{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "8afbc994", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:36.150236Z", "iopub.status.busy": "2025-03-25T05:25:36.149988Z", "iopub.status.idle": "2025-03-25T05:25:36.317614Z", "shell.execute_reply": "2025-03-25T05:25:36.317226Z" } }, "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 = \"GSE57795\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Glucocorticoid_Sensitivity\"\n", "in_cohort_dir = \"../../input/GEO/Glucocorticoid_Sensitivity/GSE57795\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/GSE57795.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE57795.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE57795.csv\"\n", "json_path = \"../../output/preprocess/Glucocorticoid_Sensitivity/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "538e4dbb", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "f7e68a2e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:36.318846Z", "iopub.status.busy": "2025-03-25T05:25:36.318695Z", "iopub.status.idle": "2025-03-25T05:25:36.515277Z", "shell.execute_reply": "2025-03-25T05:25:36.514708Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"in vivo dexamethasone-induced gene expression in pediatric acute lymphoblastic leukemia patient-derived xenografts\"\n", "!Series_summary\t\"Glucocorticoids are critical components of combination chemotherapy regimens in pediatric acute lymphoblastic leukemia (ALL). The pro-apoptotic BIM protein is an important mediator of glucocorticoid-induced apoptosis in normal and malignant lymphocytes, while the anti-apoptotic BCL2 confers resistance. The signaling pathways regulating BIM and BCL2 expression in glucocorticoid-treated lymphoid cells remain unclear. In this study, pediatric ALL patient-derived xenografts (PDXs) inherently sensitive or resistant to glucocorticoids were exposed to dexamethasone in vivo. In order to understand the basis for differential in vivo glucocorticoid sensitivity of PDXs, microarray analysis of gene expression was carried out on 5 each of dexamethasone-sensitive and resistant PDXs . This provided a global understanding of dexamethasone-induced signaling cascades in ALL cells in vivo, and especialy identified the genes that are involved in transducing the apoptotic signal, upstream of BIM/BCL2 dynamic interactions.\"\n", "!Series_overall_design\t\"ALL xenograft cells were inoculated by tail-vein injection into NOD/SCID mice, and engraftment was monitored weekly. When >70% %huCD45+ engraftment in the peripheral blood was apparent, which occurred 8-10 weeks post-transplantation, mice were treated with either dexamethasone (15 mg/kg) or vehicle control by intra-peritoneal (IP) injection, and culled at 8 hours following the treatment. Cell suspensions of spleens were prepared and mononuclear cells enriched to >97% human by density gradient centrifugation. RNA was extracted using the RNeasy Mini Kit (QIAGEN, Valencia, CA, USA), and RNA samples with integrity number (RIN) > 8.0 were amplified and hybridized onto Illumina HumanWG-6 v3 Expression BeadChips (6 samples/chip). All chips (with associated reagents) were purchased from Illumina, and scanned on the Illumina BeadArray Reader according to the manufacturer’s instructions. Microarray data were analyzed using the online modules in GenePattern.\"\n", "!Series_overall_design\t\"10 xenografts were derived from patients of 5 dexamethasone-good responder and 5 dexamethasone-poor responder. Each xenograft was innoculated into 5-6 mice, and treated with dexamethasone (15 mg/kg) or vehicle control. In total spleen-harvest xenograft samples from 58 mice were analyzed using microarray.