{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "88681485", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:15:12.327094Z", "iopub.status.busy": "2025-03-25T08:15:12.326842Z", "iopub.status.idle": "2025-03-25T08:15:12.496884Z", "shell.execute_reply": "2025-03-25T08:15:12.496428Z" } }, "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 = \"Chronic_Fatigue_Syndrome\"\n", "cohort = \"GSE67311\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Chronic_Fatigue_Syndrome\"\n", "in_cohort_dir = \"../../input/GEO/Chronic_Fatigue_Syndrome/GSE67311\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Chronic_Fatigue_Syndrome/GSE67311.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE67311.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Chronic_Fatigue_Syndrome/clinical_data/GSE67311.csv\"\n", "json_path = \"../../output/preprocess/Chronic_Fatigue_Syndrome/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "f5255a4a", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "ace81830", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:15:12.498336Z", "iopub.status.busy": "2025-03-25T08:15:12.498005Z", "iopub.status.idle": "2025-03-25T08:15:12.709014Z", "shell.execute_reply": "2025-03-25T08:15:12.708533Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Peripheral Blood Gene Expression in Fibromyalgia Patients Reveals Potential Biological Markers and Physiological Pathways\"\n", "!Series_summary\t\"Fibromyalgia (FM) is a common pain disorder characterized by dysregulation in the processing of pain. Although FM has similarities with other rheumatologic pain disorders, the search for objective markers has not been successful. In the current study we analyzed gene expression in the whole blood of 70 fibromyalgia patients and 70 healthy matched controls. Global molecular profiling revealed an upregulation of several inflammatory molecules in FM patients and downregulation of specific pathways related to hypersensitivity and allergy. There was a differential expression of genes in known pathways for pain processing, such as glutamine/glutamate signaling and axonal development. We also identified a panel of candidate gene expression-based classifiers that could establish an objective blood-based molecular diagnostic to objectively identify FM patients and guide design and testing of new therapies. Ten classifier probesets (CPA3, C11orf83, LOC100131943, RGS17, PARD3B, ANKRD20A9P, TTLL7, C8orf12, KAT2B and RIOK3) provided a diagnostic sensitivity of 95% and a specificity of 96%. Molecular scores developed from these classifiers were able to clearly distinguish FM patients from healthy controls. An understanding of molecular dysregulation in fibromyalgia is in its infancy; however the results described herein indicate blood global gene expression profiling provides many testable hypotheses that deserve further exploration.\"\n", "!Series_overall_design\t\"Blood samples were collected in PAXgene tubes and collected samples were stored at -80oC. The RNA was isolated using the PAXgene RNA isolation kit according to standard protocols. Total RNA was quantified on a Nanodrop spectrophotometer and visualized for quality on an Agilent Bioanalyzer. Samples with an average RIN (RNA Integrity Number) >8, indicating good quality RNA, were processed. 200ng of total RNA was amplified and then hybridized to Affymetrix® Human Gene 1.1 ST Peg arrays using standard manufacturer’s protocols on a Gene Titan MC instrument. Data was analyzed using Partek Genomics Suite (version 6.6) using RMA normalization. All genes with Log2 signal intensity less than 4.8 were excluded from analysis based on low expression. Differential expression analysis was carried out using a one way ANOVA by using Method of Moments and Fisher's Least Significant Difference (LSD) tests with a threshold p-value <0.005 for the biological and molecular function analyses, and a more conservative threshold p-value <0.001 (FDR q-value range 0.002% to 13%) for candidate diagnostic signatures.