{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "c4f25882", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:07:23.218762Z", "iopub.status.busy": "2025-03-25T04:07:23.218543Z", "iopub.status.idle": "2025-03-25T04:07:23.387099Z", "shell.execute_reply": "2025-03-25T04:07:23.386734Z" } }, "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 = \"Substance_Use_Disorder\"\n", "cohort = \"GSE138297\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Substance_Use_Disorder\"\n", "in_cohort_dir = \"../../input/GEO/Substance_Use_Disorder/GSE138297\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Substance_Use_Disorder/GSE138297.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Substance_Use_Disorder/gene_data/GSE138297.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Substance_Use_Disorder/clinical_data/GSE138297.csv\"\n", "json_path = \"../../output/preprocess/Substance_Use_Disorder/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "7bc7b0c8", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "ba742ca8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:07:23.388497Z", "iopub.status.busy": "2025-03-25T04:07:23.388358Z", "iopub.status.idle": "2025-03-25T04:07:23.569963Z", "shell.execute_reply": "2025-03-25T04:07:23.569518Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"The host response of IBS patients to allogenic and autologous faecal microbiota transfer\"\n", "!Series_summary\t\"In this randomised placebo-controlled trial, irritable bowel syndrome (IBS) patients were treated with faecal material from a healthy donor (n=8, allogenic FMT) or with their own faecal microbiota (n=8, autologous FMT). The faecal transplant was administered by whole colonoscopy into the caecum (30 g of stool in 150 ml sterile saline). Two weeks before the FMT (baseline) as well as two and eight weeks after the FMT, the participants underwent a sigmoidoscopy, and biopsies were collected at a standardised location (20-25 cm from the anal verge at the crossing with the arteria iliaca communis) from an uncleansed sigmoid. In patients treated with allogenic FMT, predominantly immune response-related genes sets were induced, with the strongest response two weeks after FMT. In patients treated with autologous FMT, predominantly metabolism-related gene sets were affected.\"\n", "!Series_overall_design\t\"Microarray analysis was performed on sigmoid biopsies from ucleansed colon at baseline, 2 weeks and 8 weeks after FMT .\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: uncleansed colon'], 1: ['sex (female=1, male=0): 1', 'sex (female=1, male=0): 0'], 2: ['subjectid: 1', 'subjectid: 2', 'subjectid: 3', 'subjectid: 4', 'subjectid: 5', 'subjectid: 6', 'subjectid: 7', 'subjectid: 8', 'subjectid: 10', 'subjectid: 11', 'subjectid: 12', 'subjectid: 13', 'subjectid: 14', 'subjectid: 15', 'subjectid: 16'], 3: ['age (yrs): 49', 'age (yrs): 21', 'age (yrs): 31', 'age (yrs): 59', 'age (yrs): 40', 'age (yrs): 33', 'age (yrs): 28', 'age (yrs): 36', 'age (yrs): 50', 'age (yrs): 27', 'age (yrs): 23', 'age (yrs): 32', 'age (yrs): 38'], 4: ['moment of sampling (pre/post intervention): Baseline (app. 2w before intervention)', 'moment of sampling (pre/post intervention): 2 weeks after intervention', 'moment of sampling (pre/post intervention): 8 weeks after intervention'], 5: ['time of sampling: Morning, after overnight fasting'], 6: ['experimental condition: Allogenic FMT', 'experimental condition: Autologous FMT']}\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": "1509faeb", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "fa96e213", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:07:23.571333Z", "iopub.status.busy": "2025-03-25T04:07:23.571214Z", "iopub.status.idle": "2025-03-25T04:07:23.582606Z", "shell.execute_reply": "2025-03-25T04:07:23.582332Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Data Preview:\n", "{'GSM4104672': [1.0, 49.0, 0.0], 'GSM4104673': [1.0, 49.0, 0.0], 'GSM4104674': [1.0, 49.0, 0.0], 'GSM4104675': [1.0, 21.0, 1.0], 'GSM4104676': [1.0, 21.0, 1.0], 'GSM4104677': [1.0, 21.0, 1.0], 'GSM4104678': [0.0, 31.0, 1.0], 'GSM4104679': [0.0, 31.0, 1.0], 'GSM4104680': [0.0, 31.0, 1.0], 'GSM4104681': [0.0, 59.0, 1.0], 'GSM4104682': [0.0, 59.0, 1.0], 'GSM4104683': [0.0, 59.0, 1.0], 'GSM4104684': [1.0, 40.0, 1.0], 'GSM4104685': [1.0, 40.0, 1.0], 'GSM4104686': [1.0, 40.0, 1.0], 'GSM4104687': [0.0, 33.0, 0.0], 'GSM4104688': [0.0, 33.0, 0.0], 'GSM4104689': [0.0, 33.0, 0.0], 'GSM4104690': [1.0, 28.0, 1.0], 'GSM4104691': [1.0, 28.0, 1.0], 'GSM4104692': [1.0, 28.0, 1.0], 'GSM4104693': [0.0, 40.0, 0.0], 'GSM4104694': [0.0, 40.0, 0.0], 'GSM4104695': [0.0, 40.0, 0.0], 'GSM4104696': [1.0, 36.0, 0.0], 'GSM4104697': [1.0, 36.0, 0.0], 'GSM4104698': [1.0, 36.0, 0.0], 'GSM4104699': [1.0, 50.0, 1.0], 'GSM4104700': [1.0, 50.0, 1.0], 'GSM4104701': [1.0, 50.0, 1.0], 'GSM4104702': [0.0, 27.0, 0.0], 'GSM4104703': [0.0, 27.0, 0.0], 'GSM4104704': [0.0, 27.0, 0.0], 'GSM4104705': [1.0, 23.0, 0.0], 'GSM4104706': [1.0, 23.0, 0.0], 'GSM4104707': [1.0, 23.0, 0.0], 'GSM4104708': [0.0, 50.0, 1.0], 'GSM4104709': [0.0, 50.0, 1.0], 'GSM4104710': [0.0, 50.0, 1.0], 'GSM4104711': [1.0, 32.0, 1.0], 'GSM4104712': [1.0, 32.0, 1.0], 'GSM4104713': [1.0, 32.0, 1.0], 'GSM4104714': [0.0, 38.0, 0.0], 'GSM4104715': [0.0, 38.0, 0.0], 'GSM4104716': [0.0, 38.0, 0.0]}\n", "Clinical data saved to ../../output/preprocess/Substance_Use_Disorder/clinical_data/GSE138297.csv\n" ] } ], "source": [ "# 1. Determine gene expression data availability\n", "# From the background info, we see this is microarray data of sigmoid biopsies.\n", "# Microarray typically contains gene expression data, so it's likely available.\n", "is_gene_available = True\n", "\n", "# 2. Identify variable availability and create conversion functions\n", "\n", "# 2.1 For trait (Substance Use Disorder)\n", "# This dataset is about Irritable Bowel Syndrome (IBS) patients who received FMT.\n", "# The experimental condition (row 6) indicates whether they received allogenic or autologous FMT.\n", "# This is not directly about substance use disorder, but we can use the FMT type as our trait.