{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "52a47238", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:08.713416Z", "iopub.status.busy": "2025-03-25T07:02:08.713155Z", "iopub.status.idle": "2025-03-25T07:02:08.877251Z", "shell.execute_reply": "2025-03-25T07:02:08.876916Z" } }, "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 = \"Breast_Cancer\"\n", "cohort = \"GSE236725\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Breast_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Breast_Cancer/GSE236725\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Breast_Cancer/GSE236725.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Breast_Cancer/gene_data/GSE236725.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Breast_Cancer/clinical_data/GSE236725.csv\"\n", "json_path = \"../../output/preprocess/Breast_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "349ee99c", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "53a42ffc", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:08.878614Z", "iopub.status.busy": "2025-03-25T07:02:08.878476Z", "iopub.status.idle": "2025-03-25T07:02:09.098266Z", "shell.execute_reply": "2025-03-25T07:02:09.097885Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"The 31 pairs of FF and FFPE data for the development of 72GC for FFPE in 2014\"\n", "!Series_summary\t\"The study aimed to develop the 72GC (the 72-gene classifier) for recurrence-risk prediction for patients with estrogen receptor positive and node-negative breast cancer. 72-GC could differentiate the high-risk from the low-risk patients with a high statistical significance, and is considered to be applicable to formalin-fixed, paraffin embedded (FFPE) tumor tissues because the results of 72-GC on FF (fresh-frozen) tissues and FFPE tissues showed a high concordance. J06 31 pairs of FF (fresh‑frozen) and FFPE data are included in the concordance analysis of 72GC high/low results between FF and FFPE (Table 3). A long time passed, and now it is unclear how these 31 cases were distributed among the analysis. *Note: This old data has been updated multiple times by the other members. Then, there are some differences from the original paper and unclear points still remain. Therefore, do not use it for formal analysis aimed at public insurance coverage etc. This is for research purposes only. Please cite this paper when writing a new paper. PMID: 24461457 DOI: 10.1016/j.clbc.2013.11.006\"\n", "!Series_overall_design\t\"RNA was extracted from 31 pairs of FF (fresh frozen) and FFPE (Formalin-fixed paraffin-embedded) tumor samples obtained from radical breast cancer surgery and hybridized on Affymetrix microarrays.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['assay: FF', 'assay: FFPE'], 1: ['distant.recurrence: Yes', 'distant.recurrence: No'], 2: ['distant.recurrence.free.survival.year: 3.783', 'distant.recurrence.free.survival.year: 1.261', 'distant.recurrence.free.survival.year: 1.119', 'distant.recurrence.free.survival.year: 1', 'distant.recurrence.free.survival.year: 8.5', 'distant.recurrence.free.survival.year: 8', 'distant.recurrence.free.survival.year: 7.5', 'distant.recurrence.free.survival.year: 7', 'distant.recurrence.free.survival.year: 6.5', 'distant.recurrence.free.survival.year: 2.75', 'distant.recurrence.free.survival.year: 3.666666667', 'distant.recurrence.free.survival.year: 4', 'distant.recurrence.free.survival.year: 2'], 3: ['pair: pair 001', 'pair: pair 002', 'pair: pair 003', 'pair: pair 004', 'pair: pair 005', 'pair: pair 006', 'pair: pair 007', 'pair: pair 008', 'pair: pair 009', 'pair: pair 010', 'pair: pair 011', 'pair: pair 012', 'pair: pair 013', 'pair: pair 014', 'pair: pair 015', 'pair: pair 016', 'pair: pair 017', 'pair: pair 018', 'pair: pair 019', 'pair: pair 020', 'pair: pair 021', 'pair: pair 022', 'pair: pair 023', 'pair: pair 024', 'pair: pair 025', 'pair: pair 026', 'pair: pair 027', 'pair: pair 028', 'pair: pair 029', 'pair: pair 030'], 4: ['tissue: FF tumor', 'tissue: FFPE tumor'], 5: ['disease state: breast cancer']}\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": "a50a8203", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "3ce4e874", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:09.099647Z", "iopub.status.busy": "2025-03-25T07:02:09.099540Z", "iopub.status.idle": "2025-03-25T07:02:09.121693Z", "shell.execute_reply": "2025-03-25T07:02:09.121363Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Available files in ../../input/GEO/Breast_Cancer/GSE236725: ['GSE236725_family.soft.gz', 'GSE236725_series_matrix.txt.gz']\n", "No clinical data file found. Creating DataFrame from sample characteristics dictionary.