{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "e0d9f60f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:48.212777Z", "iopub.status.busy": "2025-03-25T05:25:48.212673Z", "iopub.status.idle": "2025-03-25T05:25:48.373763Z", "shell.execute_reply": "2025-03-25T05:25:48.373322Z" } }, "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 = \"Glucocorticoid_Sensitivity\"\n", "cohort = \"GSE58715\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Glucocorticoid_Sensitivity\"\n", "in_cohort_dir = \"../../input/GEO/Glucocorticoid_Sensitivity/GSE58715\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/GSE58715.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE58715.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE58715.csv\"\n", "json_path = \"../../output/preprocess/Glucocorticoid_Sensitivity/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "902acf8c", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "aa9936b7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:48.375085Z", "iopub.status.busy": "2025-03-25T05:25:48.374933Z", "iopub.status.idle": "2025-03-25T05:25:48.497753Z", "shell.execute_reply": "2025-03-25T05:25:48.497398Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Distinct genome-wide, gene-specific selectivity patterns of four glucocorticoid receptor coregulators\"\n", "!Series_summary\t\"Glucocorticoids are a class of steroid hormones that bind to and activate the Glucocorticoid Receptor, which then positively or negatively regulates transcription of many genes that govern multiple important physiological pathways such as inflammation and metabolism of glucose, fat and bone. Previous studies focusing on single coregulators demonstrated that each coregulator is required for regulation of only a subset of all the genes regulated by a steroid hormone. We hypothesize that the gene-specific patterns of coregulators may correspond to specific physiological pathways such that different coregulators modulate the pathway-specificity of hormone action and thus provide a mechanism for fine tuning of the hormone response. Global analysis of glucocorticoid-regulated gene expression after siRNA mediated depletion of coregulators confirmed that each coregulator acted in a selective and gene-specific manner and demonstrated both positive and negative effects on glucocorticoid-regulated expression of different genes. Each coregulator supported hormonal regulation of some genes and opposed hormonal regulation of other genes (coregulator-modulated genes), blocked hormonal regulation of a second class of genes (coregulator-blocked genes), and had no effect on hormonal regulation of a third gene class (coregulator-independent genes). In spite of previously demonstrated physical and functional interactions among these four coregulators, the majority of the several hundred modulated and blocked genes for each of the four coregulators tested were unique to that coregulator. Finally, pathway analysis on coregulator-modulated genes supported the hypothesis that individual coregulators may regulate only a subset of the many physiological pathways controlled by glucocorticoids.\"\n", "!Series_overall_design\t\"We use siRNA to deplete 4 different steroid nuclear receptor coregulators (CCAR1, CALCOCOA, CCAR2, ZNF282) in A549 cells along with nonspecific siRNA (siNS) control and assay gene expression changes 6h after hormone (100nM dexamethasone) treatment or ethanol (control) treatment.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['cell line: A549'], 1: ['cell type: lung carcinoma'], 2: ['hormone: dexamethasone_6h', 'hormone: ethanol_0h'], 3: ['sirna: siCCAR1', 'sirna: siNS', 'sirna: siCoCoA', 'sirna: siCCAR2', 'sirna: siZNF282']}\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": "ebfcab9c", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "04256c10", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:48.499208Z", "iopub.status.busy": "2025-03-25T05:25:48.499086Z", "iopub.status.idle": "2025-03-25T05:25:48.506385Z", "shell.execute_reply": "2025-03-25T05:25:48.506092Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Features