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['diagnosis: healthy control', 'diagnosis: fibromyalgia'], 1: ['tissue: peripheral blood'], 2: ['fiqr score: 8.5', 'fiqr score: -2.0', 'fiqr score: 9.8', 'fiqr score: 0.5', 'fiqr score: -1.0', 'fiqr score: -0.5', 'fiqr score: 2.2', 'fiqr score: 15.3', 'fiqr score: 4.0', 'fiqr score: 29.3', 'fiqr score: 27.2', 'fiqr score: 5.0', 'fiqr score: 1.0', 'fiqr score: 2.5', 'fiqr score: 3.0', 'fiqr score: -1.5', 'fiqr score: 1.3', 'fiqr score: 21.7', 'fiqr score: -1.2', 'fiqr score: 4.3', 'fiqr score: 6.5', 'fiqr score: 2.0', 'fiqr score: 11.7', 'fiqr score: 15.0', 'fiqr score: 6.0', 'fiqr score: 14.2', 'fiqr score: -0.2', 'fiqr score: 12.8', 'fiqr score: 15.7', 'fiqr score: 0.0'], 3: ['bmi: 36', 'bmi: 34', 'bmi: 33', 'bmi: 22', 'bmi: 24', 'bmi: 28', 'bmi: 23', 'bmi: 48', 'bmi: 25', 'bmi: 46', 'bmi: 32', 'bmi: 31', 'bmi: 21', 'bmi: 27', 'bmi: 39', 'bmi: 52', 'bmi: 37', 'bmi: 0', 'bmi: 38', 'bmi: 26', 'bmi: 42', 'bmi: 20', 'bmi: 30', 'bmi: 43', 'bmi: 35', 'bmi: 44', 'bmi: 29', 'bmi: 45', 'bmi: 40', 'bmi: 47'], 4: ['migraine: No', 'migraine: Yes', 'migraine: -'], 5: ['irritable bowel syndrome: No', 'irritable bowel syndrome: Yes', 'irritable bowel syndrome: -'], 6: ['major depression: No', 'major depression: -', 'major depression: Yes'], 7: ['bipolar disorder: No', 'bipolar disorder: -', 'bipolar disorder: Yes'], 8: ['chronic fatigue syndrome: No', nan, 'chronic fatigue syndrome: -', 'chronic fatigue syndrome: Yes']}\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": "5f31aa39", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "8ec2bc9c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:15:12.710427Z", "iopub.status.busy": "2025-03-25T08:15:12.710312Z", "iopub.status.idle": "2025-03-25T08:15:12.715054Z", "shell.execute_reply": "2025-03-25T08:15:12.714638Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample characteristics file not found at ../../input/GEO/Chronic_Fatigue_Syndrome/GSE67311/sample_characteristics.csv\n", "Cannot extract clinical features without sample data.\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# This is likely gene expression data as it mentions \"Blood global gene expression profiling\" and\n", "# \"Affymetrix® Human Gene 1.1 ST Peg arrays\" in the background information\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "\n", "# 2.1 For trait: Chronic Fatigue Syndrome\n", "# From the sample characteristics dictionary, we can see row 8 contains information about CFS\n", "trait_row = 8\n", "\n", "# 2.2 Trait conversion function\n", "def convert_trait(value):\n", " if pd.isna(value):\n", " return None\n", " if isinstance(value, str):\n", " value = value.lower()\n", " if \"yes\" in value:\n", " return 1\n", " elif \"no\" in value:\n", " return 0\n", " else:\n", " return None\n", " return None\n", "\n", "# Age is not available in the dataset\n", "age_row = None\n", "def convert_age(value):\n", " return None\n", "\n", "# Gender is not available in the dataset\n", "gender_row = None\n", "def convert_gender(value):\n", " return None\n", "\n", "# 3. Save Metadata\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "if trait_row is not None:\n", " # Look for the sample characteristics file in the cohort directory\n", " sample_char_file = os.path.join(in_cohort_dir, \"sample_characteristics.csv\")\n", " \n", " if os.path.exists(sample_char_file):\n", " clinical_data = pd.read_csv(sample_char_file)\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", " 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, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " else:\n", " print(f\"Sample characteristics file not found at {sample_char_file}\")\n", " print(\"Cannot extract clinical features without sample data.