{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "26576afb", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:42:14.705731Z", "iopub.status.busy": "2025-03-25T03:42:14.705560Z", "iopub.status.idle": "2025-03-25T03:42:14.878038Z", "shell.execute_reply": "2025-03-25T03:42:14.877497Z" } }, "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 = \"Psoriasis\"\n", "cohort = \"GSE178228\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Psoriasis\"\n", "in_cohort_dir = \"../../input/GEO/Psoriasis/GSE178228\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Psoriasis/GSE178228.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Psoriasis/gene_data/GSE178228.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Psoriasis/clinical_data/GSE178228.csv\"\n", "json_path = \"../../output/preprocess/Psoriasis/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "388ebd5e", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "9a2ca5a3", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:42:14.879707Z", "iopub.status.busy": "2025-03-25T03:42:14.879553Z", "iopub.status.idle": "2025-03-25T03:42:15.389943Z", "shell.execute_reply": "2025-03-25T03:42:15.389436Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Ixekizumab treatment of patients with moderate-to-severe plaque psoriasis.\"\n", "!Series_summary\t\"Objectives: To asses skin clearance and patient-reported outcomes for ixekizumab treatment. Methods: IXORA-R enrolled adults with moderate-to-severe plaque psoriasis, defined as static Physician’s Global Assessment ≥ 3, PASI ≥ 12 and involved body surface area ≥ 10%. The trial was registered with ClinicalTrials.gov (NCT03573323).\"\n", "!Series_overall_design\t\"Eligible patients were ≥ 18 years old with chronic plaque psoriasis with a static Physician’s Global Assessment of Disease (sPGA) score of ≥ 3 (moderate), a Psoriasis Area and Severity Index (PASI) ≥ 12, and ≥ 10% body surface area involvement at screening and baseline. Psoriatic plaque skin samples were collected at baseline, week 1, week2, and week 4 after ixekizumab treatment initiation.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['visitid: 2', 'visitid: 4', 'visitid: 3', 'visitid: 5'], 1: ['treatment: Ixekizumab 80mg Q2W'], 2: ['pasi: 14.7', 'pasi: 2.9', 'pasi: 7.2', 'pasi: 15.5', 'pasi: 3', 'pasi: 8.2', 'pasi: 6.4', 'pasi: 6', 'pasi: 7.8', 'pasi: 10.3', 'pasi: 20.1', 'pasi: 3.2', 'pasi: 5', 'pasi: 8.39999999999999', 'pasi: 2.6', 'pasi: 22.8', 'pasi: 4.7', 'pasi: 13.4', 'pasi: 4.3', 'pasi: 10', 'pasi: 10.6', 'pasi: 1.6', 'pasi: 10.2', 'pasi: 4.4', 'pasi: 9.2', 'pasi: 12.6', 'pasi: 12', 'pasi: 5.8', 'pasi: 11.9', 'pasi: 12.7'], 3: ['patient_id: 57', 'patient_id: 43', 'patient_id: 14', 'patient_id: 29', 'patient_id: 50', 'patient_id: 15', 'patient_id: 54', 'patient_id: 19', 'patient_id: 45', 'patient_id: 17', 'patient_id: 40', 'patient_id: 2', 'patient_id: 51', 'patient_id: 37', 'patient_id: 41', 'patient_id: 12', 'patient_id: 35', 'patient_id: 46', 'patient_id: 24', 'patient_id: 55', 'patient_id: 8', 'patient_id: 49', 'patient_id: 32', 'patient_id: 27', 'patient_id: 34', 'patient_id: 21', 'patient_id: 5', 'patient_id: 20', 'patient_id: 47', 'patient_id: 38'], 4: ['time: Gene expression data at baseline.', 'time: Gene expression data at week 2.', 'time: Gene expression data at week 1.', 'time: Gene expression data at week 4.'], 5: ['tissue: skin'], 6: ['disease state: chronic plaque psoriasis']}\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": "9364d150", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "58652415", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:42:15.391364Z", "iopub.status.busy": "2025-03-25T03:42:15.391219Z", "iopub.status.idle": "2025-03-25T03:42:15.402382Z", "shell.execute_reply": "2025-03-25T03:42:15.401897Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of clinical features:\n", "{'GSM5384796': [14.7], 'GSM5384797': [2.9], 'GSM5384798': [7.2], 'GSM5384799': [15.5], 'GSM5384800': [3.0], 'GSM5384801': [8.2], 'GSM5384802': [6.4], 'GSM5384803': [3.0], 'GSM5384804': [6.0], 'GSM5384805': [7.8], 'GSM5384806': [10.3], 'GSM5384807': [20.1], 'GSM5384808': [3.2], 'GSM5384809': [5.0], 'GSM5384810': [8.39999999999999], 'GSM5384811': [2.6], 'GSM5384812': [22.8], 'GSM5384813': [4.7], 'GSM5384814': [13.4], 'GSM5384815': [4.3], 'GSM5384816': [10.0], 'GSM5384817': [10.6], 'GSM5384818': [7.2], 'GSM5384819': [1.6], 'GSM5384820': [10.2], 'GSM5384821': [4.4], 'GSM5384822': [9.2], 'GSM5384823': [12.6], 'GSM5384824': [12.0], 'GSM5384825': [5.8], 'GSM5384826': [5.0], 'GSM5384827': [11.9], 'GSM5384828': [12.0], 'GSM5384829': [3.0], 'GSM5384830': [12.7], 'GSM5384831': [0.7], 'GSM5384832': [4.1], 'GSM5384833': [1.8], 'GSM5384834': [5.2], 'GSM5384835': [2.7], 'GSM5384836': [13.2], 'GSM5384837': [15.2], 'GSM5384838': [13.8999999999999], 'GSM5384839': [4.3], 'GSM5384840': [16.7], 'GSM5384841': [9.4], 'GSM5384842': [6.0], 'GSM5384843': [11.1], 'GSM5384844': [19.2], 'GSM5384845': [8.4], 'GSM5384846': [12.4], 'GSM5384847': [15.6], 'GSM5384848': [14.0], 'GSM5384849': [22.0], 'GSM5384850': [3.6], 'GSM5384851': [6.8], 'GSM5384852': [8.5], 'GSM5384853': [4.6], 'GSM5384854': [7.6], 'GSM5384855': [2.8], 'GSM5384856': [27.7], 'GSM5384857': [2.2], 'GSM5384858': [4.2], 'GSM5384859': [12.0], 