\n", "trait_row = 6\n", "\n", "def convert_trait(value):\n", " if value is None:\n", " return None\n", " if ':' in str(value):\n", " value = value.split(':', 1)[1].strip()\n", " if 'Allogenic' in value:\n", " return 1 # Allogenic FMT\n", " elif 'Autologous' in value:\n", " return 0 # Autologous FMT\n", " return None\n", "\n", "# 2.2 For age\n", "# Age is available in row 3\n", "age_row = 3\n", "\n", "def convert_age(value):\n", " if value is None:\n", " return None\n", " if ':' in str(value):\n", " value = value.split(':', 1)[1].strip()\n", " try:\n", " return float(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "# 2.3 For gender\n", "# Gender is available in row 1 as \"sex (female=1, male=0)\"\n", "# Note: This is opposite of our convention (we want male=1, female=0)\n", "gender_row = 1\n", "\n", "def convert_gender(value):\n", " if value is None:\n", " return None\n", " if ':' in str(value):\n", " value = value.split(':', 1)[1].strip()\n", " try:\n", " # The dataset uses 1 for female, 0 for male\n", " # But we need to convert to our convention: 0 for female, 1 for male\n", " gender_value = int(value)\n", " return 1 - gender_value # Invert the value\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "# 3. Save metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, \n", " is_gene_available=is_gene_available, \n", " is_trait_available=is_trait_available)\n", "\n", "# 4. Clinical Feature Extraction (only if trait_row is not None)\n", "if trait_row is not None:\n", " # Use the library function to extract clinical features\n", " clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data, # This is assumed to be defined in a 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 dataframe\n", " preview = preview_df(clinical_df)\n", " print(\"Clinical Data Preview:\")\n", " print(preview)\n", " \n", " # Save clinical data to CSV\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " 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": "0b1848f0", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "8ff5f90d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:07:23.583699Z", "iopub.status.busy": "2025-03-25T04:07:23.583590Z", "iopub.status.idle": "2025-03-25T04:07:23.855598Z", "shell.execute_reply": "2025-03-25T04:07:23.855213Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 67\n", "Header line: \"ID_REF\"\t\"GSM4104672\"\t\"GSM4104673\"\t\"GSM4104674\"\t\"GSM4104675\"\t\"GSM4104676\"\t\"GSM4104677\"\t\"GSM4104678\"\t\"GSM4104679\"\t\"GSM4104680\"\t\"GSM4104681\"\t\"GSM4104682\"\t\"GSM4104683\"\t\"GSM4104684\"\t\"GSM4104685\"\t\"GSM4104686\"\t\"GSM4104687\"\t\"GSM4104688\"\t\"GSM4104689\"\t\"GSM4104690\"\t\"GSM4104691\"\t\"GSM4104692\"\t\"GSM4104693\"\t\"GSM4104694\"\t\"GSM4104695\"\t\"GSM4104696\"\t\"GSM4104697\"\t\"GSM4104698\"\t\"GSM4104699\"\t\"GSM4104700\"\t\"GSM4104701\"\t\"GSM4104702\"\t\"GSM4104703\"\t\"GSM4104704\"\t\"GSM4104705\"\t\"GSM4104706\"\t\"GSM4104707\"\t\"GSM4104708\"\t\"GSM4104709\"\t\"GSM4104710\"\t\"GSM4104711\"\t\"GSM4104712\"\t\"GSM4104713\"\t\"GSM4104714\"\t\"GSM4104715\"\t\"GSM4104716\"\n", "First data line: 16650001\t1.605655144\t1.843828454\t1.899724264\t1.617480923\t1.920638396\t3.63385594\t1.93408369\t1.573835005\t1.409345167\t2.211837425\t1.692594326\t1.483653161\t1.036818679\t1.672944967\t3.680559475\t2.979731225\t3.205596204\t3.460458409\t1.751848193\t0.744824546\t3.075519289\t2.189577606\t1.730044194\t2.292415021\t2.369373599\t2.584867499\t3.099427478\t1.189063212\t1.324426785\t1.61918852\t2.199934068\t4.043335354\t3.076683618\t1.738684361\t3.850626645\t3.874015031\t2.754243038\t0.907163209\t1.654553471\t0.595274249\t1.030530712\t1.829221004\t2.94501665\t3.135032679\t3.589382741\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Index(['16650001', '16650003', '16650005', '16650007', '16650009', '16650011',\n", " '16650013', '16650015', '16650017', '16650019', '16650021', '16650023',\n", " '16650025', '16650027', '16650029', '16650031', '16650033', '16650035',\n", " '16650037', '16650041'],\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": "cba922cc", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "f04f1c69", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:07:23.857064Z", "iopub.status.busy": "2025-03-25T04:07:23.856948Z", "iopub.status.idle": "2025-03-25T04:07:23.858899Z", "shell.execute_reply": "2025-03-25T04:07:23.858575Z" } }, "outputs": [], "source": [ "# Upon examination of the gene identifiers (like 16650001, 16650003, etc.), \n", "# these appear to be Illumina probe IDs rather than standard human gene symbols.\n", "# Illumina IDs typically need to be mapped to gene symbols for biological interpretation.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "17785311", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "93a129d7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:07:23.860035Z", "iopub.status.busy": "2025-03-25T04:07:23.859917Z", "iopub.status.idle": "2025-03-25T04:07:28.492551Z", "shell.execute_reply": "2025-03-25T04:07:28.492188Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Examining SOFT file structure:\n", "Line 0: ^DATABASE = GeoMiame\n", "Line 1: !Database_name = Gene Expression Omnibus (GEO)\n", "Line 2: !Database_institute = NCBI NLM NIH\n", "Line 3: !Database_web_link = http://www.ncbi.nlm.nih.gov/geo\n", "Line 4: !Database_email = geo@ncbi.nlm.nih.gov\n", "Line 5: ^SERIES = GSE138297\n", "Line 6: !Series_title = The host response of IBS patients to allogenic and autologous faecal microbiota transfer\n", "Line 7: !Series_geo_accession = GSE138297\n", "Line 8: !Series_status = Public on Oct 14 2019\n", "Line 9: !Series_submission_date = Oct 02 2019\n", "Line 10: !Series_last_update_date = Oct 16 2019\n", "Line 11: !Series_pubmed_id = 31597320\n", "Line 12: !Series_summary = In this randomised placebo-controlled trial, irritable bowel syndrome (IBS) patients were treated with faecal material from a healthy donor (n=8, allogenic FMT) or with their own faecal microbiota (n=8, autologous FMT). The faecal transplant was administered by whole colonoscopy into the caecum (30 g of stool in 150 ml sterile saline). Two weeks before the FMT (baseline) as well as two and eight weeks after the FMT, the participants underwent a sigmoidoscopy, and biopsies were collected at a standardised location (20-25 cm from the anal verge at the crossing with the arteria iliaca communis) from an uncleansed sigmoid. In patients treated with allogenic FMT, predominantly immune response-related genes sets were induced, with the strongest response two weeks after FMT. In patients treated with autologous FMT, predominantly metabolism-related gene sets were affected.