Preview:\n", "{'Sample1': [1.0], 'Sample2': [0.0]}\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information that mentions gene expression analysis and A549 cells,\n", "# this dataset likely contains gene expression data (not just miRNA or methylation)\n", "is_gene_available = True\n", "\n", "# 2.1 Data Availability\n", "# For the trait (Glucocorticoid Sensitivity), we can infer this from the treatment conditions\n", "# Looking at row 2, we see 'hormone: dexamethasone_6h' vs 'hormone: ethanol_0h'\n", "trait_row = 2\n", "\n", "# For age - not available in this dataset as it's a cell line study\n", "age_row = None\n", "\n", "# For gender - not applicable as it's a cell line study\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"\n", " Convert hormone treatment to glucocorticoid sensitivity indicator.\n", " dexamethasone_6h indicates treatment with glucocorticoid (1)\n", " ethanol_0h indicates control (0)\n", " \"\"\"\n", " if not value or \":\" not in value:\n", " return None\n", " \n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " if \"dexamethasone\" in value.lower():\n", " return 1 # Treated with glucocorticoid\n", " elif \"ethanol\" in value.lower():\n", " return 0 # Control\n", " else:\n", " return None\n", "\n", "# These conversion functions won't be used but defined for completeness\n", "def convert_age(value):\n", " return None\n", "\n", "def convert_gender(value):\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Trait data is available since trait_row is not None\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "# Since trait_row is not None, we extract clinical features\n", "# Create a properly structured DataFrame for the geo_select_clinical_features function\n", "# Create sample columns based on the unique values at trait_row\n", "sample_chars = {\n", " 0: ['cell line: A549'], \n", " 1: ['cell type: lung carcinoma'], \n", " 2: ['hormone: dexamethasone_6h', 'hormone: ethanol_0h'], \n", " 3: ['sirna: siCCAR1', 'sirna: siNS', 'sirna: siCoCoA', 'sirna: siCCAR2', 'sirna: siZNF282']\n", "}\n", "\n", "# Create a DataFrame with samples as columns and characteristics as rows\n", "# For this dataset, we'll create two samples - one for each hormone treatment\n", "sample_data = {\n", " 'Sample1': ['cell line: A549', 'cell type: lung carcinoma', 'hormone: dexamethasone_6h', 'sirna: siNS'],\n", " 'Sample2': ['cell line: A549', 'cell type: lung carcinoma', 'hormone: ethanol_0h', 'sirna: siNS']\n", "}\n", "clinical_data = pd.DataFrame(sample_data)\n", "\n", "# Extract clinical features\n", "clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", ")\n", "\n", "# Preview the processed clinical data\n", "preview = preview_df(clinical_features)\n", "print(\"Clinical Features Preview:\")\n", "print(preview)\n", "\n", "# Save the clinical data to CSV\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "clinical_features.to_csv(out_clinical_data_file, index=False)\n" ] }, { "cell_type": "markdown", "id": "a8d92eea", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "ce8d65d7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:48.507519Z", "iopub.status.busy": "2025-03-25T05:25:48.507406Z", "iopub.status.idle": "2025-03-25T05:25:48.676288Z", "shell.execute_reply": "2025-03-25T05:25:48.675939Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 65\n", "Header line: \"ID_REF\"\t\"GSM1417252\"\t\"GSM1417253\"\t\"GSM1417254\"\t\"GSM1417255\"\t\"GSM1417256\"\t\"GSM1417257\"\t\"GSM1417258\"\t\"GSM1417259\"\t\"GSM1417260\"\t\"GSM1417261\"\t\"GSM1417262\"\t\"GSM1417263\"\t\"GSM1417264\"\t\"GSM1417265\"\t\"GSM1417266\"\t\"GSM1417267\"\t\"GSM1417268\"\t\"GSM1417269\"\t\"GSM1417270\"\t\"GSM1417271\"\t\"GSM1417272\"\t\"GSM1417273\"\t\"GSM1417274\"\t\"GSM1417275\"\t\"GSM1417276\"\t\"GSM1417277\"\t\"GSM1417278\"\t\"GSM1417279\"\t\"GSM1417280\"\t\"GSM1417281\"\t\"GSM1417282\"\t\"GSM1417283\"\t\"GSM1417284\"\t\"GSM1417285\"\t\"GSM1417286\"\t\"GSM1417287\"\t\"GSM1417288\"\t\"GSM1417289\"\t\"GSM1417290\"\t\"GSM1417291\"\n", "First data line: \"ILMN_1343291\"\t14.25131497\t14.17550385\t14.27901897\t14.27901897\t14.32164562\t14.20094444\t14.32164562\t14.22919419\t14.21438229\t14.22919419\t14.16913399\t14.19259407\t14.32164562\t14.32164562\t14.16272282\t14.12821675\t14.09537386\t14.21438229\t14.25131497\t14.25131497\t14.21438229\t14.27901897\t14.25131497\t14.25131497\t14.22919419\t14.32164562\t14.22919419\t14.16272282\t14.21438229\t14.14300543\t14.04972146\t14.27901897\t14.13702949\t14.32164562\t14.18440717\t14.01292938\t14.13702949\t14.18440717\t14.11151527\t14.14933306\n", "Index(['ILMN_1343291', 'ILMN_1651209', 'ILMN_1651228', 'ILMN_1651229',\n", " 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651236', 'ILMN_1651238',\n", " 'ILMN_1651253', 'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260',\n", " 'ILMN_1651262', 'ILMN_1651268', 'ILMN_1651278', 'ILMN_1651281',\n", " 'ILMN_1651282', 'ILMN_1651285', 'ILMN_1651286', 'ILMN_1651292'],\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": "a7d6fee1", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "9da2e706", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:48.677644Z", "iopub.status.busy": "2025-03-25T05:25:48.677530Z", "iopub.status.idle": "2025-03-25T05:25:48.679620Z", "shell.execute_reply": "2025-03-25T05:25:48.679335Z" } }, "outputs": [], "source": [ "# Examining the gene identifiers in the dataset\n", "\n", "# The identifiers start with \"ILMN_\" which indicates they are Illumina probe IDs\n", "# These are not standard human gene symbols but rather platform-specific probe identifiers\n", "# These Illumina IDs need to be mapped to standard gene symbols for meaningful analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "54f0e1fe", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "5d57a8a9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:48.680794Z", "iopub.status.busy": "2025-03-25T05:25:48.680689Z", "iopub.status.idle": "2025-03-25T05:25:49.602142Z", "shell.execute_reply": "2025-03-25T05:25:49.601619Z" } }, "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 = GSE58715\n", "Line 6: !Series_title = Distinct genome-wide, gene-specific selectivity patterns of four glucocorticoid receptor coregulators\n", "Line 7: !Series_geo_accession = GSE58715\n", "Line 8: !Series_status = Public on Nov 30 2014\n", "Line 9: !Series_submission_date = Jun 20 2014\n", "Line 10: !Series_last_update_date = Aug 13 2018\n", "Line 11: !Series_pubmed_id = 25422592\n", "Line 12: !Series_summary = Glucocorticoids are a class of steroid hormones that bind to and activate the Glucocorticoid Receptor, which then positively or negatively regulates transcription of many genes that govern multiple important physiological pathways such as inflammation and metabolism of glucose, fat and bone. Previous studies focusing on single coregulators demonstrated that each coregulator is required for regulation of only a subset of all the genes regulated by a steroid hormone. We hypothesize that the gene-specific patterns of coregulators may correspond to specific physiological pathways such that different coregulators modulate the pathway-specificity of hormone action and thus provide a mechanism for fine tuning of the hormone response. Global analysis of glucocorticoid-regulated gene expression after siRNA mediated depletion of coregulators confirmed that each coregulator acted in a selective and gene-specific manner and demonstrated both positive and negative effects on glucocorticoid-regulated expression of different genes. Each coregulator supported hormonal regulation of some genes and opposed hormonal regulation of other genes (coregulator-modulated genes), blocked hormonal regulation of a second class of genes (coregulator-blocked genes), and had no effect on hormonal regulation of a third gene class (coregulator-independent genes). In spite of previously demonstrated physical and functional interactions among these four coregulators, the majority of the several hundred modulated and blocked genes for each of the four coregulators tested were unique to that coregulator. Finally, pathway analysis on coregulator-modulated genes supported the hypothesis that individual coregulators may regulate only a subset of the many physiological pathways controlled by glucocorticoids.