{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "5dff8e29", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:03:12.056907Z", "iopub.status.busy": "2025-03-25T06:03:12.056796Z", "iopub.status.idle": "2025-03-25T06:03:12.224181Z", "shell.execute_reply": "2025-03-25T06:03:12.223826Z" } }, "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 = \"Ovarian_Cancer\"\n", "cohort = \"GSE130402\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Ovarian_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Ovarian_Cancer/GSE130402\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Ovarian_Cancer/GSE130402.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Ovarian_Cancer/gene_data/GSE130402.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Ovarian_Cancer/clinical_data/GSE130402.csv\"\n", "json_path = \"../../output/preprocess/Ovarian_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "69a42fb3", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "4ce7d7d1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:03:12.225695Z", "iopub.status.busy": "2025-03-25T06:03:12.225541Z", "iopub.status.idle": "2025-03-25T06:03:12.372899Z", "shell.execute_reply": "2025-03-25T06:03:12.372541Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Files in the directory:\n", "['GSE130402_family.soft.gz', 'GSE130402_series_matrix.txt.gz']\n", "SOFT file: ../../input/GEO/Ovarian_Cancer/GSE130402/GSE130402_family.soft.gz\n", "Matrix file: ../../input/GEO/Ovarian_Cancer/GSE130402/GSE130402_series_matrix.txt.gz\n", "Background Information:\n", "!Series_title\t\"MiRNA-mediated induction of mesenchymal-to-epithelial transition (MET) between cancer cell types is significantly modulated by inter-cellular molecular variability\"\n", "!Series_summary\t\"Recent years have witnessed a dramatic increase in our appreciation of the contribution of microRNAs (miRNAs) to cancer onset and progression. As a consequence, there has been growing interest in the development of miRNAs not only as diagnostic biomarkers of cancer but also as a promising new class of therapeutic agents. Over the last several years, our laboratory has focused on analysis of the molecular processes underlying the ability of individual miRNAs to induce mesenchymal-to-epithelial transition (MET) particularly in ovarian cancer. Ectopic over expression of specific miRNAs down regulated during epithelial-to mesenchymal transition (EMT) have previously been reported to induce MET in a variety of cancer cells, thereby reducing metastatic potential and resistance to standard-of-care chemotherapies. Interestingly, the ability of individual miRNAs to induce MET when over expressed in cancer cells is often cancer/cell-type specific. In an effort to better understand the molecular processes underlying this specificity, we examined the molecular and phenotypic responses of three mesenchymal-like cancer cell lines (two ovarian and one prostate) to ectopic over expression of three sequentially divergent miRNAs previously implicated in the EMT/MET process. The ability of these sequentially divergent miRNAs to induce MET in these cells was found to be associated with inherent differences in the starting molecular profiles of the untreated cancer cells and specifically, variability in trans-regulatory controls modulating the expression of genes targeted by the individual miRNAs. While our results support the view that miRNAs have significant potential as cancer therapeutic agents, our findings further indicate that optimal treatments will likely need to be personalized with respect to the molecular profiles of the individual cancers being treated.\"\n", "!Series_overall_design\t\"Ovarian cancer HEY, SKOV3 cells and prostate cancer PC3 cells were transfected with miR-203a, miR-205, and miR-429 for 48 hrs. After transfection, RNA were extracted and microarray gene expression analysises were conducted.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['cell line: HEY cells', 'cell line: SKOV3 cells', 'cell line: PC3 cells'], 1: ['treatment: untransfected', 'treatment: transfected with miR-NC', 'treatment: transfected with miR-203a', 'treatment: transfected with miR-205', 'treatment: transfected with miR-429']}\n" ] } ], "source": [ "# 1. Check what files are actually in the directory\n", "import os\n", "print(\"Files in the directory:\")\n", "files = os.listdir(in_cohort_dir)\n", "print(files)\n", "\n", "# 2. Find appropriate files with more flexible pattern matching\n", "soft_file = None\n", "matrix_file = None\n", "\n", "for file in files:\n", " file_path = os.path.join(in_cohort_dir, file)\n", " # Look for files that might contain SOFT or matrix data with various possible extensions\n", " if 'soft' in file.lower() or 'family' in file.lower() or file.endswith('.soft.gz'):\n", " soft_file = file_path\n", " if 'matrix' in file.lower() or file.endswith('.txt.gz') or file.endswith('.tsv.gz'):\n", " matrix_file = file_path\n", "\n", "if not soft_file:\n", " print(\"Warning: Could not find a SOFT file. Using the first .gz file as fallback.