{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "71ac4024", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:10.693645Z", "iopub.status.busy": "2025-03-25T05:40:10.693407Z", "iopub.status.idle": "2025-03-25T05:40:10.861671Z", "shell.execute_reply": "2025-03-25T05:40:10.861278Z" } }, "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 = \"Height\"\n", "cohort = \"GSE101710\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Height\"\n", "in_cohort_dir = \"../../input/GEO/Height/GSE101710\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Height/GSE101710.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Height/gene_data/GSE101710.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Height/clinical_data/GSE101710.csv\"\n", "json_path = \"../../output/preprocess/Height/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "d9ea1f6f", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "a33ad8ae", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:10.863175Z", "iopub.status.busy": "2025-03-25T05:40:10.863017Z", "iopub.status.idle": "2025-03-25T05:40:11.189176Z", "shell.execute_reply": "2025-03-25T05:40:11.188828Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene expression analysis of Influenza vaccine response in Young and Old - Year 5\"\n", "!Series_summary\t\"We profiled gene expression from a stratified cohort of subjects to define influenza vaccine response in Young and Old\"\n", "!Series_overall_design\t\"Differential gene expression by human PBMCs from Healthy Adults receiving Influenza Vaccination (Fluvirin, Novartis). Healthy adults (older >65, younger 21-30 years) were recruited at seasonal Influenza Vaccination clinics organized by Yale University Health Services during October to December of 2014 – 2015 seasons. With informed consent, healthy individuals were recruited as per a protocol approved by Human Investigations Committee of the Yale University School of Medicine. Each subject was evaluated by a screening questionnaire determining self-reported demographic information, height, weight, medications and comorbid conditions. Participants with acute illness two weeks prior to vaccination were excluded from study. Blood samples were collected into BD Vacutainer Sodium Heparin tube at four different time points, once prior to administration of vaccine and three time points after vaccination on days 2, 7 and 28. Peripheral Blood Mononuclear Cells (PBMC) were isolated from heparinized blood using Histopaque 1077 in gradient centrifugation. About 1.0x10^7 freshly isolated PBMC were lysed in Triso and immediately stored in -800C. Total RNA in aqueous phase of Trisol - Chloroform was isolated in an automated QiaCube instrument using miRNeasy according to manufacturer’s instructions. Integrity of RNA samples were assessed by Agilent 2100 BioAnalyser Samples were processed for cRNA generation using Illumina TotalPrep cRNA Amplification Kit and subsequently hybridized to Human HT12-V4.0 BeadChip at Yale Center for Genomic Analysis (YGCA).\"\n", "!Series_overall_design\t\"\"\n", "!Series_overall_design\t\"The current data set, together with GSE59654, GSE59635, GSE59743, and GSE101709, represents subsets of the same overall study\"\n", "Sample Characteristics Dictionary:\n", "{0: ['subject status: Healthy Adults receiving Influenza Vaccination'], 1: ['age group: Older', 'age group: Frail', 'age group: Young'], 2: ['blood draw date: day 0; prior to administration of vaccine', 'blood draw date: after vaccination day 2', 'blood draw date: after vaccination day 7', 'blood draw date: after vaccination day 28', 'blood draw date: after vaccination day 25', 'blood draw date: after vaccination day 37', 'blood draw date: after vaccination day 41'], 3: ['cell type: Peripheral Blood Mononuclear Cells (PBMC)'], 4: ['immport_expsamp_acc: ImmPort:ES1167372', 'immport_expsamp_acc: ImmPort:ES1167373', 'immport_expsamp_acc: ImmPort:ES1167374', 'immport_expsamp_acc: ImmPort:ES1167375', 'immport_expsamp_acc: ImmPort:ES1167376', 'immport_expsamp_acc: ImmPort:ES1167377', 'immport_expsamp_acc: ImmPort:ES1167378', 'immport_expsamp_acc: ImmPort:ES1167379', 'immport_expsamp_acc: ImmPort:ES1167380', 'immport_expsamp_acc: ImmPort:ES1167381', 'immport_expsamp_acc: ImmPort:ES1167382', 'immport_expsamp_acc: ImmPort:ES1167383', 'immport_expsamp_acc: ImmPort:ES1167384', 'immport_expsamp_acc: ImmPort:ES1167385', 'immport_expsamp_acc: ImmPort:ES1167386', 'immport_expsamp_acc: ImmPort:ES1167387', 'immport_expsamp_acc: ImmPort:ES1167388', 'immport_expsamp_acc: ImmPort:ES1167389', 'immport_expsamp_acc: ImmPort:ES1167390', 'immport_expsamp_acc: ImmPort:ES1167391', 'immport_expsamp_acc: ImmPort:ES1167392', 'immport_expsamp_acc: ImmPort:ES1167393', 'immport_expsamp_acc: ImmPort:ES1167394', 'immport_expsamp_acc: ImmPort:ES1167395', 'immport_expsamp_acc: ImmPort:ES1167396', 'immport_expsamp_acc: ImmPort:ES1167397', 'immport_expsamp_acc: ImmPort:ES1167398', 'immport_expsamp_acc: ImmPort:ES1167399', 'immport_expsamp_acc: ImmPort:ES1167400', 'immport_expsamp_acc: ImmPort:ES1167401']}\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": "2a85bcd4", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "39c28fc9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:11.190462Z", "iopub.status.busy": "2025-03-25T05:40:11.190342Z", "iopub.status.idle": "2025-03-25T05:40:11.198270Z", "shell.execute_reply": "2025-03-25T05:40:11.197935Z" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import os\n", "import re\n", "from typing import Optional, Any, Dict, Callable\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on background information, this dataset contains gene expression data from Illumina HT12-V4.0 BeadChip\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "\n", "# For height (our trait): The background info mentions height was collected in screening questionnaire\n", "# But looking at the sample characteristics, there's no direct height data\n", "trait_row = None # Height data is not available in the sample characteristics\n", "\n", "# For age: Age group is available in row 1 \n", "age_row = 1 # Contains \"age group: Older\", \"age group: Frail\", \"age group: Young\"\n", "\n", "# For gender: No gender information in the sample characteristics\n", "gender_row = None # Gender data is not available\n", "\n", "# 2.2 Data Type Conversion\n", "\n", "# Define conversion functions for each variable\n", "def convert_trait(value: str) -> Optional[float]:\n", " \"\"\"Convert height data to float (continuous). Not used in this dataset.\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " try:\n", " return float(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "def convert_age(value: str) -> Optional[int]:\n", " \"\"\"Convert age group to binary (0 for Young, 1 for Older/Frail).\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " value = value.lower()\n", " if 'young' in value:\n", " return 0\n", " elif 'older' in value or 'frail' in value:\n", " return 1\n", " else:\n", " return None\n", "\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"Convert gender to binary (0 for female, 1 for male). Not used in this dataset.\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " value = value.lower()\n", " if 'female' in value or 'f' == value:\n", " return 0\n", " elif 'male' in value or 'm' == value:\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# is_trait_available is False since trait_row is 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", "# We skip this step since trait_row is None (no height data available)\n", "# If we had trait data, we would execute:\n", "# if trait_row is not None:\n", "# # Assuming clinical_data is loaded from a previous step\n", "# clinical_data = pd.read_csv(os.path.join(in_cohort_dir, \"clinical_data.csv\"))\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 if age_row is not None else None,\n", "# gender_row=gender_row,\n", "# convert_gender=convert_gender if gender_row is not None else None\n", "# )\n", "# \n", "# # Preview the dataframe\n", "# preview = preview_df(selected_clinical_df)\n", "# print(preview)\n", "# \n", "# # Save the clinical data\n", "# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "# selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n" ] }, { "cell_type": "markdown", "id": "7b7182cb", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "1d75ee47", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:11.199389Z", "iopub.status.busy": "2025-03-25T05:40:11.199277Z", "iopub.status.idle": "2025-03-25T05:40:11.715881Z", "shell.execute_reply": "2025-03-25T05:40:11.715495Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting gene data from matrix file:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully extracted gene data with 46892 rows\n", "First 20 gene IDs:\n", "Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n", " 'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n", " 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n", " 'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n", " 'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\n", " dtype='object', name='ID')\n", "\n", "Gene expression data available: True\n" ] } ], "source": [ "# 1. Get the file paths for the SOFT file and matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Extract gene expression data from the matrix file\n", "try:\n", " print(\"Extracting gene data from matrix file:\")\n", " gene_data = get_genetic_data(matrix_file)\n", " if gene_data.empty:\n", " print(\"Extracted gene expression data is empty\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n", " print(\"First 20 gene IDs:\")\n", " print(gene_data.index[:20])\n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n", " print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n", " is_gene_available = False\n", "\n", "print(f\"\\nGene expression data available: {is_gene_available}\")\n" ] }, { "cell_type": "markdown", "id": "430a6a09", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "8f0878d5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:11.717182Z", "iopub.status.busy": "2025-03-25T05:40:11.717051Z", "iopub.status.idle": "2025-03-25T05:40:11.719025Z", "shell.execute_reply": "2025-03-25T05:40:11.718693Z" } }, "outputs": [], "source": [ "# These are Illumina microarray probe identifiers (ILMN_*), not human gene symbols.\n", "# They need to be mapped to official gene symbols for biological interpretation.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "7e8b2636", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "fb407d5b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:11.720203Z", "iopub.status.busy": "2025-03-25T05:40:11.720089Z", "iopub.status.idle": "2025-03-25T05:40:12.690269Z", "shell.execute_reply": "2025-03-25T05:40:12.689866Z" } }, "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 = GSE101710\n", "Line 6: !Series_title = Gene expression analysis of Influenza vaccine response in Young and Old - Year 5\n", "Line 7: !Series_geo_accession = GSE101710\n", "Line 8: !Series_status = Public on May 26 2019\n", "Line 9: !Series_submission_date = Jul 20 2017\n", "Line 10: !Series_last_update_date = Jul 25 2021\n", "Line 11: !Series_pubmed_id = 30239628\n", "Line 12: !Series_pubmed_id = 32060136\n", "Line 13: !Series_summary = We profiled gene expression from a stratified cohort of subjects to define influenza vaccine response in Young and Old\n", "Line 14: !Series_overall_design = Differential gene expression by human PBMCs from Healthy Adults receiving Influenza Vaccination (Fluvirin, Novartis). Healthy adults (older >65, younger 21-30 years) were recruited at seasonal Influenza Vaccination clinics organized by Yale University Health Services during October to December of 2014 – 2015 seasons. With informed consent, healthy individuals were recruited as per a protocol approved by Human Investigations Committee of the Yale University School of Medicine. Each subject was evaluated by a screening questionnaire determining self-reported demographic information, height, weight, medications and comorbid conditions. Participants with acute illness two weeks prior to vaccination were excluded from study. Blood samples were collected into BD Vacutainer Sodium Heparin tube at four different time points, once prior to administration of vaccine and three time points after vaccination on days 2, 7 and 28. Peripheral Blood Mononuclear Cells (PBMC) were isolated from heparinized blood using Histopaque 1077 in gradient centrifugation. About 1.0x10^7 freshly isolated PBMC were lysed in Triso and immediately stored in -800C. Total RNA in aqueous phase of Trisol - Chloroform was isolated in an automated QiaCube instrument using miRNeasy according to manufacturer’s instructions. Integrity of RNA samples were assessed by Agilent 2100 BioAnalyser Samples were processed for cRNA generation using Illumina TotalPrep cRNA Amplification Kit and subsequently hybridized to Human HT12-V4.0 BeadChip at Yale Center for Genomic Analysis (YGCA).\n", "Line 15: !Series_overall_design =\n", "Line 16: !Series_overall_design = The current data set, together with GSE59654, GSE59635, GSE59743, and GSE101709, represents subsets of the same overall study\n", "Line 17: !Series_type = Expression profiling by array\n", "Line 18: !Series_contributor = Albert,C,Shaw\n", "Line 19: !Series_contributor = Subhasis,,Mohanty\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Species': [nan, nan, nan, nan, nan], 'Source': [nan, nan, nan, nan, nan], 'Search_Key': [nan, nan, nan, nan, nan], 'Transcript': [nan, nan, nan, nan, nan], 'ILMN_Gene': [nan, nan, nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan, nan, nan], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Unigene_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': [nan, nan, nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB'], 'Protein_Product': [nan, nan, nan, nan, 'thrB'], 'Probe_Id': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5090180, 6510136, 7560739, 1450438, 1240647], 'Probe_Type': [nan, nan, nan, nan, nan], 'Probe_Start': [nan, nan, nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT', 'TCTGTCACTGTCAGGAAAGTGGTAAAACTGCAACTCAATTACTGCAATGC', 'CTTGTGCCTGAGCTGTCAAAAGTAGAGCACGTCGCCGAGATGAAGGGCGC'], 'Chromosome': [nan, nan, nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan, nan, nan], 'Cytoband': [nan, nan, nan, nan, nan], 'Definition': [nan, nan, nan, nan, nan], 'Ontology_Component': [nan, nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, 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": "a8672103", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "c648fd21", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:12.691729Z", "iopub.status.busy": "2025-03-25T05:40:12.691595Z", "iopub.status.idle": "2025-03-25T05:40:14.180602Z", "shell.execute_reply": "2025-03-25T05:40:14.180206Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created gene mapping with 44837 rows\n", "Gene mapping preview:\n", " ID Gene\n", "0 ILMN_1343048 phage_lambda_genome\n", "1 ILMN_1343049 phage_lambda_genome\n", "2 ILMN_1343050 phage_lambda_genome:low\n", "3 ILMN_1343052 phage_lambda_genome:low\n", "4 ILMN_1343059 thrB\n", "\n", "Applying gene mapping to convert probe-level data to gene-level data...\n", "Successfully converted to gene-level data with 21344 genes\n", "First 10 gene symbols:\n", "Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2',\n", " 'A4GALT', 'A4GNT'],\n", " dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Height/gene_data/GSE101710.csv\n" ] } ], "source": [ "# 1. Identify the correct columns for mapping\n", "# From the gene annotation preview, we can see:\n", "# - 'ID' contains the Illumina probe identifiers (ILMN_*)\n", "# - 'Symbol' contains gene symbols\n", "\n", "# 2. Create gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n", "print(f\"Created gene mapping with {len(gene_mapping)} rows\")\n", "print(\"Gene mapping preview:\")\n", "print(gene_mapping.head())\n", "\n", "# 3. Apply the gene mapping to convert probe-level data to gene-level data\n", "# This handles the many-to-many relationship between probes and genes\n", "try:\n", " print(\"\\nApplying gene mapping to convert probe-level data to gene-level data...\")\n", " gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", " \n", " print(f\"Successfully converted to gene-level data with {len(gene_data)} genes\")\n", " print(\"First 10 gene symbols:\")\n", " print(gene_data.index[:10])\n", " \n", " # Check if the dataset has non-empty gene data\n", " if gene_data.empty:\n", " print(\"Warning: No genes were successfully mapped\")\n", " is_gene_available = False\n", " else:\n", " is_gene_available = True\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", " \n", "except Exception as e:\n", " print(f\"Error in gene mapping: {e}\")\n", " is_gene_available = False\n" ] }, { "cell_type": "markdown", "id": "2bc40526", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "1ac71355", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:14.182021Z", "iopub.status.busy": "2025-03-25T05:40:14.181882Z", "iopub.status.idle": "2025-03-25T05:40:14.814558Z", "shell.execute_reply": "2025-03-25T05:40:14.814205Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (46893, 79)\n", "Gene data shape after normalization: (0, 79)\n", "Normalized gene data saved to ../../output/preprocess/Height/gene_data/GSE101710.csv\n", "Clinical data saved to ../../output/preprocess/Height/clinical_data/GSE101710.csv\n", "Linked data shape: (79, 2)\n", "Abnormality detected in the cohort: GSE101710. Preprocessing failed.\n", "Dataset usability: False\n", "Dataset does not contain Height data and cannot be used for association studies.\n" ] } ], "source": [ "import numpy as np\n", "import os\n", "import gzip\n", "\n", "# 1. Extract gene expression data using the alternative approach that worked in Step 3\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", " gene_data = pd.read_csv(file, sep='\\t', index_col=0)\n", "\n", "# Check if we have gene data before proceeding\n", "if gene_data.empty:\n", " print(\"No gene expression data found in the matrix file.\")\n", " is_gene_available = False\n", "else:\n", " is_gene_available = True\n", " print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", "\n", " # Normalize gene symbols using the NCBI Gene database information\n", " try:\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 to the output file\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", " except Exception as e:\n", " print(f\"Error normalizing gene data: {e}\")\n", " is_gene_available = False\n", " normalized_gene_data = gene_data # Use original data if normalization fails\n", "\n", "# 2. Link clinical and genetic data\n", "# In Step 2, we determined that Height data is not available in this dataset (trait_row = None)\n", "# Create a minimal clinical dataframe with the trait column (containing NaNs)\n", "if is_gene_available:\n", " sample_ids = gene_data.columns\n", " minimal_clinical_df = pd.DataFrame(index=sample_ids)\n", " minimal_clinical_df[trait] = np.nan # Add the trait column with NaN values\n", "\n", " # If we have age and gender data from Step 2, add those columns\n", " if age_row is not None:\n", " minimal_clinical_df['Age'] = get_feature_data(clinical_data, age_row, 'Age', convert_age).iloc[0]\n", "\n", " if gender_row is not None:\n", " minimal_clinical_df['Gender'] = get_feature_data(clinical_data, gender_row, 'Gender', convert_gender).iloc[0]\n", "\n", " minimal_clinical_df.index.name = 'Sample'\n", "\n", " # Save this minimal clinical data for reference\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " minimal_clinical_df.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", " # Create a linked dataset \n", " if is_gene_available and normalized_gene_data is not None:\n", " linked_data = pd.concat([minimal_clinical_df, normalized_gene_data.T], axis=1)\n", " linked_data.index.name = 'Sample'\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " else:\n", " linked_data = minimal_clinical_df\n", " print(\"No gene data to link with clinical data.\")\n", "else:\n", " # Create a minimal dataframe with just the trait for the validation step\n", " linked_data = pd.DataFrame({trait: [np.nan]})\n", " print(\"No gene data available, creating minimal dataframe for validation.\")\n", "\n", "# 4 & 5. Validate and save cohort information\n", "# Since trait_row was None in Step 2, we know Height data is not available\n", "is_trait_available = False # Height data is not available\n", "\n", "note = \"Dataset contains gene expression data but no Height measurements. This dataset is not usable for studying Height associations.\"\n", "\n", "# For datasets without trait data, we set is_biased to False\n", "# This indicates the dataset is not usable due to missing trait data, not due to bias\n", "is_biased = False\n", "\n", "# Final 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=is_gene_available, \n", " is_trait_available=is_trait_available, \n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=note\n", ")\n", "\n", "# 6. Since there is no trait data, the dataset is not usable for our association study\n", "# So we should not save it to out_data_file\n", "print(f\"Dataset usability: {is_usable}\")\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Dataset does not contain Height data and cannot be used for association studies.\")" ] } ], "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 }