\")\n" ] }, { "cell_type": "markdown", "id": "c454aad6", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "f8c87309", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:15:12.716429Z", "iopub.status.busy": "2025-03-25T08:15:12.716320Z", "iopub.status.idle": "2025-03-25T08:15:13.075545Z", "shell.execute_reply": "2025-03-25T08:15:13.074988Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 66\n", "Header line: \"ID_REF\"\t\"GSM1644447\"\t\"GSM1644448\"\t\"GSM1644449\"\t\"GSM1644450\"\t\"GSM1644451\"\t\"GSM1644452\"\t\"GSM1644453\"\t\"GSM1644454\"\t\"GSM1644455\"\t\"GSM1644456\"\t\"GSM1644457\"\t\"GSM1644458\"\t\"GSM1644459\"\t\"GSM1644460\"\t\"GSM1644461\"\t\"GSM1644462\"\t\"GSM1644463\"\t\"GSM1644464\"\t\"GSM1644465\"\t\"GSM1644466\"\t\"GSM1644467\"\t\"GSM1644468\"\t\"GSM1644469\"\t\"GSM1644470\"\t\"GSM1644471\"\t\"GSM1644472\"\t\"GSM1644473\"\t\"GSM1644474\"\t\"GSM1644475\"\t\"GSM1644476\"\t\"GSM1644477\"\t\"GSM1644478\"\t\"GSM1644479\"\t\"GSM1644480\"\t\"GSM1644481\"\t\"GSM1644482\"\t\"GSM1644483\"\t\"GSM1644484\"\t\"GSM1644485\"\t\"GSM1644486\"\t\"GSM1644487\"\t\"GSM1644488\"\t\"GSM1644489\"\t\"GSM1644490\"\t\"GSM1644491\"\t\"GSM1644492\"\t\"GSM1644493\"\t\"GSM1644494\"\t\"GSM1644495\"\t\"GSM1644496\"\t\"GSM1644497\"\t\"GSM1644498\"\t\"GSM1644499\"\t\"GSM1644500\"\t\"GSM1644501\"\t\"GSM1644502\"\t\"GSM1644503\"\t\"GSM1644504\"\t\"GSM1644505\"\t\"GSM1644506\"\t\"GSM1644507\"\t\"GSM1644508\"\t\"GSM1644509\"\t\"GSM1644510\"\t\"GSM1644511\"\t\"GSM1644512\"\t\"GSM1644513\"\t\"GSM1644514\"\t\"GSM1644515\"\t\"GSM1644516\"\t\"GSM1644517\"\t\"GSM1644518\"\t\"GSM1644519\"\t\"GSM1644520\"\t\"GSM1644521\"\t\"GSM1644522\"\t\"GSM1644523\"\t\"GSM1644524\"\t\"GSM1644525\"\t\"GSM1644526\"\t\"GSM1644527\"\t\"GSM1644528\"\t\"GSM1644529\"\t\"GSM1644530\"\t\"GSM1644531\"\t\"GSM1644532\"\t\"GSM1644533\"\t\"GSM1644534\"\t\"GSM1644535\"\t\"GSM1644536\"\t\"GSM1644537\"\t\"GSM1644538\"\t\"GSM1644539\"\t\"GSM1644540\"\t\"GSM1644541\"\t\"GSM1644542\"\t\"GSM1644543\"\t\"GSM1644544\"\t\"GSM1644545\"\t\"GSM1644546\"\t\"GSM1644547\"\t\"GSM1644548\"\t\"GSM1644549\"\t\"GSM1644550\"\t\"GSM1644551\"\t\"GSM1644552\"\t\"GSM1644553\"\t\"GSM1644554\"\t\"GSM1644555\"\t\"GSM1644556\"\t\"GSM1644557\"\t\"GSM1644558\"\t\"GSM1644559\"\t\"GSM1644560\"\t\"GSM1644561\"\t\"GSM1644562\"\t\"GSM1644563\"\t\"GSM1644564\"\t\"GSM1644565\"\t\"GSM1644566\"\t\"GSM1644567\"\t\"GSM1644568\"\t\"GSM1644569\"\t\"GSM1644570\"\t\"GSM1644571\"\t\"GSM1644572\"\t\"GSM1644573\"\t\"GSM1644574\"\t\"GSM1644575\"\t\"GSM1644576\"\t\"GSM1644577\"\t\"GSM1644578\"\t\"GSM1644579\"\t\"GSM1644580\"\t\"GSM1644581\"\t\"GSM1644582\"\t\"GSM1644583\"\t\"GSM1644584\"\t\"GSM1644585\"\t\"GSM1644586\"\t\"GSM1644587\"\t\"GSM1644588\"\n", "First data line: 7892501\t5.62341\t4.54841\t4.74053\t3.06227\t3.65178\t4.34336\t5.36535\t3.69126\t2.52748\t4.45481\t2.92058\t3.8511\t5.12552\t5.573\t4.0534\t4.01826\t3.13732\t4.08528\t2.9814\t4.50242\t3.11999\t4.39195\t3.29422\t4.65377\t4.45319\t3.90496\t5.07445\t4.45871\t3.40056\t5.43057\t3.17105\t4.24734\t3.9472\t3.26099\t4.21569\t4.80915\t3.99664\t4.83605\t3.98062\t3.68376\t4.43473\t4.48863\t5.0855\t4.75533\t3.87273\t2.65504\t3.14636\t2.8747\t2.94444\t4.67516\t5.74588\t4.40772\t4.93351\t3.68102\t4.70309\t6.77148\t3.79405\t3.50168\t4.82181\t5.26454\t5.43154\t3.56926\t4.88201\t3.77941\t4.74896\t4.85282\t3.16368\t5.73479\t4.22191\t3.30515\t3.3804\t3.91636\t5.19594\t3.73744\t3.039\t2.4157\t3.89391\t4.50269\t4.21075\t5.12803\t3.3515\t3.2859\t3.41076\t5.35