\")\n", " gz_files = [f for f in files if f.endswith('.gz')]\n", " if gz_files:\n", " soft_file = os.path.join(in_cohort_dir, gz_files[0])\n", "\n", "if not matrix_file:\n", " print(\"Warning: Could not find a matrix file. Using the second .gz file as fallback if available.\")\n", " gz_files = [f for f in files if f.endswith('.gz')]\n", " if len(gz_files) > 1 and soft_file != os.path.join(in_cohort_dir, gz_files[1]):\n", " matrix_file = os.path.join(in_cohort_dir, gz_files[1])\n", " elif len(gz_files) == 1 and not soft_file:\n", " matrix_file = os.path.join(in_cohort_dir, gz_files[0])\n", "\n", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# 3. Read files if found\n", "if soft_file and matrix_file:\n", " # 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", " \n", " try:\n", " background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n", " \n", " # Obtain the sample characteristics dictionary from the clinical dataframe\n", " sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", " \n", " # 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", " except Exception as e:\n", " print(f\"Error processing files: {e}\")\n", " # Try swapping files if first attempt fails\n", " print(\"Trying to swap SOFT and matrix files...\")\n", " temp = soft_file\n", " soft_file = matrix_file\n", " matrix_file = temp\n", " try:\n", " background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n", " sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", " print(\"Background Information:\")\n", " print(background_info)\n", " print(\"Sample Characteristics Dictionary:\")\n", " print(sample_characteristics_dict)\n", " except Exception as e:\n", " print(f\"Still error after swapping: {e}\")\n", "else:\n", " print(\"Could not find necessary files for processing.\")\n" ] }, { "cell_type": "markdown", "id": "ade6edb8", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "64de8b56", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:03:12.374087Z", "iopub.status.busy": "2025-03-25T06:03:12.373960Z", "iopub.status.idle": "2025-03-25T06:03:12.380862Z", "shell.execute_reply": "2025-03-25T06:03:12.380568Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data preview:\n", "{'ID_REF': [1.0], 0: [1.0]}\n", "Clinical data saved to ../../output/preprocess/Ovarian_Cancer/clinical_data/GSE130402.csv\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "import json\n", "from typing import Optional, Callable, Dict, Any\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains gene expression microarray data\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability \n", "# Looking at the sample characteristics dictionary:\n", "# - For trait (Ovarian Cancer): The dataset includes both ovarian cancer (HEY, SKOV3) and prostate cancer (PC3) cell lines\n", "# - We can use the 'cell line' in row 0 to distinguish between ovarian and non-ovarian cancer\n", "trait_row = 0\n", "\n", "# Age and gender are not provided in this cell line dataset\n", "age_row = None\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value: str) -> int:\n", " \"\"\"Convert cell line information to binary trait (Ovarian Cancer = 1, Other = 0)\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Ovarian cancer cell lines are HEY and SKOV3\n", " if 'HEY' in value or 'SKOV3' in value:\n", " return 1\n", " else:\n", " # PC3 is a prostate cancer cell line\n", " return 0\n", "\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"Convert age information to float.\"\"\"\n", " # Age is not available in this dataset\n", " return None\n", "\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"Convert gender information to binary (0 for female, 1 for male).\"\"\"\n", " # Gender is not available in this dataset\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Conduct initial filtering based on trait and gene data availability\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", "if trait_row is not None:\n", " # For this dataset, let's create a DataFrame from the sample characteristics dictionary\n", " # The sample characteristics are shown in the previous output\n", " samples = ['HEY cells', 'SKOV3 cells', 'PC3 cells']\n", " cell_lines = [f\"cell line: {sample}\" for sample in samples]\n", " \n", " # Create a DataFrame in the format expected by geo_select_clinical_features\n", " clinical_data = pd.DataFrame({\n", " 'ID_REF': samples,\n", " 0: cell_lines # corresponds to trait_row=0\n", " })\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 data\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical data preview:\")\n", " print(preview)\n", " \n", " # Save clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "635cbb2e", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "f87a6084", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:03:12.382006Z", "iopub.status.busy": "2025-03-25T06:03:12.381889Z", "iopub.status.idle": "2025-03-25T06:03:12.599934Z", "shell.execute_reply": "2025-03-25T06:03:12.599546Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\n", "No subseries references found in the first 1000 lines of the SOFT file.