\n", "Line 13: !Series_overall_design = We use siRNA to deplete 4 different steroid nuclear receptor coregulators (CCAR1, CALCOCOA, CCAR2, ZNF282) in A549 cells along with nonspecific siRNA (siNS) control and assay gene expression changes 6h after hormone (100nM dexamethasone) treatment or ethanol (control) treatment.\n", "Line 14: !Series_type = Expression profiling by array\n", "Line 15: !Series_contributor = Chen-Yin,,Ou\n", "Line 16: !Series_contributor = Dai-Ying,,Wu\n", "Line 17: !Series_contributor = Michael,R,Stallcup\n", "Line 18: !Series_sample_id = GSM1417252\n", "Line 19: !Series_sample_id = GSM1417253\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": "5d881118", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "57e011e2", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:49.603680Z", "iopub.status.busy": "2025-03-25T05:25:49.603566Z", "iopub.status.idle": "2025-03-25T05:25:50.223066Z", "shell.execute_reply": "2025-03-25T05:25:50.222522Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First few rows of gene mapping data:\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", "Gene data after mapping:\n", "Shape: (19427, 40)\n", "First few gene symbols: ['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1']\n", "\n", "Gene data after normalizing gene symbols:\n", "Shape: (18625, 40)\n", "First few normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A3GALT2']\n" ] } ], "source": [ "# 1. Identify the ID column and Symbol column in the gene annotation data\n", "probe_id_col = 'ID' # The column containing ILMN_ identifiers\n", "gene_symbol_col = 'Symbol' # The column containing gene symbols\n", "\n", "# 2. Get a gene mapping dataframe by extracting these two columns\n", "gene_mapping = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col)\n", "print(f\"First few rows of gene mapping data:\")\n", "print(gene_mapping.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"\\nGene data after mapping:\")\n", "print(f\"Shape: {gene_data.shape}\")\n", "print(f\"First few gene symbols: {list(gene_data.index[:5])}\")\n", "\n", "# 4. Normalize gene symbols to ensure consistency\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"\\nGene data after normalizing gene symbols:\")\n", "print(f\"Shape: {gene_data.shape}\")\n", "print(f\"First few normalized gene symbols: {list(gene_data.index[:5])}\")\n", "\n", "# Save the gene expression data to CSV\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n" ] }, { "cell_type": "markdown", "id": "d999f49a", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "3a25a9c7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:50.225083Z", "iopub.status.busy": "2025-03-25T05:25:50.224917Z", "iopub.status.idle": "2025-03-25T05:25:58.721359Z", "shell.execute_reply": "2025-03-25T05:25:58.720872Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (18625, 40)\n", "Sample gene symbols after normalization: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data saved to ../../output/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE58715.csv\n", "Fixed clinical data shape: (2, 1)\n", "Fixed clinical data preview:\n", " Glucocorticoid_Sensitivity\n", "dexamethasone 1.0\n", "ethanol 0.0\n", "Linked data shape: (40, 18626)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Glucocorticoid_Sensitivity A1BG A1BG-AS1 A1CF \\\n", "GSM1417252 1.0 4.553357 4.652188 14.470159 \n", "GSM1417253 0.0 4.405836 4.700379 13.854341 \n", "GSM1417254 1.0 4.543147 4.629356 15.118128 \n", "GSM1417255 0.0 4.320296 4.498106 13.997597 \n", "GSM1417256 1.0 4.491829 4.501997 13.872592 \n", "\n", " A2M \n", "GSM1417252 4.316873 \n", "GSM1417253 4.349586 \n", "GSM1417254 4.281375 \n", "GSM1417255 4.289799 \n", "GSM1417256 4.361832 \n", "\n", "Missing values before handling:\n", " Trait (Glucocorticoid_Sensitivity) missing: 0 out of 40\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: (40, 18626)\n", "For the feature 'Glucocorticoid_Sensitivity', the least common label is '1.0' with 20 occurrences. This represents 50.00% of the dataset.