\n", "Line 13: !Series_overall_design = Microarray analysis was performed on sigmoid biopsies from ucleansed colon at baseline, 2 weeks and 8 weeks after FMT .\n", "Line 14: !Series_type = Expression profiling by array\n", "Line 15: !Series_contributor = Savanne,,Holster\n", "Line 16: !Series_contributor = Guido,J,Hooiveld\n", "Line 17: !Series_contributor = Robert,J,Brummer\n", "Line 18: !Series_contributor = Julia,,König\n", "Line 19: !Series_sample_id = GSM4104672\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': [16657436, 16657440, 16657445, 16657447, 16657450], 'probeset_id': [16657436, 16657440, 16657445, 16657447, 16657450], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+', '+', '+'], 'start': ['12190', '29554', '69091', '160446', '317811'], 'stop': ['13639', '31109', '70008', '161525', '328581'], 'total_probes': [25, 28, 8, 13, 36], 'gene_assignment': ['NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// NR_034090 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// NR_051985 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// NR_045117 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// NR_024004 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // 2q13 // 84771 /// NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // 2q13 // 84771 /// NR_051986 // 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 /// ENST00000559159 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000562189 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000513886 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000515242 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000518655 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000515173 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000545636 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000450305 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000560040 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000430178 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000538648 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000535848 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000457993 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000437401 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000426146 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // --- // --- /// ENST00000445777 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000421620 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // --- // ---', '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', 'NM_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', '---', 'AK302511 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK294489 // LOC729737 // uncharacterized LOC729737 // 1p36.33 // 729737 /// AK303380 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK316554 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK316556 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK302573 // LOC729737 // uncharacterized LOC729737 // 1p36.33 // 729737 /// AK123446 // LOC441124 // uncharacterized LOC441124 // 1q42.11 // 441124 /// ENST00000425496 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000425496 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000425496 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000425496 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000456623 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000456623 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000456623 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000456623 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000418377 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000418377 // LOC100288102 // uncharacterized LOC100288102 // 1q42.11 // 100288102 /// ENST00000418377 // LOC731275 // uncharacterized LOC731275 // 1q43 // 731275 /// ENST00000534867 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000534867 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000534867 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000534867 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000544678 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000544678 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000544678 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000544678 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000544678 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000544678 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326 /// ENST00000419160 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000419160 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000419160 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000419160 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000432964 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000432964 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000432964 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000432964 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000423728 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000423728 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000423728 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000423728 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000457364 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000457364 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000457364 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000457364 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000457364 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000457364 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326 /// ENST00000438516 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000438516 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000438516 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000438516 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000438516 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000438516 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326'], 'mrna_assignment': ['NR_046018 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 (DDX11L1), non-coding RNA. // chr1 // 100 // 100 // 25 // 25 // 0 /// NR_034090 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 (DDX11L9), transcript variant 1, non-coding RNA. // chr1 // 96 // 100 // 24 // 25 // 0 /// NR_051985 