'GSM5384860': [2.4], 'GSM5384861': [12.3999999999999], 'GSM5384862': [2.4], 'GSM5384863': [31.9], 'GSM5384864': [5.3], 'GSM5384865': [25.0], 'GSM5384866': [1.8], 'GSM5384867': [14.9], 'GSM5384868': [2.7], 'GSM5384869': [15.6], 'GSM5384870': [8.4], 'GSM5384871': [15.2], 'GSM5384872': [5.2], 'GSM5384873': [16.7], 'GSM5384874': [53.8], 'GSM5384875': [5.8], 'GSM5384876': [11.8], 'GSM5384877': [7.8], 'GSM5384878': [22.3], 'GSM5384879': [15.3], 'GSM5384880': [6.6], 'GSM5384881': [2.8], 'GSM5384882': [7.2], 'GSM5384883': [12.4], 'GSM5384884': [17.8], 'GSM5384885': [9.0], 'GSM5384886': [10.1], 'GSM5384887': [7.39999999999999], 'GSM5384888': [22.0], 'GSM5384889': [10.5], 'GSM5384890': [29.8], 'GSM5384891': [4.1], 'GSM5384892': [18.3], 'GSM5384893': [12.0], 'GSM5384894': [8.8], 'GSM5384895': [16.0], 'GSM5384896': [41.4], 'GSM5384897': [13.5], 'GSM5384898': [6.9], 'GSM5384899': [12.4], 'GSM5384900': [15.7], 'GSM5384901': [15.6], 'GSM5384902': [12.3], 'GSM5384903': [2.4], 'GSM5384904': [14.6], 'GSM5384905': [14.9], 'GSM5384906': [8.8], 'GSM5384907': [22.2], 'GSM5384908': [19.2], 'GSM5384909': [6.1], 'GSM5384910': [11.2], 'GSM5384911': [10.6], 'GSM5384912': [14.2]}\n", "Clinical features saved to ../../output/preprocess/Psoriasis/clinical_data/GSE178228.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the backgrounds, this appears to be a gene expression dataset since it involves measuring gene expression data at different time points after ixekizumab treatment\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# For trait, key 2 contains PASI scores which indicate psoriasis severity\n", "trait_row = 2\n", "\n", "# Unfortunately, age and gender data are not available in the sample characteristics\n", "age_row = None\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"Convert PASI score to a continuous value\"\"\"\n", " try:\n", " # Extract the value after the colon and convert to float\n", " if ':' in value:\n", " val = value.split(':', 1)[1].strip()\n", " return float(val)\n", " return None\n", " except:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age value to a continuous value (not used as age is not available)\"\"\"\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender to binary (not used as gender is not available)\"\"\"\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Trait data availability is True if trait_row is not None\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", " # Extract clinical features using geo_select_clinical_features\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_data, # Using the input dataframe from previous step\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the extracted clinical features\n", " print(\"Preview of clinical features:\")\n", " print(preview_df(clinical_features))\n", " \n", " # Save the clinical features to the specified output file\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "98a6209c", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "a02c8f99", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:42:15.403604Z", "iopub.status.busy": "2025-03-25T03:42:15.403487Z", "iopub.status.idle": "2025-03-25T03:42:16.292088Z", "shell.execute_reply": "2025-03-25T03:42:16.291666Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "First 20 gene/probe identifiers:\n", "Index(['2824546_st', '2824549_st', '2824551_st', '2824554_st', '2827992_st',\n", " '2827995_st', '2827996_st', '2828010_st', '2828012_st', '2835442_st',\n", " '2835447_st', '2835453_st', '2835456_st', '2835459_st', '2835461_st',\n", " '2839509_st', '2839511_st', '2839513_st', '2839515_st', '2839517_st'],\n", " dtype='object', name='ID')\n", "\n", "Gene data dimensions: 70523 genes × 117 samples\n" ] } ], "source": [ "# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Extract the gene expression data from the matrix file\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# 3. Print the first 20 row IDs (gene or probe identifiers)\n", "print(\"\\nFirst 20 gene/probe identifiers:\")\n", "print(gene_data.index[:20])\n", "\n", "# 4. Print the dimensions of the gene expression data\n", "print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n", "\n", "# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n", "is_gene_available = True\n" ] }, { "cell_type": "markdown", "id": "af314c02", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "e8463cc4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:42:16.293562Z", "iopub.status.busy": "2025-03-25T03:42:16.293459Z", "iopub.status.idle": "2025-03-25T03:42:16.295238Z", "shell.execute_reply": "2025-03-25T03:42:16.294980Z" } }, "outputs": [], "source": [ "# Analyze the gene identifiers\n", "# The identifiers have the format \"XXXXXXX_st\" which appears to be probe IDs from a microarray\n", "# These are not standard human gene symbols (like BRCA1, TP53, etc.)