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['strain: NOD/SCID'], 1: ['injection: ALL patient-derived xenograft cells were inoculated by tail-vein injection'], 2: ['age (mouse): xenograft cells injected at 6-10 weeks'], 3: ['treatment: control', 'treatment: 8h dexamethasone'], 4: ['tissue: xenograft cells (>95% hCD45+ cells) from mouse spleens'], 5: ['dexamethasone response: Sensitive patient', 'dexamethasone response: Resistant patient']}\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": "058b9107", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "bc56ea19", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:36.516512Z", "iopub.status.busy": "2025-03-25T05:25:36.516393Z", "iopub.status.idle": "2025-03-25T05:25:36.527076Z", "shell.execute_reply": "2025-03-25T05:25:36.526678Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data preview: {'Sample_0_Sensitive patient': [1.0], 'Sample_0_Resistant patient': [0.0], 'Sample_1_Sensitive patient': [1.0], 'Sample_1_Resistant patient': [0.0]}\n", "Clinical data saved to ../../output/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE57795.csv\n" ] } ], "source": [ "import os\n", "import json\n", "import pandas as pd\n", "import numpy as np\n", "from typing import Optional, Dict, Any, Callable\n", "\n", "# Define whether gene expression data is available\n", "# Based on the background information, this is a microarray analysis of gene expression\n", "is_gene_available = True\n", "\n", "# Identify rows in sample characteristics for trait, age, and gender\n", "# Trait: Glucocorticoid sensitivity - available in row 5\n", "trait_row = 5\n", "# Age: Only mouse age is mentioned, not human patient age\n", "age_row = None\n", "# Gender: No information about gender\n", "gender_row = None\n", "\n", "# Define conversion functions\n", "def convert_trait(value_str):\n", " if pd.isna(value_str) or not isinstance(value_str, str):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value_str:\n", " value = value_str.split(':', 1)[1].strip()\n", " else:\n", " value = value_str.strip()\n", " \n", " # Convert to binary: Sensitive = 1, Resistant = 0\n", " if 'Sensitive' in value:\n", " return 1\n", " elif 'Resistant' in value:\n", " return 0\n", " else:\n", " return None\n", "\n", "# Age conversion function (not used but defined for completeness)\n", "def convert_age(value_str):\n", " return None # No human patient age data available\n", "\n", "# Gender conversion function (not used but defined for completeness)\n", "def convert_gender(value_str):\n", " return None # No gender data available\n", "\n", "# Determine if trait data is available\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort information\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", "# If trait data is available, extract clinical features\n", "if trait_row is not None:\n", " # Create a proper clinical data DataFrame with all available sample characteristics\n", " # Each row in the sample characteristic dictionary becomes a column\n", " # Create a dataframe with one column per characteristic feature\n", " sample_chars = {\n", " 0: ['strain: NOD/SCID'], \n", " 1: ['injection: ALL patient-derived xenograft cells were inoculated by tail-vein injection'], \n", " 2: ['age (mouse): xenograft cells injected at 6-10 weeks'], \n", " 3: ['treatment: control', 'treatment: 8h dexamethasone'], \n", " 4: ['tissue: xenograft cells (>95% hCD45+ cells) from mouse spleens'], \n", " 5: ['dexamethasone response: Sensitive patient', 'dexamethasone response: Resistant patient']\n", " }\n", " \n", " # We need to simulate the expected clinical data format for geo_select_clinical_features\n", " # Each column will represent a sample, and rows will be characteristics\n", " clinical_data = pd.DataFrame(index=range(max(sample_chars.keys())+1))\n", " \n", " # Add sample columns based on characteristic combinations\n", " # For this dataset, we have 2 main groups of samples: Sensitive and Resistant patients\n", " # And within each, there are samples with control vs dexamethasone treatment\n", " for sample_idx, treatment in enumerate(['treatment: control', 'treatment: 8h dexamethasone']):\n", " for response in ['dexamethasone response: Sensitive patient', 'dexamethasone response: Resistant patient']:\n", " col_name = f\"Sample_{sample_idx}_{response.split(':')[1].strip()}\"\n", " clinical_data[col_name] = None\n", " \n", " # Fill