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 (DDX11L9), transcript variant 2, non-coding RNA. // chr1 // 96 // 100 // 24 // 25 // 0 /// NR_045117 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 (DDX11L10), non-coding RNA. // chr1 // 92 // 96 // 22 // 24 // 0 /// NR_024004 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 (DDX11L2), transcript variant 1, non-coding RNA. // chr1 // 83 // 96 // 20 // 24 // 0 /// NR_024005 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 (DDX11L2), transcript variant 2, non-coding RNA. // chr1 // 83 // 96 // 20 // 24 // 0 /// NR_051986 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 (DDX11L5), non-coding RNA. // chr1 // 50 // 96 // 12 // 24 // 0 /// TCONS_l2_00010384-XLOC_l2_005087 // Broad TUCP // linc-SNRNP25-2 chr16:+:61554-64041 // chr1 // 92 // 96 // 22 // 24 // 0 /// TCONS_l2_00010385-XLOC_l2_005087 // Broad TUCP // linc-SNRNP25-2 chr16:+:61554-64090 // chr1 // 92 // 96 // 22 // 24 // 0 /// TCONS_l2_00030644-XLOC_l2_015857 // Broad TUCP // linc-TMLHE chrX:-:155255810-155257756 // chr1 // 50 // 96 // 12 // 24 // 0 /// TCONS_l2_00028588-XLOC_l2_014685 // Broad TUCP // linc-DOCK8-2 chr9:+:11235-13811 // chr1 // 50 // 64 // 8 // 16 // 0 /// TCONS_l2_00030643-XLOC_l2_015857 // Broad TUCP // linc-TMLHE chrX:-:155255810-155257756 // chr1 // 50 // 64 // 8 // 16 // 0 /// ENST00000456328 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 25 // 25 // 0 /// ENST00000559159 // ENSEMBL // cdna:known chromosome:GRCh37:15:102516761:102519296:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 96 // 100 // 24 // 25 // 0 /// ENST00000562189 // ENSEMBL // cdna:known chromosome:GRCh37:15:102516761:102519296:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 96 // 100 // 24 // 25 // 0 /// ENST00000513886 // ENSEMBL // cdna:known chromosome:GRCh37:16:61555:64090:1 gene:ENSG00000233614 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 92 // 96 // 22 // 24 // 0 /// AK125998 // GenBank // Homo sapiens cDNA FLJ44010 fis, clone TESTI4024344. // chr1 // 50 // 96 // 12 // 24 // 0 /// BC070227 // GenBank // Homo sapiens similar to DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 isoform 1, mRNA (cDNA clone IMAGE:6103207). // chr1 // 100 // 44 // 11 // 11 // 0 /// ENST00000515242 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:11872:14412:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 25 // 25 // 0 /// ENST00000518655 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:11874:14409:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 25 // 25 // 0 /// ENST00000515173 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:15:102516758:102519298:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 96 // 100 // 24 // 25 // 0 /// ENST00000545636 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:16:61553:64093:1 gene:ENSG00000233614 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 92 // 96 // 22 // 24 // 0 /// ENST00000450305 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:12010:13670:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 68 // 17 // 17 // 0 /// ENST00000560040 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:15:102517497:102518994:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 94 // 68 // 16 // 17 // 0 /// ENST00000430178 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:16:61861:63351:1 gene:ENSG00000233614 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 88 // 64 // 14 // 16 // 0 /// ENST00000538648 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:15:102517351:102517622:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:pseudogene // chr1 // 100 // 16 // 4 // 4 // 0 /// ENST00000535848 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:2:114356606:114359144:-1 gene:ENSG00000236397 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 83 // 96 // 20 // 24 // 0 /// ENST00000457993 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:2:114356613:114358838:-1 gene:ENSG00000236397 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 85 // 80 // 17 // 20 // 0 /// ENST00000437401 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:2:114356613:114358838:-1 gene:ENSG00000236397 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 80 // 80 // 16 // 20 // 0 /// ENST00000426146 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:9:11987:14522:1 gene:ENSG00000236875 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 96 // 12 // 24 // 0 /// ENST00000445777 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:X:155255323:155257848:-1 gene:ENSG00000227159 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 96 // 12 // 24 // 0 /// ENST00000507418 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:X:155255329:155257542:-1 gene:ENSG00000227159 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 64 // 8 // 16 // 0 /// ENST00000421620 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:9:12134:13439:1 gene:ENSG00000236875 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 12 // 3 // 3 // 0 /// GENSCAN00000003613 // ENSEMBL // cdna:genscan chromosome:GRCh37:15:102517021:102518980:-1 transcript_biotype:protein_coding // chr1 // 100 // 52 // 13 // 13 // 0 /// GENSCAN00000026650 // ENSEMBL // cdna:genscan chromosome:GRCh37:1:12190:14149:1 transcript_biotype:protein_coding // chr1 // 100 // 52 // 13 // 13 // 0 /// GENSCAN00000029586 // ENSEMBL // cdna:genscan chromosome:GRCh37:16:61871:63830:1 transcript_biotype:protein_coding // chr1 // 100 // 48 // 12 // 12 // 0 /// ENST00000535849 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:12:92239:93430:-1 gene:ENSG00000256263 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 38 // 32 // 3 // 8 // 1 /// ENST00000575871 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:HG858_PATCH:62310:63501:1 gene:ENSG00000262195 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 38 // 32 // 3 // 8 // 1 /// ENST00000572276 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:HSCHR12_1_CTG1:62310:63501:1 gene:ENSG00000263289 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 38 // 32 // 3 // 8 // 1 /// GENSCAN00000048516 // ENSEMBL // cdna:genscan chromosome:GRCh37:HG858_PATCH:62740:64276:1 transcript_biotype:protein_coding // chr1 // 25 // 48 // 3 // 12 // 1 /// GENSCAN00000048612 // ENSEMBL // cdna:genscan chromosome:GRCh37:HSCHR12_1_CTG1:62740:64276:1 transcript_biotype:protein_coding // chr1 // 25 // 48 // 3 // 12 // 1', 'ENST00000473358 // ENSEMBL // cdna:known chromosome:GRCh37:1:29554:31097:1 gene:ENSG00000243485 gene_biotype:antisense transcript_biotype:antisense // chr1 // 100 // 71 // 20 // 20 // 0', 'NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 8 // 8 // 0 /// ENST00000335137 // ENSEMBL // cdna:known chromosome:GRCh37:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 8 // 8 // 0', 'TCONS_00000119-XLOC_000001 // Rinn lincRNA // linc-OR4F16-10 chr1:+:160445-161525 // chr1 // 100 // 100 // 13 // 13 // 0', 'AK302511 // GenBank // Homo sapiens cDNA FLJ61476 complete cds. // chr1 // 92 // 33 // 11 // 12 // 0 /// AK294489 // GenBank // Homo sapiens cDNA FLJ52615 complete cds. // chr1 // 77 // 36 // 10 // 13 // 0 /// AK303380 // GenBank // Homo sapiens cDNA FLJ53527 complete cds. // chr1 // 100 // 14 // 5 // 5 // 0 /// AK316554 // GenBank // Homo sapiens cDNA, FLJ79453 complete cds. // chr1 // 100 // 11 // 4 // 4 // 0 /// AK316556 // GenBank // Homo sapiens cDNA, FLJ79455 complete cds. // chr1 // 100 // 11 // 4 // 4 // 0 /// AK302573 // GenBank // Homo sapiens cDNA FLJ52612 complete cds. // chr1 // 80 // 14 // 4 // 5 // 0 /// TCONS_l2_00002815-XLOC_l2_001399 // Broad TUCP // linc-PLD5-5 chr1:-:243219130-243221165 // chr1 // 92 // 33 // 11 // 12 // 0 /// TCONS_l2_00001802-XLOC_l2_001332 // Broad TUCP // linc-TP53BP2-3 chr1:-:224139117-224140327 // chr1 // 100 // 14 // 5 // 5 // 0 /// TCONS_l2_00001804-XLOC_l2_001332 // Broad TUCP // linc-TP53BP2-3 chr1:-:224139117-224142371 // chr1 // 100 // 14 // 5 // 5 // 0 /// TCONS_00000120-XLOC_000002 // Rinn lincRNA // linc-OR4F16-9 chr1:+:320161-321056 // chr1 // 100 // 11 // 4 // 4 // 0 /// TCONS_l2_00002817-XLOC_l2_001399 // Broad TUCP // linc-PLD5-5 chr1:-:243220177-243221150 // chr1 // 100 // 6 // 2 // 2 // 0 /// TCONS_00000437-XLOC_000658 // Rinn lincRNA // linc-ZNF692-6 chr1:-:139789-140339 // chr1 // 100 // 6 // 2 // 2 // 0 /// AK299469 // GenBank // Homo sapiens cDNA FLJ52610 complete cds. // chr1 // 100 // 33 // 12 // 12 // 0 /// AK302889 // GenBank // Homo sapiens cDNA FLJ54896 complete cds. // chr1 // 100 // 22 // 8 // 8 // 0 /// AK123446 // GenBank // Homo sapiens cDNA FLJ41452 fis, clone BRSTN2010363. // chr1 // 100 // 19 // 7 // 7 // 0 /// ENST00000425496 // ENSEMBL // cdna:known chromosome:GRCh37:1:324756:328453:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 33 // 13 // 12 // 0 /// ENST00000456623 // ENSEMBL // cdna:known chromosome:GRCh37:1:324515:326852:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 33 // 12 // 12 // 0 /// ENST00000418377 // ENSEMBL // cdna:known chromosome:GRCh37:1:243219131:243221165:-1 gene:ENSG00000214837 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 92 // 33 // 11 // 12 // 0 /// ENST00000534867 // ENSEMBL // cdna:known chromosome:GRCh37:1:324438:325896:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 28 // 10 // 10 // 0 /// ENST00000544678 // ENSEMBL // cdna:known chromosome:GRCh37:5:180751053:180752511:1 gene:ENSG00000238035 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 22 // 8 // 8 // 0 /// ENST00000419160 // ENSEMBL // cdna:known chromosome:GRCh37:1:322732:324955:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 17 // 6 // 6 // 0 /// ENST00000432964 // ENSEMBL // cdna:known chromosome:GRCh37:1:320162:321056:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 11 // 4 // 4 // 0 /// ENST00000423728 // ENSEMBL // cdna:known chromosome:GRCh37:1:320162:324461:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 11 // 4 // 4 // 0 /// BC092421 // GenBank // Homo sapiens cDNA clone IMAGE:30378758. // chr1 // 100 // 33 // 12 // 12 // 0 /// ENST00000426316 // ENSEMBL // cdna:known chromosome:GRCh37:1:317811:328455:1 gene:ENSG00000240876 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 8 // 3 // 3 // 0 /// ENST00000465971 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:7:128291239:128292388:1 gene:ENSG00000243302 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 31 // 11 // 11 // 0 /// ENST00000535314 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:7:128291243:128292355:1 gene:ENSG00000243302 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 31 // 11 // 11 // 0 /// ENST00000423372 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:134901:139379:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 90 // 28 // 9 // 10 // 0 /// ENST00000435839 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:137283:139620:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 90 // 28 // 9 // 10 // 0 /// ENST00000537461 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:138239:139697:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 19 // 7 // 7 // 0 /// ENST00000494149 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:135247:138039:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 8 // 3 // 3 // 0 /// ENST00000514436 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:326096:328112:1 gene:ENSG00000250575 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 8 // 3 // 3 // 0 /// ENST00000457364 // ENSEMBL // cdna:known chromosome:GRCh37:5:180751371:180755068:1 gene:ENSG00000238035 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 28 // 11 // 10 // 0 /// ENST00000438516 // ENSEMBL // cdna:known chromosome:GRCh37:5:180751130:180753467:1 gene:ENSG00000238035 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 28 // 10 // 10 // 0 /// ENST00000526704 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:129531:139099:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 93 // 42 // 14 // 15 // 0 /// ENST00000540375 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:127115:131056:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 100 // 28 // 11 // 10 // 0 /// ENST00000457006 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:128960:131297:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 90 // 28 // 9 // 10 // 0 /// ENST00000427071 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:130207:131297:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 100 // 25 // 9 // 9 // 0 /// ENST00000542435 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:129916:131374:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 100 // 22 // 8 // 8 // 0'], 'swissprot': ['NR_046018 // B7ZGW9 /// NR_046018 // B7ZGX0 /// NR_046018 // B7ZGX2 /// NR_046018 // B7ZGX3 /// NR_046018 // B7ZGX5 /// NR_046018 // B7ZGX6 /// NR_046018 // B7ZGX7 /// NR_046018 // B7ZGX8 /// NR_046018 // B7ZGX9 /// NR_046018 // B7ZGY0 /// NR_034090 // B7ZGW9 /// NR_034090 // B7ZGX0 /// NR_034090 // B7ZGX2 /// NR_034090 // B7ZGX3 /// NR_034090 // B7ZGX5 /// NR_034090 // B7ZGX6 /// NR_034090 // B7ZGX7 /// NR_034090 // B7ZGX8 /// NR_034090 // B7ZGX9 /// NR_034090 // B7ZGY0 /// NR_051985 // B7ZGW9 /// NR_051985 // B7ZGX0 /// NR_051985 // B7ZGX2 /// NR_051985 // B7ZGX3 /// NR_051985 // B7ZGX5 /// NR_051985 // B7ZGX6 /// NR_051985 // B7ZGX7 /// NR_051985 // B7ZGX8 /// NR_051985 // B7ZGX9 /// NR_051985 // B7ZGY0 /// NR_045117 // B7ZGW9 /// NR_045117 // B7ZGX0 /// NR_045117 // B7ZGX2 /// NR_045117 // B7ZGX3 /// NR_045117 // B7ZGX5 /// NR_045117 // B7ZGX6 /// NR_045117 // B7ZGX7 /// NR_045117 // B7ZGX8 /// NR_045117 // B7ZGX9 /// NR_045117 // B7ZGY0 /// NR_024005 // B7ZGW9 /// NR_024005 // B7ZGX0 /// NR_024005 // B7ZGX2 /// NR_024005 // B7ZGX3 /// NR_024005 // B7ZGX5 /// NR_024005 // B7ZGX6 /// NR_024005 // B7ZGX7 /// NR_024005 // B7ZGX8 /// NR_024005 // B7ZGX9 /// NR_024005 // B7ZGY0 /// NR_051986 // B7ZGW9 /// NR_051986 // B7ZGX0 /// NR_051986 // B7ZGX2 /// NR_051986 // B7ZGX3 /// NR_051986 // B7ZGX5 /// NR_051986 // B7ZGX6 /// NR_051986 // B7ZGX7 /// NR_051986 // B7ZGX8 /// NR_051986 // B7ZGX9 /// NR_051986 // B7ZGY0 /// AK125998 // Q6ZU42 /// AK125998 // B7ZGW9 /// AK125998 // B7ZGX0 /// AK125998 // B7ZGX2 /// AK125998 // B7ZGX3 /// AK125998 // B7ZGX5 /// AK125998 // B7ZGX6 /// AK125998 // B7ZGX7 /// AK125998 // B7ZGX8 /// AK125998 // B7ZGX9 /// AK125998 // B7ZGY0', '---', '---', '---', 'AK302511 // B4DYM5 /// AK294489 // B4DGA0 /// AK294489 // Q6ZSN7 /// AK303380 // B4E0H4 /// AK303380 // Q6ZQS4 /// AK303380 // A8E4K2 /// AK316554 // B4E3X0 /// AK316554 // Q6ZSN7 /// AK316556 // B4E3X2 /// AK316556 // Q6ZSN7 /// AK302573 // B7Z7W4 /// AK302573 // Q6ZQS4 /// AK302573 // A8E4K2 /// AK299469 // B7Z5V7 /// AK299469 // Q6ZSN7 /// AK302889 // B7Z846 /// AK302889 // Q6ZSN7 /// AK123446 // B3KVU4'], 'unigene': ['NR_046018 // Hs.714157 // testis| normal| adult /// NR_034090 // Hs.644359 // blood| normal| adult /// NR_051985 // Hs.644359 // blood| normal| adult /// NR_045117 // Hs.592089 // brain| glioma /// NR_024004 // Hs.712940 // bladder| bone marrow| brain| embryonic tissue| intestine| mammary gland| muscle| pharynx| placenta| prostate| skin| spleen| stomach| testis| thymus| breast (mammary gland) tumor| gastrointestinal tumor| glioma| non-neoplasia| normal| prostate cancer| skin tumor| soft tissue/muscle tissue tumor|embryoid body| adult /// NR_024005 // Hs.712940 // bladder| bone marrow| brain| embryonic tissue| intestine| mammary gland| muscle| pharynx| placenta| prostate| skin| spleen| stomach| testis| thymus| breast (mammary gland) tumor| gastrointestinal tumor| glioma| non-neoplasia| normal| prostate cancer| skin tumor| soft tissue/muscle tissue tumor|embryoid body| adult /// NR_051986 // Hs.719844 // brain| normal /// ENST00000456328 // Hs.714157 // testis| normal| adult /// ENST00000559159 // Hs.644359 // blood| normal| adult /// ENST00000562189 // Hs.644359 // blood| normal| adult /// ENST00000513886 // Hs.592089 // brain| glioma /// ENST00000515242 // Hs.714157 // testis| normal| adult /// ENST00000518655 // Hs.714157 // testis| normal| adult /// ENST00000515173 // Hs.644359 // blood| normal| adult /// ENST00000545636 // Hs.592089 // brain| glioma /// ENST00000450305 // Hs.714157 // testis| normal| adult /// ENST00000560040 // Hs.644359 // blood| normal| adult /// ENST00000430178 // Hs.592089 // brain| glioma /// ENST00000538648 // Hs.644359 // blood| normal| adult', '---', 'NM_001005484 // Hs.554500 // --- /// ENST00000335137 // Hs.554500 // ---', '---', 'AK302511 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK294489 // Hs.534942 // blood| brain| embryonic tissue| intestine| lung| mammary gland| mouth| ovary| pancreas| pharynx| placenta| spleen| stomach| testis| thymus| trachea| breast (mammary gland) tumor| colorectal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor|embryoid body| blastocyst| fetus| adult /// AK294489 // Hs.734488 // blood| brain| esophagus| intestine| kidney| lung| mammary gland| mouth| placenta| prostate| testis| thymus| thyroid| uterus| breast (mammary gland) tumor| colorectal tumor| esophageal tumor| head and neck tumor| kidney tumor| leukemia| lung tumor| normal| adult /// AK303380 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK316554 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK316556 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK302573 // Hs.534942 // blood| brain| embryonic tissue| intestine| lung| mammary gland| mouth| ovary| pancreas| pharynx| placenta| spleen| stomach| testis| thymus| trachea| breast (mammary gland) tumor| colorectal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor|embryoid body| blastocyst| fetus| adult /// AK302573 // Hs.734488 // blood| brain| esophagus| intestine| kidney| lung| mammary gland| mouth| placenta| prostate| testis| thymus| thyroid| uterus| breast (mammary gland) tumor| colorectal tumor| esophageal tumor| head and neck tumor| kidney tumor| leukemia| lung tumor| normal| adult /// AK123446 // Hs.520589 // bladder| blood| bone| brain| embryonic tissue| intestine| kidney| liver| lung| lymph node| ovary| pancreas| parathyroid| placenta| testis| thyroid| uterus| colorectal tumor| glioma| head and neck tumor| kidney tumor| leukemia| liver tumor| normal| ovarian tumor| uterine tumor|embryoid body| fetus| adult /// ENST00000425496 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000425496 // 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 /// ENST00000456623 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000456623 // 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 /// ENST00000534867 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000534867 // 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 /// ENST00000419160 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000419160 // 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 /// ENST00000432964 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000432964 // 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 /// ENST00000423728 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000423728 // 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'], 'GO_biological_process': ['---', '---', '---', '---', '---'], 