\n", "# They need to be mapped to proper gene symbols for analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "bff03c14", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "0b0685c4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:42:16.296361Z", "iopub.status.busy": "2025-03-25T03:42:16.296267Z", "iopub.status.idle": "2025-03-25T03:42:31.728473Z", "shell.execute_reply": "2025-03-25T03:42:31.728161Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of SOFT file content:\n", "^DATABASE = GeoMiame\n", "!Database_name = Gene Expression Omnibus (GEO)\n", "!Database_institute = NCBI NLM NIH\n", "!Database_web_link = http://www.ncbi.nlm.nih.gov/geo\n", "!Database_email = geo@ncbi.nlm.nih.gov\n", "^SERIES = GSE178228\n", "!Series_title = Ixekizumab treatment of patients with moderate-to-severe plaque psoriasis.\n", "!Series_geo_accession = GSE178228\n", "!Series_status = Public on Apr 10 2023\n", "!Series_submission_date = Jun 15 2021\n", "!Series_last_update_date = Apr 11 2023\n", "!Series_pubmed_id = 36967086\n", "!Series_summary = Objectives: To asses skin clearance and patient-reported outcomes for ixekizumab treatment. Methods: IXORA-R enrolled adults with moderate-to-severe plaque psoriasis, defined as static Physician’s Global Assessment ≥ 3, PASI ≥ 12 and involved body surface area ≥ 10%. The trial was registered with ClinicalTrials.gov (NCT03573323).\n", "!Series_overall_design = Eligible patients were ≥ 18 years old with chronic plaque psoriasis with a static Physician’s Global Assessment of Disease (sPGA) score of ≥ 3 (moderate), a Psoriasis Area and Severity Index (PASI) ≥ 12, and ≥ 10% body surface area involvement at screening and baseline. Psoriatic plaque skin samples were collected at baseline, week 1, week2, and week 4 after ixekizumab treatment initiation.\n", "!Series_type = Expression profiling by array\n", "!Series_contributor = Scott,A,Ochsner\n", "!Series_contributor = Neil,J,Mckenna\n", "!Series_sample_id = GSM5384796\n", "!Series_sample_id = GSM5384797\n", "!Series_sample_id = GSM5384798\n", "!Series_sample_id = GSM5384799\n", "...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation dataframe using default method:\n", "Shape: (8322061, 15)\n", "Columns: ['ID', 'probeset_id', 'seqname', 'strand', 'start', 'stop', 'total_probes', 'gene_assignment', 'mrna_assignment', 'swissprot', 'unigene', 'category', 'locus type', 'notes', 'SPOT_ID']\n", " ID probeset_id seqname strand start stop \\\n", "0 TC01000001.hg.1 TC01000001.hg.1 chr1 + 11869 14409 \n", "1 TC01000002.hg.1 TC01000002.hg.1 chr1 + 29554 31109 \n", "2 TC01000003.hg.1 TC01000003.hg.1 chr1 + 69091 70008 \n", "\n", " total_probes gene_assignment \\\n", "0 49.0 NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-As... \n", "1 60.0 ENST00000408384 // MIR1302-11 // microRNA 1302... \n", "2 30.0 NM_001005484 // OR4F5 // olfactory receptor, f... \n", "\n", " mrna_assignment \\\n", "0 NR_046018 // RefSeq // Homo sapiens DEAD/H (As... \n", "1 ENST00000408384 // ENSEMBL // ncrna:miRNA chro... \n", "2 NM_001005484 // RefSeq // Homo sapiens olfacto... \n", "\n", " swissprot \\\n", "0 NR_046018 // B7ZGX0 /// NR_046018 // B7ZGX2 //... \n", "1 --- \n", "2 NM_001005484 // Q8NH21 /// ENST00000335137 // ... \n", "\n", " unigene category locus type \\\n", "0 NR_046018 // Hs.714157 // testis| normal| adul... main Coding \n", "1 ENST00000469289 // Hs.622486 // eye| normal| a... main Coding \n", "2 NM_001005484 // Hs.554500 // --- /// ENST00000... main Coding \n", "\n", " notes SPOT_ID \n", "0 --- chr1(+):11869-14409 \n", "1 --- chr1(+):29554-31109 \n", "2 --- chr1(+):69091-70008 \n", "\n", "Searching for platform annotation section in SOFT file...\n", "^PLATFORM = GPL17586\n", "!platform_table_begin\n", "ID\tprobeset_id\tseqname\tstrand\tstart\tstop\ttotal_probes\tgene_assignment\tmrna_assignment\tswissprot\tunigene\tcategory\tlocus type\tnotes\tSPOT_ID\n", "TC01000001.hg.1\tTC01000001.hg.1\tchr1\t+\t11869\t14409\t49\tNR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000456328 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // 9p24.3 // 100287596 /// ENST00000456328 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102\tNR_046018 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 (DDX11L1), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000456328 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aaa.3 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc010nxq.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc010nxr.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0\tNR_046018 // B7ZGX0 /// NR_046018 // B7ZGX2 /// NR_046018 // B7ZGX7 /// NR_046018 // B7ZGX8 /// ENST00000456328 // B7ZGX0 /// ENST00000456328 // B7ZGX2 /// ENST00000456328 // B7ZGX3 /// ENST00000456328 // B7ZGX7 /// ENST00000456328 // B7ZGX8 /// ENST00000456328 // Q6ZU42\tNR_046018 // Hs.714157 // testis| normal| adult /// ENST00000456328 // Hs.719844 // brain| testis| normal /// ENST00000456328 // Hs.714157 // testis| normal| adult /// ENST00000456328 // Hs.618434 // testis| normal\tmain\tCoding\t---\tchr1(+):11869-14409\n", "TC01000002.hg.1\tTC01000002.hg.1\tchr1\t+\t29554\t31109\t60\tENST00000408384 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000408384 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000408384 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000408384 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// ENST00000469289 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000469289 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000469289 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000469289 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// ENST00000473358 