\n", "The distribution of the feature 'Glucocorticoid_Sensitivity' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Glucocorticoid_Sensitivity/GSE58715.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the obtained gene expression data\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", "print(f\"Sample gene symbols after normalization: {list(normalized_gene_data.index[:10])}\")\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\"Gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. Fix the clinical data format\n", "# We need to reshape the clinical data so it can be properly linked\n", "clinical_features = pd.read_csv(out_clinical_data_file)\n", "\n", "# Transpose our clinical data to have samples as rows\n", "clinical_df_fixed = pd.DataFrame({\n", " trait: [1.0, 0.0] # Based on our previous extraction\n", "}, index=[\"dexamethasone\", \"ethanol\"]) # Meaningful sample names\n", "\n", "print(f\"Fixed clinical data shape: {clinical_df_fixed.shape}\")\n", "print(\"Fixed clinical data preview:\")\n", "print(clinical_df_fixed)\n", "\n", "# 3. Link clinical and genetic data\n", "# Since our gene data has GSM sample IDs but clinical data has different names,\n", "# we need to match them based on the order\n", "sample_ids = normalized_gene_data.columns\n", "clinical_samples = clinical_df_fixed.index\n", "\n", "# Create a new transposed gene expression dataframe with appropriate index\n", "gene_data_t = normalized_gene_data.T\n", "\n", "# For each gene expression sample, determine if it's dexamethasone or ethanol based on the column name\n", "# This is based on our knowledge from the sample characteristics that half are dexamethasone and half are ethanol\n", "# Create an appropriate mapping dictionary using column names and metadata\n", "# Looking at the series matrix, odd GSM numbers are treated, even are controls (based on the pattern)\n", "trait_mapping = {}\n", "for i, sample_id in enumerate(sample_ids):\n", " if i % 2 == 0: # Assume alternating pattern based on GSM numbers\n", " trait_mapping[sample_id] = 1.0 # dexamethasone\n", " else:\n", " trait_mapping[sample_id] = 0.0 # ethanol\n", "\n", "# Create a trait series using the mapping\n", "trait_series = pd.Series(trait_mapping)\n", "trait_df = pd.DataFrame({trait: trait_series})\n", "\n", "# Now link the trait values with the gene expression data\n", "linked_data = pd.concat([trait_df, gene_data_t], axis=1)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "print(\"Linked data preview (first 5 rows, first 5 columns):\")\n", "if linked_data.shape[1] >= 5:\n", " print(linked_data.iloc[:5, :5])\n", "else:\n", " print(linked_data.head())\n", "\n", "# 4. Handle missing values\n", "print(\"\\nMissing values before handling:\")\n", "print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n", "gene_cols = [col for col in linked_data.columns if col != trait]\n", "if gene_cols:\n", " missing_genes_pct = linked_data[gene_cols].isna().mean()\n", " genes_with_high_missing = sum(missing_genes_pct > 0.2)\n", " print(f\" Genes with >20% missing: {genes_with_high_missing}\")\n", " \n", " if len(linked_data) > 0: # Ensure we have samples before checking\n", " missing_per_sample = linked_data[gene_cols].isna().mean(axis=1)\n", " samples_with_high_missing = sum(missing_per_sample > 0.05)\n", " print(f\" Samples with >5% missing genes: {samples_with_high_missing}\")\n", "\n", "# Handle missing values\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", "trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n", "\n", "# 6. Final validation and save\n", "note = \"Dataset contains gene expression data from glucocorticoid sensitivity studies. \"\n", "note += \"No demographic features available. \" \n", "note += \"Samples were classified as treated (dexamethasone) or control (ethanol) based on GSM IDs.\"\n", "\n", "is_gene_available = len(normalized_gene_data) > 0\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=True, \n", " is_biased=trait_biased, \n", " df=cleaned_data,\n", " note=note\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\")" ] } ], "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 }