577\t5.0399\t5.26434\t5.07121\t4.81385\t4.70926\t5.03955\t3.10709\t3.46736\t3.10186\t5.22351\t3.17449\t3.98248\t3.41802\t2.61387\t2.19567\t3.53848\t2.38796\t3.72276\t4.78528\t4.06687\t3.24888\t2.38341\t2.66362\t3.56916\t3.15337\t4.0438\t4.54457\t4.82199\t3.59462\t4.18813\t3.95037\t4.82841\t3.73593\t4.82214\t4.17745\t4.01625\t4.42865\t4.15125\t3.63591\t2.76813\t3.24467\t4.59799\t5.77069\t5.22108\t5.2745\t4.68387\t4.87527\t5.47274\t2.93373\t4.78499\t2.86791\t5.63311\t4.38304\t3.72055\t3.03068\t4.61205\t5.38301\t3.56539\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Index(['7892501', '7892502', '7892503', '7892504', '7892505', '7892506',\n", " '7892507', '7892508', '7892509', '7892510', '7892511', '7892512',\n", " '7892513', '7892514', '7892515', '7892516', '7892517', '7892518',\n", " '7892519', '7892520'],\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": "85a1f188", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "c12a2c9a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:15:13.077273Z", "iopub.status.busy": "2025-03-25T08:15:13.077139Z", "iopub.status.idle": "2025-03-25T08:15:13.079439Z", "shell.execute_reply": "2025-03-25T08:15:13.079010Z" } }, "outputs": [], "source": [ "# The identifiers in the gene expression data appear to be probe IDs (like \"7892501\"), not human gene symbols\n", "# These are likely to be Illumina microarray probe IDs that need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "e3b9b5b2", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "d79cc27c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:15:13.081028Z", "iopub.status.busy": "2025-03-25T08:15:13.080880Z", "iopub.status.idle": "2025-03-25T08:15:19.375899Z", "shell.execute_reply": "2025-03-25T08:15:19.375533Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['7896736', '7896738', '7896740', '7896742', '7896744'], 'GB_LIST': [nan, nan, 'NM_001005240,NM_001004195,NM_001005484,BC136848,BC136907', 'BC118988,AL137655', 'NM_001005277,NM_001005221,NM_001005224,NM_001005504,BC137547'], 'SPOT_ID': ['chr1:53049-54936', 'chr1:63015-63887', 'chr1:69091-70008', 'chr1:334129-334296', 'chr1:367659-368597'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10'], 'RANGE_STRAND': ['+', '+', '+', '+', '+'], 'RANGE_START': [53049.0, 63015.0, 69091.0, 334129.0, 367659.0], 'RANGE_STOP': [54936.0, 63887.0, 70008.0, 334296.0, 368597.0], 'total_probes': [7.0, 31.0, 24.0, 6.0, 36.0], 'gene_assignment': ['---', '---', 'NM_001005240 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// NM_001004195 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000318050 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// ENST00000335137 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// ENST00000326183 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// BC136848 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136907 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// ENST00000442916 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099', 'ENST00000388975 // SEPT14 // septin 14 // 7p11.2 // 346288 /// BC118988 // NCRNA00266 // non-protein coding RNA 266 // --- // 140849 /// AL137655 // LOC100134822 // similar to hCG1739109 // --- // 100134822', 'NM_001005277 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// NM_001005221 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// NM_001005224 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// NM_001005504 // OR4F21 // olfactory receptor, family 4, subfamily F, member 21 // 8p23.3 // 441308 /// ENST00000320901 // OR4F21 // olfactory receptor, family 4, subfamily F, member 21 // 8p23.3 // 441308 /// BC137547 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// BC137547 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// BC137547 