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000473358 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000473358 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000473358 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// OTTHUMT00000002841 // OTTHUMG00000000959 // NULL // --- // --- /// OTTHUMT00000002841 // RP11-34P13.3 // NULL // --- // --- /// OTTHUMT00000002840 // OTTHUMG00000000959 // NULL // --- // --- /// OTTHUMT00000002840 // RP11-34P13.3 // NULL // --- // ---\tENST00000408384 // ENSEMBL // ncrna:miRNA chromosome:GRCh37:1:30366:30503:1 gene:ENSG00000221311 gene_biotype:miRNA transcript_biotype:miRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000469289 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:30267:31109:1 gene:ENSG00000243485 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000473358 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:29554:31097:1 gene:ENSG00000243485 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000002841 // Havana transcript // cdna:all chromosome:VEGA52:1:30267:31109:1 Gene:OTTHUMG00000000959 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000002840 // Havana transcript // cdna:all chromosome:VEGA52:1:29554:31097:1 Gene:OTTHUMG00000000959 // chr1 // 100 // 100 // 0 // --- // 0\t---\tENST00000469289 // Hs.622486 // eye| normal| adult /// ENST00000469289 // Hs.729632 // testis| normal /// ENST00000469289 // Hs.742718 // testis /// ENST00000473358 // Hs.622486 // eye| normal| adult /// ENST00000473358 // Hs.729632 // testis| normal /// ENST00000473358 // Hs.742718 // testis\tmain\tCoding\t---\tchr1(+):29554-31109\n", "TC01000003.hg.1\tTC01000003.hg.1\tchr1\t+\t69091\t70008\t30\tNM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// OTTHUMT00000003223 // OR4F5 // NULL // --- // ---\tNM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // cdna:known chromosome:GRCh37:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aal.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000003223 // Havana transcript // cdna:all chromosome:VEGA52:1:69091:70008:1 Gene:OTTHUMG00000001094 // chr1 // 100 // 100 // 0 // --- // 0\tNM_001005484 // Q8NH21 /// ENST00000335137 // Q8NH21\tNM_001005484 // Hs.554500 // --- /// ENST00000335137 // Hs.554500 // ---\tmain\tCoding\t---\tchr1(+):69091-70008\n", "TC01000004.hg.1\tTC01000004.hg.1\tchr1\t+\t160446\t161525\t30\tOTTHUMT00000007169 // OTTHUMG00000002525 // NULL // --- // --- /// OTTHUMT00000007169 // RP11-34P13.9 // NULL // --- // ---\tENST00000496488 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:160446:161525:1 gene:ENSG00000241599 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000007169 // Havana transcript // cdna:all chromosome:VEGA52:1:160446:161525:1 Gene:OTTHUMG00000002525 // chr1 // 100 // 100 // 0 // --- // 0\t---\t---\tmain\tCoding\t---\tchr1(+):160446-161525\n", "TC01000005.hg.1\tTC01000005.hg.1\tchr1\t+\t317811\t328581\t191\tNR_028322 // LOC100132287 // uncharacterized LOC100132287 // 1p36.33 // 100132287 /// NR_028327 // LOC100133331 // uncharacterized LOC100133331 // 1p36.33 // 100133331 /// ENST00000425496 // LOC101060495 // uncharacterized LOC101060495 // --- // 101060495 /// ENST00000425496 // LOC101060494 // uncharacterized LOC101060494 // --- // 101060494 /// ENST00000425496 // LOC101059936 // uncharacterized LOC101059936 // --- // 101059936 /// ENST00000425496 // LOC100996502 // uncharacterized LOC100996502 // --- // 100996502 /// ENST00000425496 // LOC100996328 // uncharacterized LOC100996328 // --- // 100996328 /// ENST00000425496 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// NR_028325 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// OTTHUMT00000346878 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346878 // RP4-669L17.10 // NULL // --- // --- /// OTTHUMT00000346879 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346879 // RP4-669L17.10 // NULL // --- // --- /// OTTHUMT00000346880 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346880 // RP4-669L17.10 // NULL // --- // --- /// OTTHUMT00000346881 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346881 // RP4-669L17.10 // NULL // --- // ---\tNR_028322 // RefSeq // Homo sapiens uncharacterized LOC100132287 (LOC100132287), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// NR_028327 // RefSeq // Homo sapiens uncharacterized LOC100133331 (LOC100133331), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000425496 // ENSEMBL // ensembl:lincRNA chromosome:GRCh37:1:324756:328453:1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000426316 // ENSEMBL // [retired] cdna:known chromosome:GRCh37:1:317811:328455:1 gene:ENSG00000240876 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 100 // 0 // --- // 0 /// NR_028325 // RefSeq // Homo sapiens uncharacterized LOC100132062 (LOC100132062), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// uc009vjk.2 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc021oeh.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc021oei.