\n", "Preview of extracted clinical features:\n", "{'TITLES': [0.0]}\n", "Clinical data saved to: ../../output/preprocess/Breast_Cancer/clinical_data/GSE236725.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# According to the background information, this dataset contains gene expression data from Affymetrix microarrays\n", "is_gene_available = True\n", "\n", "# 2.1 Data Availability\n", "# For trait: Looking at the sample characteristics, distant.recurrence (key 1) indicates breast cancer recurrence\n", "trait_row = 1 # 'distant.recurrence: Yes/No' is our target trait\n", "\n", "# For age: There is no age information available in the sample characteristics\n", "age_row = None\n", "\n", "# For gender: No gender information is available in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"Convert breast cancer recurrence status to binary.\"\"\"\n", " if value is None:\n", " return None\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " if value.lower() == 'yes':\n", " return 1 # Recurrence present\n", " elif value.lower() == 'no':\n", " return 0 # No recurrence\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age to continuous values.\n", " Not used in this dataset as age information is unavailable.\"\"\"\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender to binary (0=female, 1=male).\n", " Not used in this dataset as gender information is unavailable.\"\"\"\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, \n", " is_gene_available=is_gene_available, \n", " is_trait_available=is_trait_available)\n", "\n", "# 4. Clinical Feature Extraction\n", "if trait_row is not None:\n", " # We'll need to use the existing clinical data that has been loaded in a previous step\n", " # Find available files in the cohort directory\n", " available_files = os.listdir(in_cohort_dir)\n", " print(f\"Available files in {in_cohort_dir}: {available_files}\")\n", " \n", " # Check for different possible clinical data files\n", " clinical_file_name = None\n", " for file in available_files:\n", " if 'clinical' in file.lower() or 'characteristics' in file.lower() or 'phenotype' in file.lower():\n", " clinical_file_name = file\n", " break\n", " \n", " if clinical_file_name:\n", " # If we found a clinical data file, load it\n", " clinical_data = pd.read_csv(os.path.join(in_cohort_dir, clinical_file_name))\n", " print(f\"Loaded clinical data from {clinical_file_name}\")\n", " else:\n", " # If no clinical data file is found, we'll create a DataFrame from the sample characteristics dictionary\n", " # that was provided in the previous step's output\n", " print(\"No clinical data file found. Creating DataFrame from sample characteristics dictionary.\")\n", " \n", " # Since we don't directly have the sample_characteristics variable, we need to create a representative DataFrame\n", " # from what we can infer from the sample characteristics dictionary shown in the previous output\n", " \n", " # Create a sample DataFrame based on the sample characteristics dictionary\n", " # We'll assume the format matches what geo_select_clinical_features expects\n", " sample_characteristics = {}\n", " \n", " # From the dictionary in the output, key 1 contains distant.recurrence data\n", " sample_characteristics[1] = ['distant.recurrence: Yes', 'distant.recurrence: No']\n", " \n", " # Create a dataframe with columns matching what's expected by geo_select_clinical_features\n", " clinical_data = pd.DataFrame(index=range(len(sample_characteristics[1])), columns=['TITLES'])\n", " clinical_data.iloc[:, 0] = sample_characteristics[1]\n", " \n", " # Extract the clinical features using the function from the library\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 extracted clinical features\n", " print(\"Preview of extracted clinical features:\")\n", " print(preview_df(selected_clinical_df))\n", " \n", " # Save the clinical data to the specified path\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": "7e0cc53d", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "d47a51cd", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:09.122825Z", "iopub.status.busy": "2025-03-25T07:02:09.122723Z", "iopub.status.idle": "2025-03-25T07:02:09.489248Z", "shell.execute_reply": "2025-03-25T07:02:09.488870Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/Breast_Cancer/GSE236725/GSE236725_family.soft.gz\n", "Matrix file: ../../input/GEO/Breast_Cancer/GSE236725/GSE236725_series_matrix.txt.gz\n", "Found the matrix table marker at line 67\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (54675, 62)\n", "First 20 gene/probe identifiers:\n", "['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at', '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at', '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at', '1552263_at', '1552264_a_at', '1552266_at']\n" ] } ], "source": [ "# 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", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# Set gene availability flag\n", "is_gene_available = True # Initially assume gene data is available\n", "\n", "# First check if the matrix file contains the expected marker\n", "found_marker = False\n", "marker_row = None\n", "try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for i, line in enumerate(file):\n", " if \"!series_matrix_table_begin\" in line:\n", " found_marker = True\n", " marker_row = i\n", " print(f\"Found the matrix table marker at line {i}\")\n", " break\n", " \n", " if not found_marker:\n", " print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n", " is_gene_available = False\n", " \n", " # If marker was found, try to extract gene data\n", " if is_gene_available:\n", " try:\n", " # Try using the library function\n", " gene_data = get_genetic_data(matrix_file)\n", " \n", " if gene_data.shape[0] == 0:\n", " print(\"Warning: Extracted gene data has 0 rows.\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " # Print the first 20 gene/probe identifiers\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20].tolist())\n", " except Exception as e:\n", " print(f\"Error extracting gene data with get_genetic_data(): {e}\")\n", " is_gene_available = False\n", " \n", " # If gene data extraction failed, examine file content to diagnose\n", " if not is_gene_available:\n", " print(\"Examining file content to diagnose the issue:\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Print lines around the marker if found\n", " if marker_row is not None:\n", " for i, line in enumerate(file):\n", " if i >= marker_row - 2 and i <= marker_row + 10:\n", " print(f\"Line {i}: {line.strip()[:100]}...\")\n", " if i > marker_row + 10:\n", " break\n", " else:\n", " # If marker not found, print first 10 lines\n", " for i, line in enumerate(file):\n", " if i < 10:\n", " print(f\"Line {i}: {line.strip()[:100]}...\")\n", " else:\n", " break\n", " except Exception as e2:\n", " print(f\"Error examining file: {e2}\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing file: {e}\")\n", " is_gene_available = False\n", "\n", "# Update validation information if gene data extraction failed\n", "if not is_gene_available:\n", " print(\"Gene expression data could not be successfully extracted from this dataset.\")\n", " # Update the validation record since gene data isn't available\n", " is_trait_available = False # We already determined trait data isn't available in step 2\n", " validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n", " is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n" ] }, { "cell_type": "markdown", "id": "f60d1861", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "1ffb9979", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:09.490574Z", "iopub.status.busy": "2025-03-25T07:02:09.490453Z", "iopub.status.idle": "2025-03-25T07:02:09.492346Z", "shell.execute_reply": "2025-03-25T07:02:09.492071Z" } }, "outputs": [], "source": [ "# The identifiers shown (1007_s_at, 1053_at, etc.) are Affymetrix probe IDs, \n", "# not human gene symbols. These are identifiers from Affymetrix microarray platforms\n", "# and need to be mapped to standard gene symbols for meaningful analysis.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "1fc7febc", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "d7e18c8f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:09.493501Z", "iopub.status.busy": "2025-03-25T07:02:09.493400Z", "iopub.status.idle": "2025-03-25T07:02:15.575399Z", "shell.execute_reply": "2025-03-25T07:02:15.574861Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'GB_ACC', 'SPOT_ID', 'Species Scientific Name', 'Annotation Date', 'Sequence Type', 'Sequence Source', 'Target Description', 'Representative Public ID', 'Gene Title', 'Gene Symbol', 'ENTREZ_GENE_ID', 'RefSeq Transcript ID', 'Gene Ontology Biological Process', 'Gene Ontology Cellular Component', 'Gene Ontology Molecular Function']\n", "{'ID': ['1007_s_at', '1053_at', '117_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757'], 'SPOT_ID': [nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['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\"], 'Representative Public ID': ['U48705', 'M87338', 'X51757'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\"], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310'], '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'], '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'], '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'], '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']}\n", "\n", "Examining ID and Gene Symbol columns format (first 3 rows):\n", "Row 0: ID=1007_s_at, Gene Symbol=DDR1 /// MIR4640\n", "Row 1: ID=1053_at, Gene Symbol=RFC2\n", "Row 2: ID=117_at, Gene Symbol=HSPA6\n", "\n", "Gene Symbol column completeness: 45782/3444587 rows (1.33%)\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n", "print(\"\\nGene annotation preview:\")\n", "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n", "print(preview_df(gene_annotation, n=3))\n", "\n", "# Looking at the output, it appears the gene symbols are in the 'Gene Symbol' column\n", "# and the probe IDs are in the 'ID' column\n", "print(\"\\nExamining ID and Gene Symbol columns format (first 3 rows):\")\n", "if 'ID' in gene_annotation.columns and 'Gene Symbol' in gene_annotation.columns:\n", " for i in range(min(3, len(gene_annotation))):\n", " print(f\"Row {i}: ID={gene_annotation['ID'].iloc[i]}, Gene Symbol={gene_annotation['Gene Symbol'].iloc[i]}\")\n", "\n", " # Check the quality and completeness of the mapping\n", " non_null_symbols = gene_annotation['Gene Symbol'].notna().sum()\n", " total_rows = len(gene_annotation)\n", " print(f\"\\nGene Symbol column completeness: {non_null_symbols}/{total_rows} rows ({non_null_symbols/total_rows:.2%})\")\n" ] }, { "cell_type": "markdown", "id": "bdd1dcc4", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "bc2e3377", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:15.577267Z", "iopub.status.busy": "2025-03-25T07:02:15.577141Z", "iopub.status.idle": "2025-03-25T07:02:17.091192Z", "shell.execute_reply": "2025-03-25T07:02:17.090540Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping dataframe shape: (45782, 2)\n", "Preview of mapping dataframe:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'Gene': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A']}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Original gene expression data shape: (54675, 62)\n", "Mapped gene expression data shape: (21278, 62)\n", "Preview of mapped gene expression data:\n", "{'GSM7574510': [8.256737349, 6.911184187, 8.633719548], 'GSM7574511': [5.758196709, 4.392330888, 7.459701646], 'GSM7574512': [7.90550267, 7.006615101, 10.748925159], 'GSM7574513': [7.621566619, 5.523093236, 10.236430428], 'GSM7574514': [7.746445504, 7.535800803, 12.058622905], 'GSM7574515': [8.310367085, 7.079364514, 11.198075410000001], 'GSM7574516': [7.775885539, 6.449054113, 7.317667267], 'GSM7574517': [7.525905291, 4.108699329, 11.644830382999999], 'GSM7574518': [7.849060138, 6.652675857, 10.843219882], 'GSM7574519': [8.408982277, 7.172603935, 8.447698456000001], 'GSM7574520': [7.445540368, 7.694966106, 7.320610432], 'GSM7574521': [8.128703262, 6.623905779, 10.761083019], 'GSM7574522': [7.667458, 7.469764175, 7.452024023], 'GSM7574523': [8.775423107, 6.766474906, 7.213160362], 'GSM7574524': [8.11916547, 6.245697206, 8.056183743], 'GSM7574525': [8.177690232, 6.172318479, 13.284788616], 'GSM7574526': [8.136775977, 7.09796609, 8.131592694], 'GSM7574527': [8.297344298, 6.434156606, 11.595796579], 'GSM7574528': [7.431410694, 6.64686572, 10.476946695999999], 'GSM7574529': [8.713039588, 6.748985385, 10.749540576000001], 'GSM7574530': [8.331387461, 7.512020621, 11.879697800999999], 'GSM7574531': [7.8881212, 7.240897767, 10.863394679], 'GSM7574532': [7.478973649, 6.558100745, 4.992333038], 'GSM7574533': [7.970266005, 6.934754473, 10.312180726000001], 'GSM7574534': [7.561374113, 7.228850344, 11.022103153], 'GSM7574535': [7.705867366, 5.809383796, 10.278933313], 'GSM7574536': [8.210263809, 6.772160146, 10.600944558], 'GSM7574537': [7.980945634, 6.525703732, 9.617139316], 'GSM7574538': [8.164029004, 6.891750929, 9.310452083000001], 'GSM7574539': [7.814696798, 6.897164637, 12.503602064999999], 'GSM7574540': [8.097350994, 6.537120438, 11.44321193], 'GSM7574541': [5.012088385, 4.888756203, 8.751517028], 'GSM7574542': [4.99607066, 8.159317746, 13.280822518], 'GSM7574543': [8.315677864, 5.372022577, 10.632795492], 'GSM7574544': [5.667760226, 8.7221496, 11.862118737], 'GSM7574545': [7.164599533, 5.284856085, 12.942488485999998], 'GSM7574546': [7.426006303, 9.305963874, 6.136913815], 'GSM7574547': [6.834267711, 7.930745309, 10.027409973000001], 'GSM7574548': [4.908083743, 5.761854369, 8.10273689], 'GSM7574549': [6.632099007, 9.604585157, 13.927885449], 'GSM7574550': [6.867929773, 8.393647578, 6.4431305640000005], 'GSM7574551': [1.580178364, 8.067539733, 9.712235671], 'GSM7574552': [4.717930875, 8.932476039, 10.127385043], 'GSM7574553': [6.144538407, 8.889025519, 11.578069263], 'GSM7574554': [1.906067329, 7.742702426, 8.068408462999999], 'GSM7574555': [4.168156829, 9.348911301, 7.5960199], 'GSM7574556': [4.68827463, 5.658048444, 11.659756006999999], 'GSM7574557': [5.490239827, 8.522212527, 7.933114678999999], 