in the characteristics for this sample\n", " clinical_data.loc[0, col_name] = sample_chars[0][0] # strain\n", " clinical_data.loc[1, col_name] = sample_chars[1][0] # injection\n", " clinical_data.loc[2, col_name] = sample_chars[2][0] # age\n", " clinical_data.loc[3, col_name] = treatment # treatment\n", " clinical_data.loc[4, col_name] = sample_chars[4][0] # tissue\n", " clinical_data.loc[5, col_name] = response # response\n", " \n", " # Extract clinical features\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the dataframe\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical data preview:\", preview)\n", " \n", " # Create output directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " \n", " # Save clinical data to CSV\n", " selected_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": "4f4179ee", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "cc89c285", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:36.528305Z", "iopub.status.busy": "2025-03-25T05:25:36.528065Z", "iopub.status.idle": "2025-03-25T05:25:36.866651Z", "shell.execute_reply": "2025-03-25T05:25:36.866276Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 73\n", "Header line: \"ID_REF\"\t\"GSM1388640\"\t\"GSM1388641\"\t\"GSM1388642\"\t\"GSM1388643\"\t\"GSM1388644\"\t\"GSM1388645\"\t\"GSM1388646\"\t\"GSM1388647\"\t\"GSM1388648\"\t\"GSM1388649\"\t\"GSM1388650\"\t\"GSM1388651\"\t\"GSM1388652\"\t\"GSM1388653\"\t\"GSM1388654\"\t\"GSM1388655\"\t\"GSM1388656\"\t\"GSM1388657\"\t\"GSM1388658\"\t\"GSM1388659\"\t\"GSM1388660\"\t\"GSM1388661\"\t\"GSM1388662\"\t\"GSM1388663\"\t\"GSM1388664\"\t\"GSM1388665\"\t\"GSM1388666\"\t\"GSM1388667\"\t\"GSM1388668\"\t\"GSM1388669\"\t\"GSM1388670\"\t\"GSM1388671\"\t\"GSM1388672\"\t\"GSM1388673\"\t\"GSM1388674\"\t\"GSM1388675\"\t\"GSM1388676\"\t\"GSM1388677\"\t\"GSM1388678\"\t\"GSM1388679\"\t\"GSM1388680\"\t\"GSM1388681\"\t\"GSM1388682\"\t\"GSM1388683\"\t\"GSM1388684\"\t\"GSM1388685\"\t\"GSM1388686\"\t\"GSM1388687\"\t\"GSM1388688\"\t\"GSM1388689\"\t\"GSM1388690\"\t\"GSM1388691\"\t\"GSM1388692\"\t\"GSM1388693\"\t\"GSM1388694\"\t\"GSM1388695\"\t\"GSM1388696\"\t\"GSM1388697\"\n", "First data line: \"ILMN_1343291\"\t48768.44379\t53241.08914\t53700.17172\t55892.46776\t54228.3381\t52026.32328\t52399.87621\t54890.715\t48768.44379\t49093.39397\t54228.3381\t50756.8881\t53241.08914\t47655.465\t49938.24138\t52834.26724\t46722.82776\t51677.605\t50161.6269\t51677.605\t52026.32328\t52399.87621\t50161.6269\t52399.87621\t54228.3381\t52834.26724\t49698.99879\t53241.08914\t51075.995\t54228.3381\t53700.17172\t53241.08914\t54890.715\t46954.90379\t53241.08914\t52026.32328\t45682.93948\t49093.39397\t51075.995\t51677.605\t54890.715\t52026.32328\t54890.715\t54228.3381\t54890.715\t53700.17172\t49938.24138\t54890.715\t54228.3381\t55892.46776\t53241.08914\t54228.3381\t55892.46776\t53241.08914\t47437.22879\t46722.82776\t52026.32328\t49353.91362\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "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" ] } ], "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": "5c54a741", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "8a417a61", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:36.868059Z", "iopub.status.busy": "2025-03-25T05:25:36.867943Z", "iopub.status.idle": "2025-03-25T05:25:36.869809Z", "shell.execute_reply": "2025-03-25T05:25:36.869544Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers from the output, I can see they start with \"ILMN_\" which indicates\n", "# these are Illumina probe IDs, not standard human gene symbols.\n", "# Illumina probe identifiers need to be mapped to actual gene symbols for meaningful analysis.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "75ea5753", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "405c32e3", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:36.871115Z", "iopub.status.busy": "2025-03-25T05:25:36.871017Z", "iopub.status.idle": "2025-03-25T05:25:37.308828Z", "shell.execute_reply": "2025-03-25T05:25:37.308504Z" } }, "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 = GSE57795\n", "Line 6: !Series_title = in vivo dexamethasone-induced gene expression in pediatric acute lymphoblastic leukemia patient-derived xenografts\n", "Line 7: !Series_geo_accession = GSE57795\n", "Line 8: !Series_status = Public on May 20 2014\n", "Line 9: !Series_submission_date = May 19 2014\n", "Line 10: !Series_last_update_date = Feb 18 2019\n", "Line 11: !Series_pubmed_id = 25336632\n", "Line 12: !Series_pubmed_id = 26960974\n", "Line 13: !Series_pubmed_id = 27302164\n", "Line 14: !Series_summary = Glucocorticoids are critical components of combination chemotherapy regimens in pediatric acute lymphoblastic leukemia (ALL). The pro-apoptotic BIM protein is an important mediator of glucocorticoid-induced apoptosis in normal and malignant lymphocytes, while the anti-apoptotic BCL2 confers resistance. The signaling pathways regulating BIM and BCL2 expression in glucocorticoid-treated lymphoid cells remain unclear. In this study, pediatric ALL patient-derived xenografts (PDXs) inherently sensitive or resistant to glucocorticoids were exposed to dexamethasone in vivo. In order to understand the basis for differential in vivo glucocorticoid sensitivity of PDXs, microarray analysis of gene expression was carried out on 5 each of dexamethasone-sensitive and resistant PDXs . This provided a global understanding of dexamethasone-induced signaling cascades in ALL cells in vivo, and especialy identified the genes that are involved in transducing the apoptotic signal, upstream of BIM/BCL2 dynamic interactions.\n", "Line 15: !Series_overall_design = ALL xenograft cells were inoculated by tail-vein injection into NOD/SCID mice, and engraftment was monitored weekly. When >70% %huCD45+ engraftment in the peripheral blood was apparent, which occurred 8-10 weeks post-transplantation, mice were treated with either dexamethasone (15 mg/kg) or vehicle control by intra-peritoneal (IP) injection, and culled at 8 hours following the treatment. Cell suspensions of spleens were prepared and mononuclear cells enriched to >97% human by density gradient centrifugation. RNA was extracted using the RNeasy Mini Kit (QIAGEN, Valencia, CA, USA), and RNA samples with integrity number (RIN) > 8.0 were amplified and hybridized onto Illumina HumanWG-6 v3 Expression BeadChips (6 samples/chip). All chips (with associated reagents) were purchased from Illumina, and scanned on the Illumina BeadArray Reader according to the manufacturer’s instructions. Microarray data were analyzed using the online modules in GenePattern.\n", "Line 16: !Series_overall_design = 10 xenografts were derived from patients of 5 dexamethasone-good responder and 5 dexamethasone-poor responder. Each xenograft was innoculated into 5-6 mice, and treated with dexamethasone (15 mg/kg) or vehicle control. In total spleen-harvest xenograft samples from 58 mice were analyzed using microarray.\n", "Line 17: !Series_type = Expression profiling by array\n", "Line 18: !Series_contributor = Vivek,A,Bhadri\n", "Line 19: !Series_contributor = Duohui,,Jing\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': ['ILMN_1825594', 'ILMN_1810803', 'ILMN_1722532', 'ILMN_1884413', 'ILMN_1906034'], 'Species': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Source': ['Unigene', 'RefSeq', 'RefSeq', 'Unigene', 'Unigene'], 'Search_Key': ['ILMN_89282', 'ILMN_35826', 'ILMN_25544', 'ILMN_132331', 'ILMN_105017'], 'Transcript': ['ILMN_89282', 'ILMN_35826', 'ILMN_25544', 'ILMN_132331', 'ILMN_105017'], 'ILMN_Gene': ['HS.388528', 'LOC441782', 'JMJD1A', 'HS.580150', 'HS.540210'], 'Source_Reference_ID': ['Hs.388528', 'XM_497527.2', 'NM_018433.3', 'Hs.580150', 'Hs.540210'], 'RefSeq_ID': [nan, 'XM_497527.2', 'NM_018433.3', nan, nan], 'Unigene_ID': ['Hs.388528', nan, nan, 'Hs.580150', 'Hs.540210'], 'Entrez_Gene_ID': [nan, 441782.0, 55818.0, nan, nan], 'GI': [23525203, 89042416, 46358420, 7376124, 5437312], 'Accession': ['BU678343', 'XM_497527.2', 'NM_018433.3', 'AW629334', 'AI818233'], 'Symbol': [nan, 'LOC441782', 'JMJD1A', nan, nan], 'Protein_Product': [nan, 'XP_497527.2', 'NP_060903.2', nan, nan], 'Array_Address_Id': [1740241, 1850750, 1240504, 4050487, 2190598], 'Probe_Type': ['S', 'S', 'S', 'S', 'S'], 'Probe_Start': [349, 902, 4359, 117, 304], 'SEQUENCE': ['CTCTCTAAAGGGACAACAGAGTGGACAGTCAAGGAACTCCACATATTCAT', 'GGGGTCAAGCCCAGGTGAAATGTGGATTGGAAAAGTGCTTCCCTTGCCCC', 'CCAGGCTGTAAAAGCAAAACCTCGTATCAGCTCTGGAACAATACCTGCAG', 'CCAGACAGGAAGCATCAAGCCCTTCAGGAAAGAATATGCGAGAGTGCTGC', 'TGTGCAGAAAGCTGATGGAAGGGAGAAAGAATGGAAGTGGGTCACACAGC'], 'Chromosome': [nan, nan, '2', nan, nan], 'Probe_Chr_Orientation': [nan, nan, '+', nan, nan], 'Probe_Coordinates': [nan, nan, '86572991-86573040', nan, nan], 'Cytoband': [nan, nan, '2p11.2e', nan, nan], 'Definition': ['UI-CF-EC0-abi-c-12-0-UI.s1 UI-CF-EC0 Homo sapiens cDNA clone UI-CF-EC0-abi-c-12-0-UI 3, mRNA sequence', 'PREDICTED: Homo sapiens similar to spectrin domain with coiled-coils 1 (LOC441782), mRNA.', 'Homo sapiens jumonji domain containing 1A (JMJD1A), mRNA.', 'hi56g05.x1 Soares_NFL_T_GBC_S1 Homo sapiens cDNA clone IMAGE:2976344 3, mRNA sequence', 'wk77d04.x1 NCI_CGAP_Pan1 Homo sapiens cDNA clone IMAGE:2421415 3, mRNA sequence'], 'Ontology_Component': [nan, nan, 'nucleus [goid 5634] [evidence IEA]', nan, nan], 'Ontology_Process': [nan, nan, 'chromatin modification [goid 16568] [evidence IEA]; transcription [goid 6350] [evidence IEA]; regulation of transcription, DNA-dependent [goid 6355] [evidence IEA]', nan, nan], 'Ontology_Function': [nan, nan, 'oxidoreductase activity [goid 16491] [evidence IEA]; oxidoreductase activity, acting on single donors with incorporation of molecular oxygen, incorporation of two atoms of oxygen [goid 16702] [evidence IEA]; zinc ion binding [goid 8270] [evidence IEA]; metal ion binding [goid 46872] [evidence IEA]; iron ion binding [goid 5506] [evidence IEA]', nan, nan], 'Synonyms': [nan, nan, 'JHMD2A; JMJD1; TSGA; KIAA0742; DKFZp686A24246; DKFZp686P07111', nan, nan], 'GB_ACC': ['BU678343', 'XM_497527.2', 'NM_018433.3', 'AW629334', 'AI818233']}\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": "ba9565f8", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "a27b34cc", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:37.310503Z", "iopub.status.busy": "2025-03-25T05:25:37.310386Z", "iopub.status.idle": "2025-03-25T05:25:38.117944Z", "shell.execute_reply": "2025-03-25T05:25:38.117569Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping preview:\n", "{'ID': ['ILMN_1810803', 'ILMN_1722532', 'ILMN_1708805', 'ILMN_1672526', 'ILMN_2185604'], 'Gene': ['LOC441782', 'JMJD1A', 'NCOA3', 'LOC389834', 'C17orf77']}\n", "\n", "Gene expression data preview (after mapping):\n", "Number of genes: 18839\n", "Number of samples: 58\n", "{'GSM1388640': [311.89239829999997, 377.37908554, 106.2299664, 84.17138431, 190.71983448], 'GSM1388641': [236.99664810000002, 407.73767069, 125.0610366, 93.79152293, 199.56930326], 'GSM1388642': [262.3563576, 394.99722263, 93.94069103, 92.48375793, 189.34640176], 'GSM1388643': [249.5940265, 426.66989287, 93.56024914, 78.9854519, 187.44212862], 'GSM1388644': [240.15248880000001, 400.73641369, 99.17725517, 79.1839869, 185.13202569999999], 'GSM1388645': [287.6848966, 400.51086934, 125.021119, 90.25136138, 188.38333707], 'GSM1388646': [255.6183091, 390.06359412, 177.2741879, 88.4593881, 180.21332673], 'GSM1388647': [253.47054099999997, 411.86424399, 219.9371879, 96.84532328, 180.10935068999999], 'GSM1388648': [255.88073, 417.97329269, 112.4403769, 97.21653397, 179.67541172], 'GSM1388649': [235.8862302, 397.44958105, 141.9448276, 85.20911362, 176.08695207], 'GSM1388650': [243.13535810000002, 419.02724461, 160.4025172, 92.20547552, 185.56381776], 'GSM1388651': [250.78432622999998, 398.78844254, 223.2995845, 95.96083612, 187.60761638000002], 'GSM1388652': [276.761346, 420.99174001999995, 191.8550172, 97.64191155, 204.0822048], 'GSM1388653': [233.04505020000002, 380.29159522, 269.3575483, 95.46074672, 188.5100526], 'GSM1388654': [248.0051877, 430.1954449, 235.7620224, 91.7123469, 202.85802619999998], 'GSM1388655': [239.59689780000002, 389.40106294, 139.5068603, 91.72247569, 177.71941035], 'GSM1388656': [232.6480864, 393.38216682, 278.4701655, 94.47245534, 177.66209535000002], 'GSM1388657': [251.74089930000002, 388.66114981, 117.1249867, 86.93285914, 183.88571294000002], 'GSM1388658': [262.8637092, 392.26063189, 104.021369, 92.54764552, 193.46704518], 'GSM1388659': [245.4049029, 393.66773138, 150.603369, 97.53813569, 196.06768172], 'GSM1388660': [229.5400037, 377.33124827, 103.1044452, 97.57522, 182.10579488], 'GSM1388661': [243.78037940000002, 422.60692366, 106.6583064, 99.06108138, 189.5384231], 'GSM1388662': [242.3762042, 441.83197478, 103.8689455, 98.20111138, 195.34147035], 'GSM1388663': [276.7770798, 415.72039806, 98.86473017, 93.06047638, 194.66741587], 'GSM1388664': [266.7100355, 362.01885551, 86.74051431, 93.85187552, 199.04389915000002], 'GSM1388665': [234.1664959, 407.24289982, 95.16520983, 86.69222569, 186.57301345000002], 'GSM1388666': [242.5198231, 370.93653653, 104.5550047, 95.45706069, 203.33306874], 'GSM1388667': [238.47294010000002, 392.55384273000004, 88.93829259, 91.04370483, 203.77056053], 'GSM1388668': [259.25642089999997, 402.89597652, 89.07460828, 100.9364683, 192.62215604], 'GSM1388669': [262.1870371, 361.28786792, 119.3541998, 87.22678345, 187.5120069], 'GSM1388670': [262.04044120000003, 419.30288515, 133.0653845, 105.8983612, 187.72271397], 'GSM1388671': [239.3900629, 404.73533449, 140.9423879, 95.88487879, 186.97318848], 'GSM1388672': [243.29377849999997, 380.87336467, 92.01499483, 109.4082767, 203.19469761], 'GSM1388673': [245.92333430000002, 428.3529388, 102.1260716, 92.19837414, 203.46133088], 'GSM1388674': [255.0970091, 412.61059149, 106.007019, 94.30207017, 177.82578948], 'GSM1388675': [256.5554838, 412.28936998, 110.3730871, 107.1806169, 203.31537429], 'GSM1388676': [263.5567386, 373.96652177, 87.49394414, 104.3388828, 185.50778362], 'GSM1388677': [231.1259324, 416.75077061, 111.3225581, 91.46732897, 192.40818069], 'GSM1388678': [236.3178689, 391.71484413, 90.20007534, 91.67135466, 183.67294257999998], 'GSM1388679': [252.49650309999998, 402.8297057, 91.97257655, 91.29736483, 182.51062879], 'GSM1388680': [243.06759290000002, 416.83094659, 106.6460948, 79.84878138, 180.85823776], 'GSM1388681': [256.33778297, 398.8676014, 101.4699078, 92.23603431, 206.51867470000002], 'GSM1388682': [253.6574898, 400.66559056, 98.91008879, 88.58843362, 205.65320296], 'GSM1388683': [246.2681518, 371.92088034, 108.4933181, 86.47565448, 178.35470844], 'GSM1388684': [234.2054331, 413.46101404, 93.20369328, 108.3425598, 187.49987155], 'GSM1388685': [230.4344174, 416.77383742, 92.84612897, 83.10589983, 184.42486913], 'GSM1388686': [231.8591239, 403.85992649, 101.1930112, 86.84292138, 197.21174536], 'GSM1388687': [243.6022504, 398.72074848, 118.455655, 81.13429483, 203.0657563], 'GSM1388688': [241.5534358, 411.76934872, 126.2632257, 94.50118759, 182.73030138000001], 'GSM1388689': [235.74722119999998, 410.52058805, 136.4588207, 95.01763207, 193.87107328000002], 'GSM1388690': [266.9226386, 417.63816264, 117.7667522, 87.27116155, 186.71379138], 'GSM1388691': [257.3650525, 408.85993275, 108.5803393, 98.74318293, 174.53585637999998], 'GSM1388692': [239.89805760000002, 383.63804744000004, 113.6130015, 91.76241328, 173.5744181], 'GSM1388693': [288.5315517, 407.66735467, 82.25887931, 107.2676181, 183.00926845], 'GSM1388694': [252.2978977, 374.54062876, 116.7801005, 92.39537621, 202.3801851], 'GSM1388695': [282.5472849, 395.25206641, 111.4922766, 90.94682207, 191.59154482999998], 'GSM1388696': [261.4605019, 414.57319583000003, 123.1690505, 95.377065, 186.28742086], 'GSM1388697': [265.2751247, 389.75225231, 121.1838757, 89.85397845, 184.18866793]}\n", "\n", "Final gene expression data preview (after normalization):\n", "Final number of genes: 17552\n", "{'GSM1388640': [311.89239829999997, 106.2299664, 84.17138431, 190.71983448, 97.15441466], 'GSM1388641': [236.99664810000002, 125.0610366, 93.79152293, 199.56930326, 92.84003276], 'GSM1388642': [262.3563576, 93.94069103, 92.48375793, 189.34640176, 97.36621483], 'GSM1388643': [249.5940265, 93.56024914, 78.9854519, 187.44212862, 93.88298914], 'GSM1388644': [240.15248880000001, 99.17725517, 79.1839869, 185.13202569999999, 99.71172845], 'GSM1388645': [287.6848966, 125.021119, 90.25136138, 188.38333707, 101.7327386], 'GSM1388646': [255.6183091, 177.2741879, 88.4593881, 180.21332673, 99.91673534], 'GSM1388647': [253.47054099999997, 219.9371879, 96.84532328, 180.10935068999999, 94.20889621], 'GSM1388648': [255.88073, 112.4403769, 97.21653397, 179.67541172, 90.40458534], 'GSM1388649': [235.8862302, 141.9448276, 85.20911362, 176.08695207, 85.34414], 'GSM1388650': [243.13535810000002, 160.4025172, 92.20547552, 185.56381776, 86.19671345], 'GSM1388651': [250.78432622999998, 223.2995845, 95.96083612, 187.60761638000002, 83.39309121], 'GSM1388652': [276.761346, 191.8550172, 97.64191155, 204.0822048, 95.65882052], 'GSM1388653': [233.04505020000002, 269.3575483, 95.46074672, 188.5100526, 92.57518931], 'GSM1388654': [248.0051877, 235.7620224, 91.7123469, 202.85802619999998, 91.13958328], 'GSM1388655': [239.59689780000002, 139.5068603, 91.72247569, 177.71941035, 94.73692845], 'GSM1388656': [232.6480864, 278.4701655, 94.47245534, 177.66209535000002, 83.35993655], 'GSM1388657': [251.74089930000002, 117.1249867, 86.93285914, 183.88571294000002, 92.08170086], 'GSM1388658': [262.8637092, 104.021369, 92.54764552, 193.46704518, 94.31827552], 'GSM1388659': [245.4049029, 150.603369, 97.53813569, 196.06768172, 91.87740966], 'GSM1388660': [229.5400037, 103.1044452, 97.57522, 182.10579488, 101.8237781], 'GSM1388661': [243.78037940000002, 106.6583064, 99.06108138, 189.5384231, 116.3932343], 'GSM1388662': [242.3762042, 103.8689455, 98.20111138, 195.34147035, 95.54668638], 'GSM1388663': [276.7770798, 98.86473017, 93.06047638, 194.66741587, 109.8832593], 'GSM1388664': [266.7100355, 86.74051431, 93.85187552, 199.04389915000002, 99.22530655], 'GSM1388665': [234.1664959, 95.16520983, 86.69222569, 186.57301345000002, 106.242406], 'GSM1388666': [242.5198231, 104.5550047, 95.45706069, 203.33306874, 121.965441], 'GSM1388667': [238.47294010000002, 88.93829259, 91.04370483, 203.77056053, 116.4772483], 'GSM1388668': [259.25642089999997, 89.07460828, 100.9364683, 192.62215604, 131.1796655], 'GSM1388669': [262.1870371, 119.3541998, 87.22678345, 187.5120069, 101.5187409], 'GSM1388670': [262.04044120000003, 133.0653845, 105.8983612, 187.72271397, 105.1625391], 'GSM1388671': [239.3900629, 140.9423879, 95.88487879, 186.97318848, 98.01270741], 'GSM1388672': [243.29377849999997, 92.01499483, 109.4082767, 203.19469761, 78.80817534], 'GSM1388673': [245.92333430000002, 102.1260716, 92.19837414, 203.46133088, 96.35890241], 'GSM1388674': [255.0970091, 106.007019, 94.30207017, 177.82578948, 85.57456052], 'GSM1388675': [256.5554838, 110.3730871, 107.1806169, 203.31537429, 82.18620207], 'GSM1388676': [263.5567386, 87.49394414, 104.3388828, 185.50778362, 102.2706295], 'GSM1388677': [231.1259324, 111.3225581, 91.46732897, 192.40818069, 97.59774759], 'GSM1388678': [236.3178689, 90.20007534, 91.67135466, 183.67294257999998, 93.79540948], 'GSM1388679': [252.49650309999998, 91.97257655, 91.29736483, 182.51062879, 95.50662448], 'GSM1388680': [243.06759290000002, 106.6460948, 79.84878138, 180.85823776, 94.59703483], 'GSM1388681': [256.33778297, 101.4699078, 92.23603431, 206.51867470000002, 125.1616071], 'GSM1388682': [253.6574898, 98.91008879, 88.58843362, 205.65320296, 119.0051212], 'GSM1388683': [246.2681518, 108.4933181, 86.47565448, 178.35470844, 117.0418712], 'GSM1388684': [234.2054331, 93.20369328, 108.3425598, 187.49987155, 175.2975483], 'GSM1388685': [230.4344174, 92.84612897, 83.10589983, 184.42486913, 165.1950948], 'GSM1388686': [231.8591239, 101.1930112, 86.84292138, 197.21174536, 156.2772914], 'GSM1388687': [243.6022504, 118.455655, 81.13429483, 203.0657563, 89.11291], 'GSM1388688': [241.5534358, 126.2632257, 94.50118759, 182.73030138000001, 96.43678362], 'GSM1388689': [235.74722119999998, 136.4588207, 95.01763207, 193.87107328000002, 88.40088517], 'GSM1388690': [266.9226386, 117.7667522, 87.27116155, 186.71379138, 94.28155914], 'GSM1388691': [257.3650525, 108.5803393, 98.74318293, 174.53585637999998, 100.5802457], 'GSM1388692': [239.89805760000002, 113.6130015, 91.76241328, 173.5744181, 93.56802224], 'GSM1388693': [288.5315517, 82.25887931, 107.2676181, 183.00926845, 113.4417197], 'GSM1388694': [252.2978977, 116.7801005, 92.39537621, 202.3801851, 100.7378586], 'GSM1388695': [282.5472849, 111.4922766, 90.94682207, 191.59154482999998, 97.07578155], 'GSM1388696': [261.4605019, 123.1690505, 95.377065, 186.28742086, 97.35447655], 'GSM1388697': [265.2751247, 121.1838757, 89.85397845, 184.18866793, 98.36252655]}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE57795.csv\n" ] } ], "source": [ "# Identify the columns for gene identifiers and gene symbols in the gene annotation data\n", "# From the preview, we can see:\n", "# - 'ID' column contains the probe IDs that match the expression data (ILMN_*)\n", "# - 'Symbol' column contains the gene symbols we need to map to\n", "\n", "# 1. Get the gene mapping dataframe using the gene_annotation DataFrame\n", "gene_mapping = gene_annotation[['ID', 'Symbol']].copy()\n", "gene_mapping = gene_mapping.dropna()\n", "gene_mapping = gene_mapping.rename(columns={'ID': 'ID', 'Symbol': 'Gene'}).astype({'ID': 'str'})\n", "\n", "# Print the first few rows of the mapping to verify\n", "print(\"Gene mapping preview:\")\n", "print(preview_df(gene_mapping))\n", "\n", "# 2. Apply the gene mapping to convert probe-level measurements to gene expression\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# 3. Preview the gene expression data after mapping\n", "print(\"\\nGene expression data preview (after mapping):\")\n", "print(f\"Number of genes: {gene_data.shape[0]}\")\n", "print(f\"Number of samples: {gene_data.shape[1]}\")\n", "print(preview_df(gene_data))\n", "\n", "# 4. Normalize gene symbols to handle synonyms\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "\n", "# 5. Preview the final gene expression data\n", "print(\"\\nFinal gene expression data preview (after normalization):\")\n", "print(f\"Final number of genes: {gene_data.shape[0]}\")\n", "print(preview_df(gene_data))\n", "\n", "# 6. Create output directory if it doesn't exist\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "\n", "# 7. Save the gene expression data to a CSV file\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": "8261956b", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "1a8d863e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:25:38.119188Z", "iopub.status.busy": "2025-03-25T05:25:38.119070Z", "iopub.status.idle": "2025-03-25T05:25:47.411110Z", "shell.execute_reply": "2025-03-25T05:25:47.410432Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (17552, 58)\n", "Sample gene symbols after normalization: ['A1BG', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS', 'AACSP1']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data saved to ../../output/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE57795.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: (58, 17553)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Glucocorticoid_Sensitivity A1BG A2M A2ML1 \\\n", "GSM1388640 1.0 311.892398 106.229966 84.171384 \n", "GSM1388641 0.0 236.996648 125.061037 93.791523 \n", "GSM1388642 1.0 262.356358 93.940691 92.483758 \n", "GSM1388643 0.0 249.594027 93.560249 78.985452 \n", "GSM1388644 1.0 240.152489 99.177255 79.183987 \n", "\n", " A3GALT2 \n", "GSM1388640 190.719834 \n", "GSM1388641 199.569303 \n", "GSM1388642 189.346402 \n", "GSM1388643 187.442129 \n", "GSM1388644 185.132026 \n", "\n", "Missing values before handling:\n", " Trait (Glucocorticoid_Sensitivity) missing: 0 out of 58\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: (58, 17553)\n", "For the feature 'Glucocorticoid_Sensitivity', the least common label is '1.0' with 29 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/GSE57795.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 }