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346906 // Havana transcript // [retired] cdna:all chromosome:VEGA50:1:317811:328455:1 Gene:OTTHUMG00000156972 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346878 // Havana transcript // cdna:all chromosome:VEGA52:1:320162:321056:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346879 // Havana transcript // cdna:all chromosome:VEGA52:1:320162:324461:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346880 // Havana transcript // cdna:all chromosome:VEGA52:1:317720:324873:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346881 // Havana transcript // cdna:all chromosome:VEGA52:1:322672:324955:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0\tNR_028325 // B4DYM5 /// NR_028325 // B4E0H4 /// NR_028325 // B4E3X0 /// NR_028325 // B4E3X2 /// NR_028325 // Q6ZQS4\tNR_028322 // Hs.446409 // adrenal gland| blood| bone| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| lymph node| mouth| pharynx| placenta| prostate| skin| testis| thymus| thyroid| uterus| bladder carcinoma| chondrosarcoma| colorectal tumor| germ cell tumor| head and neck tumor| kidney tumor| leukemia| lung tumor| normal| primitive neuroectodermal tumor of the CNS| uterine tumor|embryoid body| blastocyst| fetus| neonate| adult /// NR_028327 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000425496 // Hs.744556 // mammary gland| normal| adult /// ENST00000425496 // Hs.660700 // eye| placenta| testis| normal| adult /// ENST00000425496 // Hs.518952 // blood| brain| intestine| lung| mammary gland| mouth| muscle| pharynx| placenta| prostate| spleen| testis| thymus| thyroid| trachea| breast (mammary gland) tumor| colorectal tumor| head and neck tumor| leukemia| lung tumor| normal| prostate cancer| fetus| adult /// ENST00000425496 // Hs.742131 // testis| normal| adult /// ENST00000425496 // Hs.636102 // uterus| uterine tumor /// ENST00000425496 // Hs.646112 // brain| intestine| larynx| lung| mouth| prostate| testis| thyroid| colorectal tumor| head and neck tumor| lung tumor| normal| prostate cancer| adult /// ENST00000425496 // Hs.647795 // brain| lung| lung tumor| adult /// ENST00000425496 // Hs.684307 // --- /// ENST00000425496 // Hs.720881 // testis| normal /// ENST00000425496 // Hs.729353 // brain| lung| placenta| testis| trachea| lung tumor| normal| fetus| adult /// ENST00000425496 // Hs.735014 // ovary| ovarian tumor /// NR_028325 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| kidney tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult\tmain\tCoding\t2 retired transcript(s) from ENSEMBL, Havana transcript\tchr1(+):317811-328581\n", "TC01000006.hg.1\tTC01000006.hg.1\tchr1\t+\t321084\t321115\t8\t--- // --- // DQ597235,uc001aaq.2 // --- // ---\tuc001aaq.2 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0\t---\t---\tmain\tCoding\t---\tchr1(+):321084-321115\n", "TC01000007.hg.1\tTC01000007.hg.1\tchr1\t+\t321146\t321207\t30\t--- // --- // DQ599768,uc001aar.2 // --- // ---\tuc001aar.2 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0\t---\t---\tmain\tCoding\t---\tchr1(+):321146-321207\n", "TC01000008.hg.1\tTC01000008.hg.1\tchr1\t+\t334140\t342806\t30\tENST00000455464 // LOC101060495 // uncharacterized LOC101060495 // --- // 101060495 /// ENST00000455464 // LOC101060494 // uncharacterized LOC101060494 // --- // 101060494 /// ENST00000455464 // LOC101059936 // uncharacterized LOC101059936 // --- // 101059936 /// ENST00000455464 // LOC100996502 // uncharacterized LOC100996502 // --- // 100996502 /// ENST00000455464 // LOC100996328 // uncharacterized LOC100996328 // --- // 100996328 /// ENST00000455464 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894\tENST00000455464 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:322078:342806:1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0\t---\tENST00000455464 // Hs.744556 // mammary gland| normal| adult /// ENST00000455464 // Hs.660700 // eye| placenta| testis| normal| adult /// ENST00000455464 // Hs.518952 // blood| brain| intestine| lung| mammary gland| mouth| muscle| pharynx| placenta| prostate| spleen| testis| thymus| thyroid| trachea| breast (mammary gland) tumor| colorectal tumor| head and neck tumor| leukemia| lung tumor| normal| prostate cancer| fetus| adult /// ENST00000455464 // Hs.742131 // testis| normal| adult /// ENST00000455464 // Hs.636102 // uterus| uterine tumor /// ENST00000455464 // Hs.646112 // brain| intestine| larynx| lung| mouth| prostate| testis| thyroid| colorectal tumor| head and neck tumor| lung tumor| normal| prostate cancer| adult /// ENST00000455464 // Hs.647795 // brain| lung| lung tumor| adult /// ENST00000455464 // Hs.684307 // --- /// ENST00000455464 // Hs.720881 // testis| normal /// ENST00000455464 // Hs.729353 // brain| lung| placenta| testis| trachea| lung tumor| normal| fetus| adult /// ENST00000455464 // Hs.735014 // ovary| ovarian tumor\tmain\tCoding\t---\tchr1(+):334140-342806\n", "TC01000009.hg.1\tTC01000009.hg.1\tchr1\t+\t367640\t368634\t28\tNM_001005221 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// 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 /// NM_001005277 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// BC137568 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// BC137568 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// BC137568 // 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\tNM_001005221 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 29 (OR4F29), mRNA. // chr1 // 100 // 100 // 0 // --- // 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 // 0 // --- // 0 /// NM_001005277 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 16 (OR4F16), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// BC137568 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 3, mRNA (cDNA clone MGC:169191 IMAGE:9021568), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// NM_001005224 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 3 (OR4F3), mRNA. // chr1 // 100 // 100 // 0 // --- // 0\tNM_001005221 // Q6IEY1 /// BC137547 // Q6IEY1 /// BC137547 // Q6IFP3 /// NM_001005277 // Q6IEY1 /// NM_001005277 // Q6IFP3 /// BC137568 // Q6IFP3 /// BC137568 // Q6IEY1 /// NM_001005224 // Q6IEY1\tNM_001005221 // Hs.722724 // --- /// BC137547 // Hs.722724 // --- /// BC137547 // Hs.632360 // muscle| normal /// NM_001005277 // Hs.632360 // muscle| normal /// BC137568 // Hs.722724 // --- /// BC137568 // Hs.632360 // muscle| normal /// NM_001005224 // Hs.722724 // ---\tmain\tCoding\t---\tchr1(+):367640-368634\n" ] } ], "source": [ "# 1. First get the file paths using geo_get_relevant_filepaths function\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Inspect the SOFT file structure to understand the annotation format\n", "# Read the first few lines of the SOFT file to examine its structure\n", "import gzip\n", "print(\"Preview of SOFT file content:\")\n", "with gzip.open(soft_file, 'rt') as f:\n", " for i, line in enumerate(f):\n", " print(line.strip())\n", " if i >= 20: # Print first 20 lines to understand structure\n", " break\n", "print(\"...\\n\")\n", "\n", "# 3. Try different approaches to extract gene annotation data\n", "# First, let's try the default method to see what's actually in the file\n", "gene_annotation = get_gene_annotation(soft_file)\n", "print(\"Gene annotation dataframe using default method:\")\n", "print(f\"Shape: {gene_annotation.shape}\")\n", "print(f\"Columns: {gene_annotation.columns.tolist()}\")\n", "print(gene_annotation.head(3))\n", "\n", "# 4. Check if there's another section in the file that might contain the mapping\n", "# Look for platform annotation information in the SOFT file\n", "print(\"\\nSearching for platform annotation section in SOFT file...\")\n", "with gzip.open(soft_file, 'rt') as f:\n", " platform_lines = []\n", " capture = False\n", " for i, line in enumerate(f):\n", " if \"^PLATFORM\" in line:\n", " capture = True\n", " platform_lines.append(line.strip())\n", " elif capture and line.startswith(\"!platform_table_begin\"):\n", " platform_lines.append(line.strip())\n", " for j in range(10): # Capture the next 10 lines to understand the table structure\n", " try:\n", " platform_line = next(f).strip()\n", " platform_lines.append(platform_line)\n", " except StopIteration:\n", " break\n", " break\n", " \n", " print(\"\\n\".join(platform_lines))\n", "\n", "# Maintain gene availability status as True based on previous steps\n", "is_gene_available = True\n" ] }, { "cell_type": "markdown", "id": "e068a91a", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "7d7e6815", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:42:31.730444Z", "iopub.status.busy": "2025-03-25T03:42:31.730286Z", "iopub.status.idle": "2025-03-25T03:42:49.335460Z", "shell.execute_reply": "2025-03-25T03:42:49.334881Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Platform information: !Series_platform_id = GPL17586\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of common probe IDs between expression data and annotation: 70523\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Sample of mapping dataframe before extraction:\n", " ID Gene\n", "0 TC01000001.hg.1 NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-As...\n", "1 TC01000002.hg.1 ENST00000408384 // MIR1302-11 // microRNA 1302...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Sample of mapping after extracting gene symbols:\n", " ID Gene\n", "0 TC01000001.hg.1 [DDX11L1, DEAD, DDX11L5]\n", "1 TC01000002.hg.1 [MIR1302-11, MIR1302-10, MIR1302-9, MIR1302-2,...\n", "2 TC01000003.hg.1 [OR4F5, NULL]\n", "3 TC01000004.hg.1 [NULL, RP11-34P13]\n", "4 TC01000005.hg.1 [NULL, RP4-669L17]\n", "\n", "Converting probe measurements to gene expression data...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression dataframe shape: (0, 117)\n", "Sample of gene expression data:\n", "Empty gene data frame\n", "\n", "Warning: Gene expression data is empty after mapping.\n", "Using original probe IDs as fallback...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "NOTE: Gene mapping resulted in empty data. Using original probe IDs which may affect interpretability of results.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Psoriasis/gene_data/GSE178228.csv\n", "Gene data available: True (with 70523 features)\n" ] } ], "source": [ "# First, let's get more information about the dataset platform from the SOFT file\n", "soft_platform_info = None\n", "with gzip.open(soft_file, 'rt') as f:\n", " for line in f:\n", " if line.startswith('!Series_platform_id'):\n", " soft_platform_info = line.strip()\n", " break\n", "\n", "print(f\"Platform information: {soft_platform_info}\")\n", "\n", "# Check for matching probe IDs between expression data and annotation\n", "common_ids = set(gene_data.index) & set(gene_annotation['ID'])\n", "print(f\"Number of common probe IDs between expression data and annotation: {len(common_ids)}\")\n", "\n", "# Since we have matches, let's filter the annotation to only include these probes\n", "filtered_annotation = gene_annotation[gene_annotation['ID'].isin(gene_data.index)].copy()\n", "\n", "# Create mapping dataframe with probe IDs and gene symbols extracted from gene_assignment\n", "mapping_df = filtered_annotation[['ID', 'gene_assignment']].copy()\n", "mapping_df = mapping_df.rename(columns={'gene_assignment': 'Gene'})\n", "\n", "# Display a sample of the mapping dataframe before processing\n", "print(\"\\nSample of mapping dataframe before extraction:\")\n", "print(mapping_df.head(2))\n", "\n", "# Apply the extract_human_gene_symbols function to extract gene symbols\n", "mapping_df['Gene'] = mapping_df['Gene'].apply(extract_human_gene_symbols)\n", "\n", "# Check if we got any gene symbols\n", "sample_with_genes = mapping_df[mapping_df['Gene'].apply(lambda x: len(x) > 0 if isinstance(x, list) else False)].head(5)\n", "print(\"\\nSample of mapping after extracting gene symbols:\")\n", "print(sample_with_genes)\n", "\n", "# Apply the gene mapping to convert probe-level measurements to gene expression\n", "print(\"\\nConverting probe measurements to gene expression data...\")\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "\n", "# Check the shape of the resulting gene expression dataframe\n", "print(f\"Gene expression dataframe shape: {gene_data.shape}\")\n", "print(\"Sample of gene expression data:\")\n", "print(gene_data.head(3) if not gene_data.empty else \"Empty gene data frame\")\n", "\n", "# If we have extracted gene symbols correctly, normalize them\n", "if not gene_data.empty:\n", " gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"\\nFinal gene expression data shape after normalization: {gene_data.shape}\")\n", " print(\"Sample of normalized gene expression data:\")\n", " print(gene_data.head(3))\n", "else:\n", " print(\"\\nWarning: Gene expression data is empty after mapping.\")\n", " # If mapping failed, use the original probe IDs\n", " print(\"Using original probe IDs as fallback...\")\n", " gene_data = get_genetic_data(matrix_file)\n", " \n", " # Create a note about the mapping issue\n", " mapping_note = (\"Gene mapping resulted in empty data. Using original probe IDs \"\n", " \"which may affect interpretability of results.\")\n", " print(f\"\\nNOTE: {mapping_note}\")\n", "\n", "# Save gene 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", "\n", "# Confirm gene data is still available\n", "is_gene_available = gene_data.shape[0] > 0\n", "print(f\"Gene data available: {is_gene_available} (with {gene_data.shape[0]} features)\")\n" ] }, { "cell_type": "markdown", "id": "1baa3b10", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "24ac4a62", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:42:49.336895Z", "iopub.status.busy": "2025-03-25T03:42:49.336762Z", "iopub.status.idle": "2025-03-25T03:42:49.931460Z", "shell.execute_reply": "2025-03-25T03:42:49.930980Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalizing gene symbols...\n", "Gene data shape after normalization: 0 genes × 117 samples\n", "Normalized gene expression data saved to ../../output/preprocess/Psoriasis/gene_data/GSE178228.csv\n", "Extracting clinical features from the original source...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Extracted clinical features preview:\n", "{'GSM5384796': [14.7], 'GSM5384797': [2.9], 'GSM5384798': [7.2], 'GSM5384799': [15.5], 'GSM5384800': [3.0], 'GSM5384801': [8.2], 'GSM5384802': [6.4], 'GSM5384803': [3.0], 'GSM5384804': [6.0], 'GSM5384805': [7.8], 'GSM5384806': [10.3], 'GSM5384807': [20.1], 'GSM5384808': [3.2], 'GSM5384809': [5.0], 'GSM5384810': [8.39999999999999], 'GSM5384811': [2.6], 'GSM5384812': [22.8], 'GSM5384813': [4.7], 'GSM5384814': [13.4], 'GSM5384815': [4.3], 'GSM5384816': [10.0], 'GSM5384817': [10.6], 'GSM5384818': [7.2], 'GSM5384819': [1.6], 'GSM5384820': [10.2], 'GSM5384821': [4.4], 'GSM5384822': [9.2], 'GSM5384823': [12.6], 'GSM5384824': [12.0], 'GSM5384825': [5.8], 'GSM5384826': [5.0], 'GSM5384827': [11.9], 'GSM5384828': [12.0], 'GSM5384829': [3.0], 'GSM5384830': [12.7], 'GSM5384831': [0.7], 'GSM5384832': [4.1], 'GSM5384833': [1.8], 'GSM5384834': [5.2], 'GSM5384835': [2.7], 'GSM5384836': [13.2], 'GSM5384837': [15.2], 'GSM5384838': [13.8999999999999], 'GSM5384839': [4.3], 'GSM5384840': [16.7], 'GSM5384841': [9.4], 'GSM5384842': [6.0], 'GSM5384843': [11.1], 'GSM5384844': [19.2], 'GSM5384845': [8.4], 'GSM5384846': [12.4], 'GSM5384847': [15.6], 'GSM5384848': [14.0], 'GSM5384849': [22.0], 'GSM5384850': [3.6], 'GSM5384851': [6.8], 'GSM5384852': [8.5], 'GSM5384853': [4.6], 'GSM5384854': [7.6], 'GSM5384855': [2.8], 'GSM5384856': [27.7], 'GSM5384857': [2.2], 'GSM5384858': [4.2], 'GSM5384859': [12.0], 'GSM5384860': [2.4], 'GSM5384861': [12.3999999999999], 'GSM5384862': [2.4], 'GSM5384863': [31.9], 'GSM5384864': [5.3], 'GSM5384865': [25.0], 'GSM5384866': [1.8], 'GSM5384867': [14.9], 'GSM5384868': [2.7], 'GSM5384869': [15.6], 'GSM5384870': [8.4], 'GSM5384871': [15.2], 'GSM5384872': [5.2], 'GSM5384873': [16.7], 'GSM5384874': [53.8], 'GSM5384875': [5.8], 'GSM5384876': [11.8], 'GSM5384877': [7.8], 'GSM5384878': [22.3], 'GSM5384879': [15.3], 'GSM5384880': [6.6], 'GSM5384881': [2.8], 'GSM5384882': [7.2], 'GSM5384883': [12.4], 'GSM5384884': [17.8], 'GSM5384885': [9.0], 'GSM5384886': [10.1], 'GSM5384887': [7.39999999999999], 'GSM5384888': [22.0], 'GSM5384889': [10.5], 'GSM5384890': [29.8], 'GSM5384891': [4.1], 'GSM5384892': [18.3], 'GSM5384893': [12.0], 'GSM5384894': [8.8], 'GSM5384895': [16.0], 'GSM5384896': [41.4], 'GSM5384897': [13.5], 'GSM5384898': [6.9], 'GSM5384899': [12.4], 'GSM5384900': [15.7], 'GSM5384901': [15.6], 'GSM5384902': [12.3], 'GSM5384903': [2.4], 'GSM5384904': [14.6], 'GSM5384905': [14.9], 'GSM5384906': [8.8], 'GSM5384907': [22.2], 'GSM5384908': [19.2], 'GSM5384909': [6.1], 'GSM5384910': [11.2], 'GSM5384911': [10.6], 'GSM5384912': [14.2]}\n", "Clinical data shape: (1, 117)\n", "Clinical features saved to ../../output/preprocess/Psoriasis/clinical_data/GSE178228.csv\n", "Linking clinical and genetic data...\n", "Linked data shape: (117, 1)\n", "Error: Linked data has insufficient samples or features.\n", "Abnormality detected in the cohort: GSE178228. Preprocessing failed.\n", "Dataset deemed not usable due to linking failure.\n" ] } ], "source": [ "# 1. Check if gene data is available after mapping\n", "if gene_data.shape[0] == 0:\n", " print(\"Error: Gene expression matrix is empty after mapping.\")\n", " # Mark the dataset as not usable due to lack of gene expression data\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=False, # No usable gene data\n", " is_trait_available=True,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"Failed to map probe IDs to gene symbols. The annotation format may not be compatible with the extraction methods.\"\n", " )\n", " print(\"Dataset deemed not usable due to lack of gene expression data.\")\n", "else:\n", " # Only proceed with normalization if we have gene data\n", " print(\"Normalizing gene symbols...\")\n", " gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Gene data shape after normalization: {gene_data_normalized.shape[0]} genes × {gene_data_normalized.shape[1]} samples\")\n", "\n", " # Save the normalized gene data\n", " os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", " gene_data_normalized.to_csv(out_gene_data_file)\n", " print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n", " \n", " # Extract clinical features from the original data source\n", " print(\"Extracting clinical features from the original source...\")\n", " # Get background information and clinical data again\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", " # 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", " print(\"Extracted clinical features preview:\")\n", " print(preview_df(selected_clinical_df))\n", " print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n", " \n", " # Save the extracted clinical features\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 features saved to {out_clinical_data_file}\")\n", " \n", " # Link clinical and genetic data\n", " print(\"Linking clinical and genetic data...\")\n", " linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data_normalized)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " \n", " # Check if the linked data has adequate data\n", " if linked_data.shape[0] == 0 or linked_data.shape[1] <= 4: # 4 is an arbitrary small number\n", " print(\"Error: Linked data has insufficient samples or features.\")\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=True,\n", " df=linked_data,\n", " note=\"Failed to properly link gene expression data with clinical features.\"\n", " )\n", " print(\"Dataset deemed not usable due to linking failure.\")\n", " else:\n", " # Handle missing values systematically\n", " print(\"Handling missing values...\")\n", " linked_data_clean = handle_missing_values(linked_data, trait_col=trait)\n", " print(f\"Data shape after handling missing values: {linked_data_clean.shape}\")\n", " \n", " # Check if there are still samples after missing value handling\n", " if linked_data_clean.shape[0] == 0:\n", " print(\"Error: No samples remain after handling missing values.\")\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=True,\n", " df=pd.DataFrame(),\n", " note=\"All samples were removed during missing value handling.\"\n", " )\n", " print(\"Dataset deemed not usable as all samples were filtered out.\")\n", " else:\n", " # Check if the dataset is biased\n", " print(\"\\nChecking for bias in feature variables:\")\n", " is_biased, linked_data_final = judge_and_remove_biased_features(linked_data_clean, trait)\n", " \n", " # Conduct final quality validation\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_biased,\n", " df=linked_data_final,\n", " note=\"Dataset contains gene expression data for Crohn's Disease patients, examining response to Infliximab treatment.\"\n", " )\n", " \n", " # Save linked data if usable\n", " if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data_final.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " print(f\"Final dataset shape: {linked_data_final.shape}\")\n", " else:\n", " print(\"Dataset deemed not usable for trait association studies, linked data 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 }