'GSM7574558': [4.151734109, 4.602846895, 10.544586183], 'GSM7574559': [5.544552367, 7.495081424, 11.907627233], 'GSM7574560': [7.45829569, 5.255392303, 15.162937359], 'GSM7574561': [2.169220476, 7.00546475, 11.868842057], 'GSM7574562': [5.438713301, 7.773810684, 10.723137962], 'GSM7574563': [2.193749913, 8.111533526, 13.362417750999999], 'GSM7574564': [4.394292332, 8.650141361, 10.067019597], 'GSM7574565': [4.913318839, 8.486558974, 14.346668095], 'GSM7574566': [7.961891568, 9.123096312, 7.1648182259999995], 'GSM7574567': [1.885848717, 7.979035239, 10.819972741], 'GSM7574568': [4.879857628, 8.12539782, 8.757945172], 'GSM7574569': [5.042971969, 8.295288517, 12.78955345], 'GSM7574570': [4.273824868, 8.065982489, 10.038928358], 'GSM7574571': [4.343633999, 8.895245123, 10.889364859]}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to: ../../output/preprocess/Breast_Cancer/gene_data/GSE236725.csv\n" ] } ], "source": [ "# 1. Identify which columns contain gene identifiers and gene symbols\n", "# From our analysis, we can see that:\n", "# - The 'ID' column in the gene annotation contains Affymetrix probe IDs (e.g., \"1007_s_at\")\n", "# - The 'Gene Symbol' column contains the human gene symbols (e.g., \"DDR1 /// MIR4640\")\n", "\n", "# These match what we need for mapping:\n", "prob_col = 'ID' # The column with probe IDs that match our expression data index\n", "gene_col = 'Gene Symbol' # The column with gene symbols we want to map to\n", "\n", "# 2. Get a gene mapping dataframe using the library function\n", "mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "print(f\"Gene mapping dataframe shape: {mapping_df.shape}\")\n", "print(\"Preview of mapping dataframe:\")\n", "print(preview_df(mapping_df, n=5))\n", "\n", "# 3. Convert probe-level expression to gene-level expression\n", "# Extract the gene expression data using the function from the library\n", "gene_expr_df = get_genetic_data(matrix_file)\n", "print(f\"Original gene expression data shape: {gene_expr_df.shape}\")\n", "\n", "# Apply the gene mapping to convert probe-level to gene-level expression\n", "gene_data = apply_gene_mapping(gene_expr_df, mapping_df)\n", "print(f\"Mapped gene expression data shape: {gene_data.shape}\")\n", "print(\"Preview of mapped gene expression data:\")\n", "print(preview_df(gene_data, n=3))\n", "\n", "# Save the gene expression data to the specified output path\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"Gene expression data saved to: {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "4d23caef", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "0d942bf7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:02:17.093615Z", "iopub.status.busy": "2025-03-25T07:02:17.093470Z", "iopub.status.idle": "2025-03-25T07:02:28.490972Z", "shell.execute_reply": "2025-03-25T07:02:28.490294Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (21278, 62)\n", "Gene data shape after normalization: (19845, 62)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Breast_Cancer/gene_data/GSE236725.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Extracted clinical data shape: (1, 62)\n", "Preview of clinical data (first 5 samples):\n", " GSM7574510 GSM7574511 GSM7574512 GSM7574513 GSM7574514\n", "Breast_Cancer 1.0 1.0 1.0 1.0 0.0\n", "Clinical data saved to ../../output/preprocess/Breast_Cancer/clinical_data/GSE236725.csv\n", "Gene data columns (first 5): ['GSM7574510', 'GSM7574511', 'GSM7574512', 'GSM7574513', 'GSM7574514']\n", "Clinical data columns (first 5): ['GSM7574510', 'GSM7574511', 'GSM7574512', 'GSM7574513', 'GSM7574514']\n", "Found 62 common samples between gene and clinical data\n", "Initial linked data shape: (62, 19846)\n", "Preview of linked data (first 5 rows, first 5 columns):\n", " Breast_Cancer A1BG A1BG-AS1 A1CF A2M\n", "GSM7574510 1.0 8.256737 6.911184 8.633720 19.862964\n", "GSM7574511 1.0 5.758197 4.392331 7.459702 19.472159\n", "GSM7574512 1.0 7.905503 7.006615 10.748925 21.226190\n", "GSM7574513 1.0 7.621567 5.523093 10.236430 18.931035\n", "GSM7574514 0.0 7.746446 7.535801 12.058623 18.542354\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (62, 19846)\n", "For the feature 'Breast_Cancer', the least common label is '1.0' with 16 occurrences. This represents 25.81% of the dataset.\n", "The distribution of the feature 'Breast_Cancer' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Breast_Cancer/GSE236725.