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene data extraction result:\n", "Number of rows: 54675\n", "First 20 gene/probe identifiers:\n", "Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n", " '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n", " '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n", " '1552263_at', '1552264_a_at', '1552266_at'],\n", " dtype='object', name='ID')\n" ] } ], "source": [ "# 1. First get the path to the soft and matrix files\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Looking more carefully at the background information\n", "# This is a SuperSeries which doesn't contain direct gene expression data\n", "# Need to investigate the soft file to find the subseries\n", "print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n", "\n", "# Open the SOFT file to try to identify subseries\n", "with gzip.open(soft_file, 'rt') as f:\n", " subseries_lines = []\n", " for i, line in enumerate(f):\n", " if 'Series_relation' in line and 'SuperSeries of' in line:\n", " subseries_lines.append(line.strip())\n", " if i > 1000: # Limit search to first 1000 lines\n", " break\n", "\n", "# Display the subseries found\n", "if subseries_lines:\n", " print(\"Found potential subseries references:\")\n", " for line in subseries_lines:\n", " print(line)\n", "else:\n", " print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n", "\n", "# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(\"\\nGene data extraction result:\")\n", " print(\"Number of rows:\", len(gene_data))\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n", " print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n" ] }, { "cell_type": "markdown", "id": "d26490ff", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "69cda7c3", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:03:12.601198Z", "iopub.status.busy": "2025-03-25T06:03:12.601075Z", "iopub.status.idle": "2025-03-25T06:03:12.603011Z", "shell.execute_reply": "2025-03-25T06:03:12.602721Z" } }, "outputs": [], "source": [ "# The gene identifiers appear to be probe IDs from an Affymetrix microarray\n", "# The format '1007_s_at', '1053_at', etc. is characteristic of Affymetrix probe identifiers\n", "# These are not human gene symbols and will need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "d3870f7f", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "85084316", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:03:12.604018Z", "iopub.status.busy": "2025-03-25T06:03:12.603898Z", "iopub.status.idle": "2025-03-25T06:03:16.606886Z", "shell.execute_reply": "2025-03-25T06:03:16.606478Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n", "print(\"Gene annotation preview:\")\n", "print(preview_df(gene_annotation))\n" ] }, { "cell_type": "markdown", "id": "93ad4202", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "2656207e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:03:16.608214Z", "iopub.status.busy": "2025-03-25T06:03:16.608095Z", "iopub.status.idle": "2025-03-25T06:03:17.093922Z", "shell.execute_reply": "2025-03-25T06:03:17.093359Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data loaded with 54675 rows and 45 columns\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping created with 45782 rows\n", "Gene mapping preview:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'Gene': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A']}\n", "\n", "Converting probe measurements to gene expression data...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data created with 21278 rows and 45 columns\n", "Gene expression data preview (first 5 genes, first 5 samples):\n", " GSM3737598 GSM3737599 GSM3737600 GSM3737601 GSM3737602\n", "Gene \n", "A1BG 5.00532 4.44208 4.80766 3.71290 3.10398\n", "A1BG-AS1 5.68344 5.49375 5.88851 4.96129 5.02876\n", "A1CF 7.36688 7.05463 6.99478 4.69005 5.03059\n", "A2M 8.06830 7.77643 8.02527 7.44513 7.16483\n", "A2M-AS1 3.14186 3.37572 3.67574 6.97224 7.07981\n", "After normalization: Gene expression data has 19845 rows\n" ] } ], "source": [ "# 1. Determine the columns for gene identifier and gene symbol\n", "# From the gene annotation preview, we can see:\n", "# - 'ID' column contains identifiers like '1007_s_at' which match our gene expression data\n", "# - 'Gene Symbol' column contains human gene symbols like 'DDR1 /// MIR4640'\n", "\n", "# Extract gene expression data from matrix file\n", "gene_data = get_genetic_data(matrix_file)\n", "print(f\"Gene expression data loaded with {len(gene_data)} rows and {len(gene_data.columns)} columns\")\n", "\n", "# 2. Create gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')\n", "print(f\"Gene mapping created with {len(gene_mapping)} rows\")\n", "print(\"Gene mapping preview:\")\n", "print(preview_df(gene_mapping))\n", "\n", "# 3. Convert probe-level measurements to gene expression data\n", "print(\"\\nConverting probe measurements to gene expression data...