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759'], 'mrna_assignment': ['---', 'ENST00000328113 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:15:102467008:102467910:-1 gene:ENSG00000183909 // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000318181 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:19:104601:105256:1 gene:ENSG00000176705 // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000492842 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:62948:63887:1 gene:ENSG00000240361 // chr1 // 100 // 100 // 31 // 31 // 0', 'NM_001005240 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 17 (OR4F17), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001004195 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 4 (OR4F4), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000318050 // ENSEMBL // Olfactory receptor 4F17 gene:ENSG00000176695 // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000335137 // ENSEMBL // Olfactory receptor 4F4 gene:ENSG00000186092 // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000326183 // ENSEMBL // Olfactory receptor 4F5 gene:ENSG00000177693 // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136848 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168462 IMAGE:9020839), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136907 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168521 IMAGE:9020898), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000442916 // ENSEMBL // OR4F4 (Fragment) gene:ENSG00000176695 // chr1 // 100 // 88 // 21 // 21 // 0', 'ENST00000388975 // ENSEMBL // Septin-14 gene:ENSG00000154997 // chr1 // 50 // 100 // 3 // 6 // 0 /// BC118988 // GenBank // Homo sapiens chromosome 20 open reading frame 69, mRNA (cDNA clone MGC:141807 IMAGE:40035995), complete cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// AL137655 // GenBank // Homo sapiens mRNA; cDNA DKFZp434B2016 (from clone DKFZp434B2016). // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000428915 // ENSEMBL // cdna:known chromosome:GRCh37:10:38742109:38755311:1 gene:ENSG00000203496 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000455207 // ENSEMBL // cdna:known chromosome:GRCh37:1:334129:446155:1 gene:ENSG00000224813 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000455464 // ENSEMBL // cdna:known chromosome:GRCh37:1:334140:342806:1 gene:ENSG00000224813 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000440200 // ENSEMBL // cdna:known chromosome:GRCh37:1:536816:655580:-1 gene:ENSG00000230021 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000279067 // ENSEMBL // cdna:known chromosome:GRCh37:20:62921738:62934912:1 gene:ENSG00000149656 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000499986 // ENSEMBL // cdna:known chromosome:GRCh37:5:180717576:180761371:1 gene:ENSG00000248628 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000436899 // ENSEMBL // cdna:known chromosome:GRCh37:6:131910:144885:-1 gene:ENSG00000170590 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000432557 // ENSEMBL // cdna:known chromosome:GRCh37:8:132324:150572:-1 gene:ENSG00000250210 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000523795 // ENSEMBL // cdna:known chromosome:GRCh37:8:141690:150563:-1 gene:ENSG00000250210 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000490482 // ENSEMBL // cdna:known chromosome:GRCh37:8:149942:163324:-1 gene:ENSG00000223508 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000307499 // ENSEMBL // cdna:known supercontig::GL000227.1:57780:70752:-1 gene:ENSG00000229450 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000441245 // ENSEMBL // cdna:known chromosome:GRCh37:1:637316:655530:-1 gene:ENSG00000230021 // chr1 // 100 // 67 // 4 // 4 // 0 /// ENST00000425473 // ENSEMBL // cdna:known chromosome:GRCh37:20:62926294:62944485:1 gene:ENSG00000149656 // chr1 // 100 // 67 // 4 // 4 // 0 /// ENST00000471248 // ENSEMBL // cdna:known chromosome:GRCh37:1:110953:129173:-1 gene:ENSG00000238009 // chr1 // 75 // 67 // 3 // 4 // 0', 'NM_001005277 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 16 (OR4F16), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005221 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 29 (OR4F29), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005224 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 3 (OR4F3), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005504 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 21 (OR4F21), mRNA. // chr1 // 89 // 100 // 32 // 36 // 0 /// ENST00000320901 // ENSEMBL // Olfactory receptor 4F21 gene:ENSG00000176269 // chr1 // 89 // 100 // 32 // 36 // 0 /// BC137547 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 3, mRNA (cDNA clone MGC:169170 IMAGE:9021547), complete cds. // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000426406 // ENSEMBL // cdna:known chromosome:GRCh37:1:367640:368634:1 gene:ENSG00000235249 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000332831 // ENSEMBL // cdna:known chromosome:GRCh37:1:621096:622034:-1 gene:ENSG00000185097 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000456475 // ENSEMBL // cdna:known chromosome:GRCh37:5:180794269:180795263:1 gene:ENSG00000230178 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000521196 // ENSEMBL // cdna:known chromosome:GRCh37:11:86612:87605:-1 gene:ENSG00000224777 // chr1 // 78 // 100 // 28 // 36 // 0'], 'category': ['---', 'main', 'main', 'main', 'main']}\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": "75abfd6c", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "47d9ec2c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:15:19.377329Z", "iopub.status.busy": "2025-03-25T08:15:19.377211Z", "iopub.status.idle": "2025-03-25T08:17:28.832767Z", "shell.execute_reply": "2025-03-25T08:17:28.832134Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape: (33297, 142)\n", "First few probe IDs: ['7892501', '7892502', '7892503', '7892504', '7892505']\n", "Creating gene mapping from probe IDs to gene symbols...\n", "\n", "Probe ID overlap: 33297 out of 33297 expression probes\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Filtered mapping dataframe contains 4761471 rows\n", "\n", "Extracting gene symbols from gene assignments...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Sample of extracted gene symbols (first 5 rows):\n", " ID Gene\n", "0 7896736 []\n", "1 7896738 []\n", "2 7896740 [OR4F17, OR4F4, OR4F5, BC136848, BC136907]\n", "3 7896742 [SEPT14, BC118988, NCRNA00266, AL137655]\n", "4 7896744 [OR4F16, OR4F29, OR4F3, OR4F21, BC137547]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Number of probes with gene mappings: 22011 out of 4761471 total probes\n", "\n", "Applying gene mapping to convert probe data to gene expression data...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Mapped gene expression data shape: (0, 142)\n", "\n", "WARNING: Still no mapped genes. Attempting direct mapping of most common genes...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Created direct mapping with 447055 probe-to-gene mappings\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Direct mapped gene expression data shape: (55006, 142)\n", "\n", "Normalizing gene symbols...\n", "Final gene data shape after normalization: (19854, 142)\n", "\n", "Final gene expression data shape: (19854, 142)\n", "First 10 gene symbols: ['A1BG', 'A1CF', 'A2M', 'A2ML1', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS', 'AADAC']\n", "\n", "Saving gene expression data to CSV file...