [2.0], 'GSM858339': [2.0], 'GSM858340': [2.0], 'GSM858341': [2.0], 'GSM858342': [2.0], 'GSM858343': [2.0], 'GSM858344': [2.0], 'GSM858345': [2.0], 'GSM858346': [2.0], 'GSM858347': [2.0], 'GSM858348': [2.0], 'GSM858349': [2.0], 'GSM858350': [2.0], 'GSM858351': [2.0], 'GSM858352': [2.0], 'GSM858353': [2.0], 'GSM858354': [2.0], 'GSM858355': [2.0], 'GSM858356': [2.0], 'GSM858357': [2.0], 'GSM858358': [2.0], 'GSM858359': [2.0], 'GSM858360': [2.0], 'GSM858361': [2.0], 'GSM858362': [2.0], 'GSM858363': [2.0], 'GSM858364': [2.0], 'GSM858365': [2.0], 'GSM858366': [2.0], 'GSM858367': [2.0], 'GSM858368': [2.0], 'GSM858369': [2.0], 'GSM858370': [2.0], 'GSM858371': [2.0], 'GSM858372': [2.0], 'GSM858373': [2.0], 'GSM858374': [2.0], 'GSM858375': [2.0], 'GSM858376': [2.0], 'GSM858377': [2.0], 'GSM858378': [2.0], 'GSM858379': [2.0], 'GSM858380': [2.0], 'GSM858381': [2.0], 'GSM858382': [2.0], 'GSM858383': [2.0], 'GSM858384': [2.0], 'GSM858385': [2.0], 'GSM858386': [2.0], 'GSM858387': [2.0], 'GSM858388': [2.0], 'GSM858389': [2.0], 'GSM858390': [2.0], 'GSM858391': [2.0], 'GSM858392': [2.0], 'GSM858393': [2.0], 'GSM858394': [2.0], 'GSM858395': [2.0], 'GSM858396': [2.0], 'GSM858397': [2.0], 'GSM858398': [2.0], 'GSM858399': [2.0]}\n", "\n", "Gene data saved to ../../output/preprocess/LDL_Cholesterol_Levels/gene_data/GSE34945.csv\n" ] } ], "source": [ "# Looking at the genetic data, we can see it contains SNP genotypes (AA, AB, BB, NC)\n", "# We need to convert these to numeric values before mapping to genes\n", "\n", "# Convert genotypes to numeric values\n", "def convert_genotype_to_numeric(genotype):\n", " if genotype == 'AA':\n", " return 0\n", " elif genotype == 'AB':\n", " return 1\n", " elif genotype == 'BB':\n", " return 2\n", " else: # NC or any other value\n", " return float('nan')\n", "\n", "# Make a copy of the genetic data and convert genotypes to numeric values\n", "numeric_gene_data = gene_data.copy()\n", "for col in numeric_gene_data.columns:\n", " numeric_gene_data[col] = numeric_gene_data[col].apply(convert_genotype_to_numeric)\n", "\n", "print(\"\\nGenetic data after numeric conversion (preview):\")\n", "print(preview_df(numeric_gene_data))\n", "\n", "print(\"\\nExamining the annotation data to find how SNPs map to genes...\")\n", "\n", "# Extract basic annotation columns\n", "annotation_subset = gene_annotation[['ID', 'Chr', 'Position']]\n", "print(\"\\nAnnotation subset:\")\n", "print(preview_df(annotation_subset))\n", "\n", "# Create a custom mapping for SNPs to APOC3 and related genes\n", "print(\"\\nCreating custom mapping for SNPs based on available information...\")\n", "\n", "# Create a mapping dictionary - in a real application this would use\n", "# a genomic database to map coordinates to genes, but here we'll use a simplified approach\n", "snp_gene_map = {}\n", "\n", "# For SNPs on chromosome 11 near APOC3 (116208649-116223862), map to APOC3\n", "# For any SNPs on chromosome 8 near LPL (19841058-19869050), map to LPL \n", "# For SNPs on chromosome 19 near APOE (45409039-45412650), map to APOE\n", "# These are approximations based on genomic locations of lipid metabolism genes\n", "for idx, row in annotation_subset.iterrows():\n", " snp_id = row['ID']\n", " chrom = row['Chr']\n", " pos = row['Position']\n", " \n", " # Map SNPs to genes based on their genomic locations\n", " # This is a simplified mapping and would require a more robust approach in practice\n", " if pd.notna(chrom) and pd.notna(pos):\n", " if chrom == 11 and 116180000 <= pos <= 116230000:\n", " snp_gene_map[snp_id] = \"APOC3\"\n", " elif chrom == 8 and 19800000 <= pos <= 19900000:\n", " snp_gene_map[snp_id] = \"LPL\"\n", " elif chrom == 19 and 45300000 <= pos <= 45500000:\n", " snp_gene_map[snp_id] = \"APOE\"\n", " elif chrom == 1 and 62700000 <= pos <= 63000000:\n", " snp_gene_map[snp_id] = \"ANGPTL3\"\n", " else:\n", " # For SNPs without a clear gene association, assign a placeholder value\n", " snp_gene_map[snp_id] = f\"SNP_chr{int(chrom)}_{int(pos)}\"\n", " else:\n", " snp_gene_map[snp_id] = f\"Unknown_{snp_id}\"\n", "\n", "# Create a mapping DataFrame\n", "mapping_df = pd.DataFrame({'ID': list(snp_gene_map.keys()), \n", " 'Gene': list(snp_gene_map.values())})\n", "\n", "# Filter to only include SNPs that are in our gene_data\n", "mapping_df = mapping_df[mapping_df['ID'].isin(numeric_gene_data.index)]\n", "\n", "print(\"\\nCreated mapping for SNPs to genes:\")\n", "print(preview_df(mapping_df))\n", "\n", "# Apply gene mapping to convert SNP genotypes to gene-based data\n", "gene_data = apply_gene_mapping(numeric_gene_data, mapping_df)\n", "\n", "# Preview the resulting gene-based data\n", "print(\"\\nGene data after mapping:\")\n", "print(preview_df(gene_data))\n", "\n", "# Save the gene 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\"\\nGene data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "a9fe4d42", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "82566213", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:28:00.993045Z", "iopub.status.busy": "2025-03-25T07:28:00.992925Z", "iopub.status.idle": "2025-03-25T07:28:01.021633Z", "shell.execute_reply": "2025-03-25T07:28:01.021298Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Normalizing gene symbols...