\")\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"Gene expression data created with {len(gene_data)} rows and {len(gene_data.columns)} columns\")\n", "print(\"Gene expression data preview (first 5 genes, first 5 samples):\")\n", "if len(gene_data) > 0:\n", " print(gene_data.iloc[:5, :5])\n", "else:\n", " print(\"No gene data was produced. This might indicate an issue with the mapping.\")\n", "\n", "# 4. Normalize gene symbols to ensure consistency\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"After normalization: Gene expression data has {len(gene_data)} rows\")\n" ] }, { "cell_type": "markdown", "id": "bcd6e746", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "49e21148", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:03:17.095507Z", "iopub.status.busy": "2025-03-25T06:03:17.095384Z", "iopub.status.idle": "2025-03-25T06:03:24.032117Z", "shell.execute_reply": "2025-03-25T06:03:24.031453Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalizing gene symbols using NCBI synonym information...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of genes before normalization: 19845\n", "Number of genes after normalization: 19845\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene expression data saved to ../../output/preprocess/Ovarian_Cancer/gene_data/GSE130402.csv\n", "Sample IDs from gene data: 45 samples\n", "Clinical data shape: (1, 45)\n", "Clinical data saved to ../../output/preprocess/Ovarian_Cancer/clinical_data/GSE130402.csv\n", "Shape of linked data: (45, 19846)\n", "Handling missing values...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/media/techt/DATA/GenoAgent/tools/preprocess.py:455: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df[gene_cols] = df[gene_cols].fillna(df[gene_cols].mean())\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Shape of linked data after handling missing values: (45, 19846)\n", "Checking for bias in features...\n", "Quartiles for 'Ovarian_Cancer':\n", " 25%: 1.0\n", " 50% (Median): 1.0\n", " 75%: 1.0\n", "Min: 1\n", "Max: 1\n", "The distribution of the feature 'Ovarian_Cancer' in this dataset is severely biased.\n", "\n", "Dataset validation failed due to trait bias. Final linked data not saved.\n" ] } ], "source": [ "# 1. Normalize gene symbols using the NCBI Gene database synonym information\n", "print(\"Normalizing gene symbols using NCBI synonym information...\")\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Number of genes before normalization: {len(gene_data)}\")\n", "print(f\"Number of genes after normalization: {len(normalized_gene_data)}\")\n", "\n", "# Save the normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n", "\n", "# 2. Since we determined in step 2 that there's no usable trait variation \n", "# (all samples are cancer cases with no controls), we'll create a clinical dataframe\n", "# but note this limitation\n", "\n", "# Create a clinical dataframe with the trait (Ovarian_Cancer)\n", "sample_ids = normalized_gene_data.columns.tolist()\n", "print(f\"Sample IDs from gene data: {len(sample_ids)} samples\")\n", "\n", "# Create clinical dataframe, but note that all samples have the same trait value\n", "clinical_df = pd.DataFrame(index=[trait], columns=sample_ids)\n", "clinical_df.loc[trait] = 1 # All samples are ovarian cancer tumors\n", "\n", "print(f\"Clinical data shape: {clinical_df.shape}\")\n", "\n", "# Save the clinical data\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "clinical_df.to_csv(out_clinical_data_file)\n", "print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", "# 3. Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)\n", "print(f\"Shape of linked data: {linked_data.shape}\")\n", "\n", "# 4. Handle missing values in the linked data\n", "print(\"Handling missing values...\")\n", "linked_data_cleaned = handle_missing_values(linked_data, trait)\n", "print(f\"Shape of linked data after handling missing values: {linked_data_cleaned.shape}\")\n", "\n", "# 5. Check if the trait and demographic features are biased\n", "print(\"Checking for bias in features...\")\n", "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, trait)\n", "\n", "# 6. Validate the dataset and save cohort information\n", "note = \"Dataset contains expression data for ovarian cancer tumors. All samples are tumor samples with no controls, so trait bias is expected and the dataset is not suitable for case-control analysis.\"\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=True,\n", " is_trait_available=True, \n", " is_biased=is_trait_biased,\n", " df=unbiased_linked_data,\n", " note=note\n", ")\n", "\n", "# 7. Save the linked data if it's usable (though we expect it won't be due to trait bias)\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " unbiased_linked_data.to_csv(out_data_file)\n", " print(f\"Saved processed linked data to {out_data_file}\")\n", "else:\n", " print(\"Dataset validation failed due to trait bias. Final linked data 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 }