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE67311.csv\n" ] } ], "source": [ "# 1. Ensure we have the gene expression data loaded properly\n", "gene_data = get_genetic_data(matrix_file)\n", "print(f\"Gene expression data shape: {gene_data.shape}\")\n", "print(f\"First few probe IDs: {list(gene_data.index[:5])}\")\n", "\n", "# 2. Examine the gene annotation structure and prepare the mapping\n", "print(\"Creating gene mapping from probe IDs to gene symbols...\")\n", "\n", "# Check the relationship between gene expression probe IDs and annotation IDs\n", "gene_ids = set(gene_data.index.astype(str))\n", "annotation_ids = set(gene_annotation['ID'].astype(str))\n", "overlap = gene_ids.intersection(annotation_ids)\n", "print(f\"\\nProbe ID overlap: {len(overlap)} out of {len(gene_ids)} expression probes\")\n", "\n", "# Create a mapping dataframe, focusing on IDs that appear in our gene expression data\n", "mapping_df = gene_annotation[gene_annotation['ID'].astype(str).isin(gene_ids)][['ID', 'gene_assignment']].copy()\n", "print(f\"Filtered mapping dataframe contains {len(mapping_df)} rows\")\n", "\n", "# Extract gene symbols from gene assignments\n", "print(\"\\nExtracting gene symbols from gene assignments...\")\n", "mapping_df['Gene'] = mapping_df['gene_assignment'].apply(extract_human_gene_symbols)\n", "\n", "# Print a sample of the extracted gene symbols\n", "print(\"\\nSample of extracted gene symbols (first 5 rows):\")\n", "print(mapping_df[['ID', 'Gene']].head(5))\n", "\n", "# Count how many mapped probes have gene assignments\n", "mappable_probes = mapping_df[mapping_df['Gene'].apply(len) > 0].shape[0]\n", "print(f\"\\nNumber of probes with gene mappings: {mappable_probes} out of {mapping_df.shape[0]} total probes\")\n", "\n", "# Check if we have any valid mappings\n", "if mappable_probes == 0:\n", " print(\"\\nWARNING: No valid gene mappings found. Using a fallback approach...\")\n", " # Fallback: Try exact string matching for probe IDs\n", " mapping_df = gene_annotation[['ID', 'gene_assignment']].copy()\n", " mapping_df['ID'] = mapping_df['ID'].astype(str)\n", " mapping_df['Gene'] = mapping_df['gene_assignment'].apply(extract_human_gene_symbols)\n", " # Only keep rows with extracted gene symbols\n", " mapping_df = mapping_df[mapping_df['Gene'].apply(len) > 0]\n", " print(f\"Fallback mapping contains {len(mapping_df)} rows with gene symbols\")\n", "\n", "# Apply gene mapping to get gene expression data\n", "print(\"\\nApplying gene mapping to convert probe data to gene expression data...\")\n", "gene_data_mapped = apply_gene_mapping(gene_data, mapping_df[['ID', 'Gene']])\n", "print(f\"Mapped gene expression data shape: {gene_data_mapped.shape}\")\n", "\n", "# If we still don't have any genes, try a more direct approach\n", "if gene_data_mapped.shape[0] == 0:\n", " print(\"\\nWARNING: Still no mapped genes. Attempting direct mapping of most common genes...\")\n", " # Direct mapping approach\n", " import re\n", " \n", " # We'll create a simple mapping from each probe to potential genes\n", " direct_mappings = {}\n", " for idx, row in gene_annotation.iterrows():\n", " probe_id = str(row['ID'])\n", " gene_text = str(row['gene_assignment'])\n", " # Extract gene symbols - common pattern: SYMBOL // DESCRIPTION\n", " matches = re.findall(r'(\\w+) // ', gene_text)\n", " if matches:\n", " direct_mappings[probe_id] = matches\n", " \n", " # Create a new mapping dataframe\n", " direct_map_records = []\n", " for probe_id, genes in direct_mappings.items():\n", " for gene in genes:\n", " if gene not in ['NM', 'BC', 'ENST', 'NC'] and len(gene) > 1: # Filter out common prefixes\n", " direct_map_records.append({'ID': probe_id, 'Gene': gene})\n", " \n", " if direct_map_records:\n", " direct_mapping_df = pd.DataFrame(direct_map_records)\n", " print(f\"Created direct mapping with {len(direct_mapping_df)} probe-to-gene mappings\")\n", " gene_data_mapped = apply_gene_mapping(gene_data, direct_mapping_df)\n", " print(f\"Direct mapped gene expression data shape: {gene_data_mapped.shape}\")\n", "\n", "# Normalize gene symbols using standard function or keep as is if still empty\n", "if gene_data_mapped.shape[0] > 0:\n", " print(\"\\nNormalizing gene symbols...\")\n", " gene_data = normalize_gene_symbols_in_index(gene_data_mapped)\n", " print(f\"Final gene data shape after normalization: {gene_data.shape}\")\n", "else:\n", " print(\"\\nWARNING: Could not map any genes. Creating an empty gene expression dataset.\")\n", " gene_data = pd.DataFrame(columns=gene_data.columns)\n", "\n", "# Print information about the resulting gene expression data\n", "print(f\"\\nFinal gene expression data shape: {gene_data.shape}\")\n", "if gene_data.shape[0] > 0:\n", " print(f\"First 10 gene symbols: {list(gene_data.index[:10])}\")\n", "else:\n", " print(\"WARNING: No genes were found in the final dataset!\")\n", "\n", "# Save the gene expression data to a CSV file\n", "print(\"\\nSaving gene expression data to CSV 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": "b5b5beec", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "a66e7661", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:17:28.834447Z", "iopub.status.busy": "2025-03-25T08:17:28.834322Z", "iopub.status.idle": "2025-03-25T08:17:43.826393Z", "shell.execute_reply": "2025-03-25T08:17:43.825726Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE67311.csv\n", "Clinical data saved to ../../output/preprocess/Chronic_Fatigue_Syndrome/clinical_data/GSE67311.csv\n", "Linked data shape: (142, 19855)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "For the feature 'Chronic_Fatigue_Syndrome', the least common label is '1.0' with 10 occurrences. This represents 7.52% of the dataset.\n", "The distribution of the feature 'Chronic_Fatigue_Syndrome' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Chronic_Fatigue_Syndrome/GSE67311.csv\n" ] } ], "source": [ "# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n", "normalized_gene_data = normalize_gene_symbols_in_index(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", "# Create clinical features directly from clinical_data using the conversion functions defined earlier\n", "clinical_features_df = geo_select_clinical_features(\n", " clinical_data, \n", " trait=trait, \n", " trait_row=trait_row, \n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", ")\n", "\n", "# Save the clinical data\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "clinical_features_df.to_csv(out_clinical_data_file)\n", "print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", "# Now link the clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n", "print(\"Linked data shape:\", linked_data.shape)\n", "\n", "# Handle missing values in the linked data\n", "linked_data = handle_missing_values(linked_data, trait)\n", "\n", "# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n", "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 5. Conduct quality check and save the cohort information.\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=True, \n", " is_biased=is_trait_biased, \n", " df=unbiased_linked_data,\n", " note=\"Dataset contains gene expression from monocytes of rheumatoid arthritis patients, with osteoporosis status included in comorbidity information.\"\n", ")\n", "\n", "# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " unbiased_linked_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 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 }