\n", "Loaded gene data with 1 genes and 1219 samples\n", "Current gene data shape: 1 genes and 1219 samples\n", "Gene expression data available: False\n", "Note: This dataset contains SNP genotypes (AA/AB/BB), not gene expression measurements.\n", "\n", "Loading previously processed clinical data...\n", "Loaded clinical data with 1 rows and 30 columns\n", "Trait information available: True\n", "\n", "Clinical data preview:\n", "{'0': [5.298013245], '1': [-47.59825328], '2': [-35.94470046], '3': [-23.8372093], '4': [-31.57894737], '5': [-20.83333333], '6': [-41.66666667], '7': [-27.92792793], '8': [-26.76056338], '9': [-32.11382114], '10': [-24.06417112], '11': [-14.48275862], '12': [-18.23899371], '13': [-35.31914894], '14': [-29.77099237], '15': [-36.95652174], '16': [-27.91666667], '17': [-8.02919708], '18': [-27.81065089], '19': [-29.76190476], '20': [-24.87309645], '21': [-29.8245614], '22': [-53.27510917], '23': [-7.352941176], '24': [-27.40384615], '25': [-26.9058296], '26': [-39.92395437], '27': [-40.75829384], '28': [-8.888888889], '29': [-6.640625]}\n", "Number of unique trait values: 1\n", "Warning: The trait data contains only a single unique value.\n", "\n", "Performing final validation...\n", "\n", "Dataset usability for LDL_Cholesterol_Levels association studies: False\n", "Reason: Dataset contains SNP genotypes (AA/AB/BB), not gene expression measurements suitable for LDL cholesterol gene expression analysis.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "print(\"\\nNormalizing gene symbols...\")\n", "try:\n", " # Load gene data that was saved in step 6\n", " gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n", " print(f\"Loaded gene data with {gene_data.shape[0]} genes and {gene_data.shape[1]} samples\")\n", " \n", " # This is SNP genotype data, not gene expression data\n", " # We should keep the original SNP IDs and not attempt to normalize as gene symbols\n", " print(f\"Current gene data shape: {gene_data.shape[0]} genes and {gene_data.shape[1]} samples\")\n", " \n", " # Set is_gene_available to False as this is SNP data, not gene expression data\n", " is_gene_available = False\n", " print(f\"Gene expression data available: {is_gene_available}\")\n", " print(\"Note: This dataset contains SNP genotypes (AA/AB/BB), not gene expression measurements.\")\n", "except Exception as e:\n", " print(f\"Error processing gene data: {e}\")\n", " is_gene_available = False\n", "\n", "# 2. Load the previously processed clinical data\n", "print(\"\\nLoading previously processed clinical data...\")\n", "try:\n", " # Load the clinical data that was saved in step 2\n", " clinical_df = pd.read_csv(out_clinical_data_file)\n", " print(f\"Loaded clinical data with {clinical_df.shape[0]} rows and {clinical_df.shape[1]} columns\")\n", " \n", " # Check if we have valid trait information\n", " is_trait_available = not clinical_df.empty\n", " print(f\"Trait information available: {is_trait_available}\")\n", " \n", " # Preview clinical data\n", " print(\"\\nClinical data preview:\")\n", " print(preview_df(clinical_df))\n", " \n", " # Check for single value bias in the trait data\n", " if is_trait_available and len(clinical_df) > 0:\n", " unique_values = clinical_df.iloc[:,0].unique()\n", " print(f\"Number of unique trait values: {len(unique_values)}\")\n", " if len(unique_values) == 1:\n", " print(\"Warning: The trait data contains only a single unique value.\")\n", " is_biased = True\n", " else:\n", " # We'll set this preliminarily, but will evaluate properly later\n", " is_biased = False\n", " else:\n", " is_biased = True\n", " \n", "except Exception as e:\n", " print(f\"Error loading clinical data: {e}\")\n", " is_trait_available = False\n", " clinical_df = pd.DataFrame()\n", " is_biased = True\n", "\n", "# We don't need to link clinical and genetic data as we've determined\n", "# this dataset contains SNP data, not gene expression data suitable for our analysis\n", "\n", "# Validate and save cohort information\n", "print(\"\\nPerforming final validation...\")\n", "note = \"Dataset contains SNP genotypes (AA/AB/BB), not gene expression measurements suitable for LDL cholesterol gene expression analysis.\"\n", "\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=clinical_df if not clinical_df.empty else pd.DataFrame({trait: []}),\n", " note=note\n", ")\n", "\n", "# Data is not usable for our gene expression analysis\n", "print(f\"\\nDataset usability for {trait} association studies: {is_usable}\")\n", "print(f\"Reason: {note}\")" ] } ], "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 }