{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "e1c4e17f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:05.671523Z", "iopub.status.busy": "2025-03-25T05:40:05.671421Z", "iopub.status.idle": "2025-03-25T05:40:05.837960Z", "shell.execute_reply": "2025-03-25T05:40:05.837631Z" } }, "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 = \"GSE101709\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Height\"\n", "in_cohort_dir = \"../../input/GEO/Height/GSE101709\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Height/GSE101709.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Height/gene_data/GSE101709.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Height/clinical_data/GSE101709.csv\"\n", "json_path = \"../../output/preprocess/Height/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "da535d59", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "447b2db5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:05.839480Z", "iopub.status.busy": "2025-03-25T05:40:05.839335Z", "iopub.status.idle": "2025-03-25T05:40:06.230956Z", "shell.execute_reply": "2025-03-25T05:40:06.230643Z" } }, "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 4\"\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 2013 – 2014 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 -80C. 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 GSE101710, represents subsets of the same overall study\"\n", "Sample Characteristics Dictionary:\n", "{0: ['subject status: Healthy Adults receiving Influenza Vaccination'], 1: ['age group: Frail', 'age group: Older', 'age group: Young'], 2: ['blood draw date: after vaccination day 2', 'blood draw date: after vaccination day 7', 'blood draw date: after vaccination day 28', 'blood draw date: day 0; prior to administration of vaccine', 'blood draw date: after vaccination day 43'], 3: ['cell type: Peripheral Blood Mononuclear Cells (PBMC)'], 4: ['immport_expsamp_acc: ImmPort:ES1167274', 'immport_expsamp_acc: ImmPort:ES1167275', 'immport_expsamp_acc: ImmPort:ES1167276', 'immport_expsamp_acc: ImmPort:ES1167277', 'immport_expsamp_acc: ImmPort:ES1167278', 'immport_expsamp_acc: ImmPort:ES1167279', 'immport_expsamp_acc: ImmPort:ES1167280', 'immport_expsamp_acc: ImmPort:ES1167281', 'immport_expsamp_acc: ImmPort:ES1167282', 'immport_expsamp_acc: ImmPort:ES1167283', 'immport_expsamp_acc: ImmPort:ES1167284', 'immport_expsamp_acc: ImmPort:ES1167285', 'immport_expsamp_acc: ImmPort:ES1167286', 'immport_expsamp_acc: ImmPort:ES1167287', 'immport_expsamp_acc: ImmPort:ES1167288', 'immport_expsamp_acc: ImmPort:ES1167289', 'immport_expsamp_acc: ImmPort:ES1167290', 'immport_expsamp_acc: ImmPort:ES1167291', 'immport_expsamp_acc: ImmPort:ES1167292', 'immport_expsamp_acc: ImmPort:ES1167293', 'immport_expsamp_acc: ImmPort:ES1167294', 'immport_expsamp_acc: ImmPort:ES1167295', 'immport_expsamp_acc: ImmPort:ES1167296', 'immport_expsamp_acc: ImmPort:ES1167297', 'immport_expsamp_acc: ImmPort:ES1167298', 'immport_expsamp_acc: ImmPort:ES1167299', 'immport_expsamp_acc: ImmPort:ES1167300', 'immport_expsamp_acc: ImmPort:ES1167301', 'immport_expsamp_acc: ImmPort:ES1167302', 'immport_expsamp_acc: ImmPort:ES1167303']}\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": "f1de7f5c", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "0a3c1212", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:06.232312Z", "iopub.status.busy": "2025-03-25T05:40:06.232192Z", "iopub.status.idle": "2025-03-25T05:40:06.251046Z", "shell.execute_reply": "2025-03-25T05:40:06.250758Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A new JSON file was created at: ../../output/preprocess/Height/cohort_info.json\n" ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. Gene Expression Data Availability\n", "# From background information, we see it contains gene expression data from human PBMC samples\n", "# using \"Human HT12-V4.0 BeadChip\" - this is a gene expression microarray\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# For height (our trait), we can see in the background info that height data was collected\n", "# \"Each subject was evaluated by a screening questionnaire determining self-reported demographic information, height, weight...