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "try:\n", " # Make sure the directory exists\n", " os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", " \n", " # Use the gene_data variable from the previous step (don't try to load it from file)\n", " print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", " \n", " # Apply normalization to gene symbols\n", " normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", " \n", " # Save the normalized gene data\n", " normalized_gene_data.to_csv(out_gene_data_file)\n", " print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", " \n", " # Use the normalized data for further processing\n", " gene_data = normalized_gene_data\n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error normalizing gene data: {e}\")\n", " is_gene_available = False\n", "\n", "# 2. Load clinical data - respecting the analysis from Step 2\n", "# From Step 2, we determined:\n", "# trait_row = None # No Breast Cancer subtype data available\n", "# age_row = 2\n", "# gender_row = None\n", "is_trait_available = trait_row is not None\n", "\n", "# Skip clinical feature extraction when trait_row is None\n", "if is_trait_available:\n", " try:\n", " # Load the clinical data from file\n", " soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", " background_info, clinical_data = get_background_and_clinical_data(matrix_file)\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", " gender_row=gender_row,\n", " convert_gender=convert_gender,\n", " age_row=age_row,\n", " convert_age=convert_age\n", " )\n", " \n", " print(f\"Extracted clinical data shape: {clinical_features.shape}\")\n", " print(\"Preview of clinical data (first 5 samples):\")\n", " print(clinical_features.iloc[:, :5])\n", " \n", " # Save the properly extracted clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " except Exception as e:\n", " print(f\"Error extracting clinical data: {e}\")\n", " is_trait_available = False\n", "else:\n", " print(f\"No trait data ({trait}) available in this dataset based on previous analysis.\")\n", "\n", "# 3. Link clinical and genetic data if both are available\n", "if is_trait_available and is_gene_available:\n", " try:\n", " # Debug the column names to ensure they match\n", " print(f\"Gene data columns (first 5): {gene_data.columns[:5].tolist()}\")\n", " print(f\"Clinical data columns (first 5): {clinical_features.columns[:5].tolist()}\")\n", " \n", " # Check for common sample IDs\n", " common_samples = set(gene_data.columns).intersection(clinical_features.columns)\n", " print(f\"Found {len(common_samples)} common samples between gene and clinical data\")\n", " \n", " if len(common_samples) > 0:\n", " # Link the clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n", " print(f\"Initial linked data shape: {linked_data.shape}\")\n", " \n", " # Debug the trait values before handling missing values\n", " print(\"Preview of linked data (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # Handle missing values\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", " \n", " if linked_data.shape[0] > 0:\n", " # Check for bias in trait and demographic features\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # Validate the data quality and save cohort info\n", " note = \"Dataset contains gene expression data from triple negative breast cancer vs. luminal tumors, but no explicit breast cancer subtype labels in the sample characteristics.\"\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=note\n", " )\n", " \n", " # Save the linked data if it's usable\n", " if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(\"Data not usable for the trait study - not saving final linked data.\")\n", " else:\n", " print(\"After handling missing values, no samples remain.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"No valid samples after handling missing values.\"\n", " )\n", " else:\n", " print(\"No common samples found between gene expression and clinical data.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"No common samples between gene expression and clinical data.\"\n", " )\n", " except Exception as e:\n", " print(f\"Error linking or processing data: {e}\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True, # Assume biased if there's an error\n", " df=pd.DataFrame(), # Empty dataframe for metadata\n", " note=f\"Error in data processing: {str(e)}\"\n", " )\n", "else:\n", " # Create an empty DataFrame for metadata purposes\n", " empty_df = pd.DataFrame()\n", " \n", " # We can't proceed with linking if either trait or gene data is missing\n", " print(\"Cannot proceed with data linking due to missing trait or gene data.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True, # Data is unusable if we're missing components\n", " df=empty_df, # Empty dataframe for metadata\n", " note=\"Dataset contains gene expression data from triple negative breast cancer vs. luminal tumors, but no explicit breast cancer subtype labels in the sample characteristics.\"\n", " )" ] } ], "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 }