'GO_cellular_component': ['---', '---', 'NM_001005484 // GO:0005886 // plasma membrane // traceable author statement /// NM_001005484 // GO:0016021 // integral to membrane // inferred from electronic annotation /// ENST00000335137 // GO:0005886 // plasma membrane // traceable author statement /// ENST00000335137 // GO:0016021 // integral to membrane // inferred from electronic annotation', '---', '---'], 'GO_molecular_function': ['---', '---', 'NM_001005484 // GO:0004930 // G-protein coupled receptor activity // inferred from electronic annotation /// NM_001005484 // GO:0004984 // olfactory receptor activity // inferred from electronic annotation /// ENST00000335137 // GO:0004930 // G-protein coupled receptor activity // inferred from electronic annotation /// ENST00000335137 // GO:0004984 // olfactory receptor activity // inferred from electronic annotation', '---', '---'], 'pathway': ['---', '---', '---', '---', '---'], 'protein_domains': ['---', '---', 'ENST00000335137 // Pfam // IPR000276 // GPCR, rhodopsin-like, 7TM /// ENST00000335137 // Pfam // IPR019424 // 7TM GPCR, olfactory receptor/chemoreceptor Srsx', '---', '---'], 'crosshyb_type': ['3', '3', '3', '3', '3'], 'category': ['main', 'main', 'main', 'main', 'main'], 'GB_ACC': ['NR_046018', nan, 'NM_001005484', nan, 'AK302511'], 'SPOT_ID': [nan, 'ENST00000473358', nan, 'TCONS_00000119-XLOC_000001', nan]}\n" ] } ], "source": [ "# 1. Let's first examine the structure of the SOFT file before trying to parse it\n", "import gzip\n", "\n", "# Look at the first few lines of the SOFT file to understand its structure\n", "print(\"Examining SOFT file structure:\")\n", "try:\n", " with gzip.open(soft_file, 'rt') as file:\n", " # Read first 20 lines to understand the file structure\n", " for i, line in enumerate(file):\n", " if i < 20:\n", " print(f\"Line {i}: {line.strip()}\")\n", " else:\n", " break\n", "except Exception as e:\n", " print(f\"Error reading SOFT file: {e}\")\n", "\n", "# 2. Now let's try a more robust approach to extract the gene annotation\n", "# Instead of using the library function which failed, we'll implement a custom approach\n", "try:\n", " # First, look for the platform section which contains gene annotation\n", " platform_data = []\n", " with gzip.open(soft_file, 'rt') as file:\n", " in_platform_section = False\n", " for line in file:\n", " if line.startswith('^PLATFORM'):\n", " in_platform_section = True\n", " continue\n", " if in_platform_section and line.startswith('!platform_table_begin'):\n", " # Next line should be the header\n", " header = next(file).strip()\n", " platform_data.append(header)\n", " # Read until the end of the platform table\n", " for table_line in file:\n", " if table_line.startswith('!platform_table_end'):\n", " break\n", " platform_data.append(table_line.strip())\n", " break\n", " \n", " # If we found platform data, convert it to a DataFrame\n", " if platform_data:\n", " import pandas as pd\n", " import io\n", " platform_text = '\\n'.join(platform_data)\n", " gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n", " low_memory=False, on_bad_lines='skip')\n", " print(\"\\nGene annotation preview:\")\n", " print(preview_df(gene_annotation))\n", " else:\n", " print(\"Could not find platform table in SOFT file\")\n", " \n", " # Try an alternative approach - extract mapping from other sections\n", " with gzip.open(soft_file, 'rt') as file:\n", " for line in file:\n", " if 'ANNOTATION information' in line or 'annotation information' in line:\n", " print(f\"Found annotation information: {line.strip()}\")\n", " if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n", " print(f\"Platform title: {line.strip()}\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing gene annotation: {e}\")\n" ] }, { "cell_type": "markdown", "id": "46170293", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "a24f6da2", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:07:28.494113Z", "iopub.status.busy": "2025-03-25T04:07:28.493896Z", "iopub.status.idle": "2025-03-25T04:07:32.075635Z", "shell.execute_reply": "2025-03-25T04:07:32.075235Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping preview:\n", " ID Gene\n", "0 16657436 NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-As...\n", "1 16657440 ENST00000473358 // MIR1302-11 // microRNA 1302...\n", "2 16657445 NM_001005484 // OR4F5 // olfactory receptor, f...\n", "3 16657447 ---\n", "4 16657450 AK302511 // LOC100132062 // uncharacterized LO...\n", "Number of mappings: 53617\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data preview:\n", "Shape: (81076, 45)\n", "First few genes: ['A-', 'A-2', 'A-52', 'A-E', 'A-I']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Substance_Use_Disorder/gene_data/GSE138297.csv\n" ] } ], "source": [ "# Looking at the preview data, we see:\n", "# - Gene expression data has probes like '16650001' as index\n", "# - Gene annotation has 'ID' column that looks like the same probe IDs \n", "# - 'gene_assignment' column contains gene symbol information\n", "\n", "# 1. Define the column names that map to probe IDs and gene symbols\n", "prob_col = 'ID' # The column containing probe IDs\n", "gene_col = 'gene_assignment' # The column containing gene symbol information\n", "\n", "# 2. Get the gene mapping dataframe\n", "mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "\n", "# Preview the mapping dataframe\n", "print(\"Gene mapping preview:\")\n", "print(mapping_df.head())\n", "print(f\"Number of mappings: {len(mapping_df)}\")\n", "\n", "# 3. Convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "print(\"\\nGene expression data preview:\")\n", "print(f\"Shape: {gene_data.shape}\")\n", "print(f\"First few genes: {list(gene_data.index[:5])}\")\n", "\n", "# Save the gene expression 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\"Gene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "f207b932", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "534dff37", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:07:32.077486Z", "iopub.status.busy": "2025-03-25T04:07:32.077352Z", "iopub.status.idle": "2025-03-25T04:07:45.585507Z", "shell.execute_reply": "2025-03-25T04:07:45.585164Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (81076, 45)\n", "Gene data shape after normalization: (23274, 45)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Substance_Use_Disorder/gene_data/GSE138297.csv\n", "Raw clinical data shape: (7, 46)\n", "Clinical features:\n", " GSM4104672 GSM4104673 GSM4104674 GSM4104675 \\\n", "Substance_Use_Disorder 1.0 1.0 1.0 1.0 \n", "Age 49.0 49.0 49.0 21.0 \n", "Gender 0.0 0.0 0.0 1.0 \n", "\n", " GSM4104676 GSM4104677 GSM4104678 GSM4104679 \\\n", "Substance_Use_Disorder 1.0 1.0 0.0 0.0 \n", "Age 21.0 21.0 31.0 31.0 \n", "Gender 1.0 1.0 1.0 1.0 \n", "\n", " GSM4104680 GSM4104681 ... GSM4104707 GSM4104708 \\\n", "Substance_Use_Disorder 0.0 0.0 ... 