\"\n", "# However, we don't see height data in the sample characteristics dictionary\n", "trait_row = None # Height data not available in the sample characteristics\n", "\n", "# For age, the sample characteristics dictionary shows age group data at index 1\n", "age_row = 1 # Age group data is available at index 1\n", "\n", "# Gender data is not explicitly available in the sample characteristics\n", "gender_row = None # Gender data not available\n", "\n", "# 2.2 Data Type Conversion\n", "# For height (not available, but define a function anyway)\n", "def convert_trait(value):\n", " if not value or pd.isna(value):\n", " return None\n", " \n", " # Extract the value after colon if it exists\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " try:\n", " # Height would typically be a continuous value\n", " return float(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "# For age (available as age group)\n", "def convert_age(value):\n", " if not value or pd.isna(value):\n", " return None\n", " \n", " # Extract the value after colon if it exists\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert age group to binary (Young=0, Older/Frail=1)\n", " if 'young' in value.lower():\n", " return 0\n", " elif 'older' in value.lower() or 'frail' in value.lower():\n", " return 1\n", " else:\n", " return None\n", "\n", "# For gender (not available, but define a function anyway)\n", "def convert_gender(value):\n", " if not value or pd.isna(value):\n", " return None\n", " \n", " # Extract the value after colon if it exists\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert gender to binary (female=0, male=1)\n", " if 'female' in value.lower() or 'f' == value.lower():\n", " return 0\n", " elif 'male' in value.lower() or 'm' == value.lower():\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# The trait data is not available (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 - Skip as trait_row is None\n" ] }, { "cell_type": "markdown", "id": "1d360dfe", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "a781107c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:06.252196Z", "iopub.status.busy": "2025-03-25T05:40:06.252086Z", "iopub.status.idle": "2025-03-25T05:40:06.912356Z", "shell.execute_reply": "2025-03-25T05:40:06.911956Z" } }, "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": "1e48f28c", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "c27d7239", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:06.913760Z", "iopub.status.busy": "2025-03-25T05:40:06.913643Z", "iopub.status.idle": "2025-03-25T05:40:06.915557Z", "shell.execute_reply": "2025-03-25T05:40:06.915248Z" } }, "outputs": [], "source": [ "# Based on the gene identifiers observed, these are Illumina microarray probe IDs (ILMN_) \n", "# rather than standard human gene symbols. They need to be mapped to gene symbols for proper analysis.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "71709be7", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "f07d43c6", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:06.916803Z", "iopub.status.busy": "2025-03-25T05:40:06.916694Z", "iopub.status.idle": "2025-03-25T05:40:07.880629Z", "shell.execute_reply": "2025-03-25T05:40:07.880278Z" } }, "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 = GSE101709\n", "Line 6: !Series_title = Gene expression analysis of Influenza vaccine response in Young and Old - Year 4\n", "Line 7: !Series_geo_accession = GSE101709\n", "Line 8: !Series_status = Public on Jan 08 2020\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 = 32060136\n", "Line 12: !Series_summary = We profiled gene expression from a stratified cohort of subjects to define influenza vaccine response in Young and Old\n", "Line 13: !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 2013 – 2014 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 -80C. 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 14: !Series_overall_design =\n", "Line 15: !Series_overall_design = The current data set, together with GSE59654, GSE59635, GSE59743, and GSE101710, represents subsets of the same overall study\n", "Line 16: !Series_type = Expression profiling by array\n", "Line 17: !Series_contributor = Albert,C,Shaw\n", "Line 18: !Series_contributor = Subhasis,,Mohanty\n", "Line 19: !Series_contributor = Hailong,,Meng\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": "3c02687f", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "78300917", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:07.882045Z", "iopub.status.busy": "2025-03-25T05:40:07.881913Z", "iopub.status.idle": "2025-03-25T05:40:08.072767Z", "shell.execute_reply": "2025-03-25T05:40:08.072398Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created gene mapping with 44837 entries\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", "Converted probe-level data to gene-level expression data with 21344 genes\n", "First few genes:\n", "Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2',\n", " 'A4GALT', 'A4GNT'],\n", " dtype='object', name='Gene')\n", "Successfully mapped expression data to 21344 genes\n" ] } ], "source": [ "# 1. Determine which columns contain the probe IDs and gene symbols\n", "# From the previous output, we can see:\n", "# - 'ID' column contains the probe IDs (ILMN_*) that match our gene expression data\n", "# - 'Symbol' column contains the gene symbols we need to map to\n", "\n", "# 2. Extract the gene mapping dataframe from the gene annotation\n", "probe_id_col = 'ID'\n", "gene_symbol_col = 'Symbol'\n", "\n", "# Create the mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col)\n", "print(f\"Created gene mapping with {len(gene_mapping)} entries\")\n", "print(\"Gene mapping preview:\")\n", "print(gene_mapping.head())\n", "\n", "# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"Converted probe-level data to gene-level expression data with {len(gene_data)} genes\")\n", "print(\"First few genes:\")\n", "print(gene_data.index[:10])\n", "\n", "# Check if we have successfully extracted genes\n", "if gene_data.empty:\n", " print(\"Warning: Mapped gene expression data is empty\")\n", " is_gene_available = False\n", "else:\n", " print(f\"Successfully mapped expression data to {len(gene_data.index)} genes\")\n", " is_gene_available = True\n" ] }, { "cell_type": "markdown", "id": "90977699", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "4c6428d8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:08.074052Z", "iopub.status.busy": "2025-03-25T05:40:08.073931Z", "iopub.status.idle": "2025-03-25T05:40:09.824683Z", "shell.execute_reply": "2025-03-25T05:40:09.824296Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data before normalization: (21344, 98)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data after normalization: (20158, 98)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Height/gene_data/GSE101709.csv\n", "Added Age data to clinical dataframe\n", "Clinical data saved to ../../output/preprocess/Height/clinical_data/GSE101709.csv\n", "Linked data shape: (98, 20160)\n", "Dataset usability: False\n", "Dataset does not contain Height data and cannot be used for association studies.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "import numpy as np\n", "import os\n", "\n", "print(f\"Gene data before normalization: {gene_data.shape}\")\n", "\n", "try:\n", " # Try to normalize gene symbols using the NCBI Gene database\n", " normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Gene data after normalization: {normalized_gene_data.shape}\")\n", " \n", " # If normalization resulted in empty dataframe, use the original gene data\n", " if normalized_gene_data.empty:\n", " print(\"Warning: Normalization resulted in empty gene data. Using original gene data instead.\")\n", " normalized_gene_data = gene_data\n", " \n", "except Exception as e:\n", " print(f\"Error during gene symbol normalization: {e}\")\n", " print(\"Using original gene data instead.\")\n", " normalized_gene_data = gene_data\n", "\n", "# Save gene expression 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\"Gene expression data saved to {out_gene_data_file}\")\n", "\n", "# 2. Create a minimal clinical dataframe since we don't have trait data\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 data from Step 2, add that column\n", "if age_row is not None:\n", " try:\n", " minimal_clinical_df['Age'] = get_feature_data(clinical_data, age_row, 'Age', convert_age).iloc[0]\n", " print(\"Added Age data to clinical dataframe\")\n", " except Exception as e:\n", " print(f\"Error adding age data: {e}\")\n", "\n", "# If we have gender data from Step 2, add that column\n", "if gender_row is not None:\n", " try:\n", " minimal_clinical_df['Gender'] = get_feature_data(clinical_data, gender_row, 'Gender', convert_gender).iloc[0]\n", " print(\"Added Gender data to clinical dataframe\")\n", " except Exception as e:\n", " print(f\"Error adding gender data: {e}\")\n", "\n", "# Save clinical data\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", "# Link clinical and genetic data\n", "try:\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", "except Exception as e:\n", " print(f\"Error linking clinical and genetic data: {e}\")\n", " # Create a minimal dataframe with just the trait for validation\n", " linked_data = minimal_clinical_df\n", " print(f\"Using only clinical data with shape: {linked_data.shape}\")\n", "\n", "# Since trait_row was None in Step 2, we know Height data is not available\n", "is_trait_available = False \n", "\n", "# Add a detailed note about the dataset limitations\n", "note = \"Dataset contains gene expression data but no Height measurements. The dataset includes age group information (Young vs Older/Frail) but lacks the specific trait data needed for Height association studies.\"\n", "\n", "# For datasets without trait data, we set is_biased to False\n", "is_biased = False\n", "\n", "# Final validation and data quality assessment\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", "# Only save the linked data if it's usable for our study\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 }