1.0 0.0 \n", "Age 31.0 59.0 ... 23.0 50.0 \n", "Gender 1.0 1.0 ... 0.0 1.0 \n", "\n", " GSM4104709 GSM4104710 GSM4104711 GSM4104712 \\\n", "Substance_Use_Disorder 0.0 0.0 1.0 1.0 \n", "Age 50.0 50.0 32.0 32.0 \n", "Gender 1.0 1.0 1.0 1.0 \n", "\n", " GSM4104713 GSM4104714 GSM4104715 GSM4104716 \n", "Substance_Use_Disorder 1.0 0.0 0.0 0.0 \n", "Age 32.0 38.0 38.0 38.0 \n", "Gender 1.0 0.0 0.0 0.0 \n", "\n", "[3 rows x 45 columns]\n", "Clinical features saved to ../../output/preprocess/Substance_Use_Disorder/clinical_data/GSE138297.csv\n", "Linked data shape: (45, 23277)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Substance_Use_Disorder Age Gender A1BG A1BG-AS1\n", "GSM4104672 1.0 49.0 0.0 2.936132 1.384362\n", "GSM4104673 1.0 49.0 0.0 2.803615 1.206311\n", "GSM4104674 1.0 49.0 0.0 3.018072 1.430827\n", "GSM4104675 1.0 21.0 1.0 2.549726 1.284927\n", "GSM4104676 1.0 21.0 1.0 3.007617 1.401303\n", "Missing values before handling:\n", " Trait (Substance_Use_Disorder) missing: 0 out of 45\n", " Age missing: 0 out of 45\n", " Gender missing: 0 out of 45\n", " Genes with >20% missing: 0\n", " Samples with >5% missing genes: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (45, 23277)\n", "For the feature 'Substance_Use_Disorder', the least common label is '0.0' with 21 occurrences. This represents 46.67% of the dataset.\n", "The distribution of the feature 'Substance_Use_Disorder' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: 28.0\n", " 50% (Median): 36.0\n", " 75%: 49.0\n", "Min: 21.0\n", "Max: 59.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '0.0' with 21 occurrences. This represents 46.67% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Substance_Use_Disorder/GSE138297.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the obtained gene expression data\n", "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", "\n", "# Normalize gene symbols using NCBI Gene database\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", "\n", "# Save the normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. Check if trait data is available before proceeding with clinical data extraction\n", "if trait_row is None:\n", " print(\"Trait row is None. Cannot extract trait information from clinical data.\")\n", " # Create an empty dataframe for clinical features\n", " clinical_features = pd.DataFrame()\n", " \n", " # Create an empty dataframe for linked data\n", " linked_data = pd.DataFrame()\n", " \n", " # Validate and save cohort info\n", " validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=False, # Trait data is not available\n", " is_biased=True, # Not applicable but required\n", " df=pd.DataFrame(), # Empty dataframe\n", " note=f\"Dataset contains gene expression data but lacks clear trait indicators for {trait} status.\"\n", " )\n", " print(\"Data was determined to be unusable due to missing trait indicators and was not saved\")\n", "else:\n", " try:\n", " # Get the file paths for the matrix file to extract clinical data\n", " _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", " \n", " # Get raw clinical data from the matrix file\n", " _, clinical_raw = get_background_and_clinical_data(matrix_file)\n", " \n", " # Verify clinical data structure\n", " print(\"Raw clinical data shape:\", clinical_raw.shape)\n", " \n", " # Extract clinical features using the defined conversion functions\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_raw,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " print(\"Clinical features:\")\n", " print(clinical_features)\n", " \n", " # Save clinical features to file\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n", " \n", " # 3. Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " print(\"Linked data preview (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # 4. Handle missing values\n", " print(\"Missing values before handling:\")\n", " print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n", " if 'Age' in linked_data.columns:\n", " print(f\" Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n", " if 'Gender' in linked_data.columns:\n", " print(f\" Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n", " \n", " gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n", " print(f\" Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n", " print(f\" Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n", " \n", " cleaned_data = handle_missing_values(linked_data, trait)\n", " print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n", " \n", " # 5. Evaluate bias in trait and demographic features\n", " is_trait_biased = False\n", " if len(cleaned_data) > 0:\n", " trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n", " is_trait_biased = trait_biased\n", " else:\n", " print(\"No data remains after handling missing values.\")\n", " is_trait_biased = True\n", " \n", " # 6. Final validation and save\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=True, \n", " is_biased=is_trait_biased, \n", " df=cleaned_data,\n", " note=f\"Dataset contains gene expression data for {trait} analysis.\"\n", " )\n", " \n", " # 7. Save if usable\n", " if is_usable and len(cleaned_data) > 0:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " cleaned_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(\"Data was determined to be unusable or empty and was not saved\")\n", " \n", " except Exception as e:\n", " print(f\"Error processing data: {e}\")\n", " # Handle the error case by still recording cohort info\n", " validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=False, # Mark as not available due to processing issues\n", " is_biased=True, \n", " df=pd.DataFrame(), # Empty dataframe\n", " note=f\"Error processing data for {trait}: {str(e)}\"\n", " )\n", " print(\"Data was determined to be unusable and was not saved\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }