{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "294af99f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:05:16.213356Z", "iopub.status.busy": "2025-03-25T06:05:16.213248Z", "iopub.status.idle": "2025-03-25T06:05:16.374356Z", "shell.execute_reply": "2025-03-25T06:05:16.374014Z" } }, "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 = \"Pancreatic_Cancer\"\n", "cohort = \"GSE124069\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Pancreatic_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Pancreatic_Cancer/GSE124069\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Pancreatic_Cancer/GSE124069.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Pancreatic_Cancer/gene_data/GSE124069.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Pancreatic_Cancer/clinical_data/GSE124069.csv\"\n", "json_path = \"../../output/preprocess/Pancreatic_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "8449b409", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "bedb9c10", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:05:16.375722Z", "iopub.status.busy": "2025-03-25T06:05:16.375583Z", "iopub.status.idle": "2025-03-25T06:05:16.511120Z", "shell.execute_reply": "2025-03-25T06:05:16.510782Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"BRD4 inhibitor JQ1 increase the sensitivity of arsenic trioxide in pancreatic cancer\"\n", "!Series_summary\t\"Pancreatic cancer is a deadliest type of malignancy, largely due to lack of effective intervention. We here report a pair of agents, ATO and JQ1, which synergistically induce apoptosis in the malignancy. Through global and molecular approaches, we have provided evidence that these agents are both able to modulate ER stress and autophagy in the cancer, probably acting in different ways. Cross-talks between ER stress and autophagy are implicated during ATO/ATO plus JQ1 induced apoptosis, in which NRF2 appears to play a central role. of the globe transcriptional profiles of ATO regulated genes in breast, colon and lung cancer cells with different p53 status. We find p53 wild type cells are resistant to ATO induced globe dynamic transcriptional changes, thus resistant to ATO induced cell growth inhibition. P53 inhibitor PFTα releases p53 mediated transcriptional resistance and increases the sensitivity of ATO in p53 wild type tumor cells.\"\n", "!Series_overall_design\t\"Pancreatic cancer cell lines BXPC3 and MIApaca were treated with ATO 2.5μM for 0, 6, 12, 24 and 48 hours. Pancreatic cancer cell lines ASPC1 and PANC1 which were unsensitive to ATO treatment, were treated with ATO, JQ1(1μM) or ATO and JQ1 combination for 0, 6, 24 and 48 hours. Total RNA was collected and profiled to Affymetrix microarray\"\n", "Sample Characteristics Dictionary:\n", "{0: ['disease state: pancreatic cancer'], 1: ['time point: 0h', 'time point: 6h', 'time point: 24h', 'time point: 48h', 'time point: 12h'], 2: ['treatment: untreated', 'treatment: ATO', 'treatment: JQ1', 'treatment: ATO and JQ1'], 3: ['cell line: ASPC1', 'cell line: PANC1', 'cell line: BXPC3', 'cell line: MIApaca']}\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": "19fb4a53", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "fcd9e21e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:05:16.512540Z", "iopub.status.busy": "2025-03-25T06:05:16.512428Z", "iopub.status.idle": "2025-03-25T06:05:16.533163Z", "shell.execute_reply": "2025-03-25T06:05:16.532879Z" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. Gene Expression Data Availability\n", "# This dataset appears to contain gene expression data as it mentions \"Total RNA was collected and\n", "# profiled to Affymetrix microarray\" in the Series_overall_design\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "\n", "# 2.1 For Trait (Pancreatic Cancer)\n", "# All samples are pancreatic cancer cell lines according to the background info\n", "# Trait should be binary, but since all samples are pancreatic cancer, this is a constant feature\n", "trait_row = None # No variation in disease state - all are pancreatic cancer\n", "\n", "# 2.2 For Age\n", "# No information on age - these are cell lines\n", "age_row = None\n", "\n", "# 2.3 For Gender\n", "# No information on gender - these are cell lines\n", "gender_row = None\n", "\n", "# Conversion functions\n", "def convert_trait(value):\n", " \"\"\"\n", " Convert trait values.\n", " \"\"\"\n", " if value is None:\n", " return None\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " if 'pancreatic cancer' in value.lower():\n", " return 1\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"\n", " Convert age values.\n", " \"\"\"\n", " # Not applicable for this dataset\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"\n", " Convert gender values.\n", " \"\"\"\n", " # Not applicable for this dataset\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Check if trait data is available (if trait_row is not None)\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort info\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", "# Skip this step as trait_row is None (no clinical variation exists in this dataset)\n" ] }, { "cell_type": "markdown", "id": "ecddb0a4", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "b80b92fc", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:05:16.534510Z", "iopub.status.busy": "2025-03-25T06:05:16.534404Z", "iopub.status.idle": "2025-03-25T06:05:16.729511Z", "shell.execute_reply": "2025-03-25T06:05:16.729129Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "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. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n", "print(gene_data.index[:20])\n" ] }, { "cell_type": "markdown", "id": "f51f62d0", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "977fa845", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:05:16.730769Z", "iopub.status.busy": "2025-03-25T06:05:16.730545Z", "iopub.status.idle": "2025-03-25T06:05:16.732484Z", "shell.execute_reply": "2025-03-25T06:05:16.732191Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers in the gene expression data\n", "# These identifiers (like '1007_s_at', '1053_at', etc.) are not human gene symbols\n", "# They are Affymetrix probe IDs, which need to be mapped to human gene symbols\n", "# These appear to be Affymetrix HG-U133 array probe IDs\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "738c4a49", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "6b79503e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:05:16.733507Z", "iopub.status.busy": "2025-03-25T06:05:16.733401Z", "iopub.status.idle": "2025-03-25T06:05:19.985347Z", "shell.execute_reply": "2025-03-25T06:05:19.985014Z" } }, "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": "328813ac", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "d24a6eca", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:05:19.987137Z", "iopub.status.busy": "2025-03-25T06:05:19.987010Z", "iopub.status.idle": "2025-03-25T06:05:20.189185Z", "shell.execute_reply": "2025-03-25T06:05:20.188740Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data preview (after mapping):\n", "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n", " 'A4GALT', 'A4GNT', 'AA06', 'AAAS', 'AACS', 'AACSP1', 'AADAC', 'AADACL2',\n", " 'AADACP1', 'AADAT', 'AAED1', 'AAGAB', 'AAK1'],\n", " dtype='object', name='Gene')\n", "Total number of genes after mapping: 21278\n" ] } ], "source": [ "# 1. Identify columns for probe IDs and gene symbols in the annotation dataframe\n", "prob_col = 'ID' # The probe identifiers are stored in the 'ID' column\n", "gene_col = 'Gene Symbol' # The gene symbols are stored in the 'Gene Symbol' column\n", "\n", "# 2. Get a gene mapping dataframe by extracting these two columns\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "\n", "# 3. Convert probe-level measurements to gene expression data by applying the gene mapping\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# Preview the gene-level expression data\n", "print(\"Gene expression data preview (after mapping):\")\n", "print(gene_data.index[:20]) # Print the first 20 gene symbols\n", "print(f\"Total number of genes after mapping: {len(gene_data)}\")\n" ] }, { "cell_type": "markdown", "id": "a3d58ca9", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "1138103a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:05:20.190955Z", "iopub.status.busy": "2025-03-25T06:05:20.190830Z", "iopub.status.idle": "2025-03-25T06:05:26.711820Z", "shell.execute_reply": "2025-03-25T06:05:26.711436Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Pancreatic_Cancer/gene_data/GSE124069.csv\n", "Created dummy clinical data with shape: (1, 30)\n", "Linked data shape: (30, 19846)\n", "Using trait column: Pancreatic_Cancer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Shape after handling missing values: (30, 19846)\n", "The trait is severely biased (all samples are pancreatic cancer).\n", "Data quality check failed. Dataset lacks necessary trait variation for association studies.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "normalized_gene_data = normalize_gene_symbols_in_index(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 data saved to {out_gene_data_file}\")\n", "\n", "# Since we determined in Step 2 that trait_row is None (no clinical variation)\n", "# and is_trait_available is False, we can't proceed with linking clinical and gene data\n", "# Instead, we need to create dummy clinical data with a constant trait value\n", "\n", "# Create minimal clinical dataframe with samples matching gene data\n", "sample_ids = normalized_gene_data.columns\n", "dummy_clinical_df = pd.DataFrame(index=['Pancreatic_Cancer'], \n", " columns=sample_ids,\n", " data=[[1] * len(sample_ids)]) # All samples are pancreatic cancer\n", "\n", "print(f\"Created dummy clinical data with shape: {dummy_clinical_df.shape}\")\n", "\n", "# 2. Link the clinical and genetic data (even though clinical data is just a constant)\n", "linked_data = geo_link_clinical_genetic_data(dummy_clinical_df, normalized_gene_data)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# Identify trait column\n", "trait_col = linked_data.columns[0]\n", "print(f\"Using trait column: {trait_col}\")\n", "\n", "# 3. Handle missing values in the linked data\n", "linked_data = handle_missing_values(linked_data, trait_col)\n", "print(f\"Shape after handling missing values: {linked_data.shape}\")\n", "\n", "# 4. Determine whether the trait and demographic features are severely biased\n", "# Since we know all samples are pancreatic cancer, this trait is severely biased\n", "is_trait_biased = True\n", "print(\"The trait is severely biased (all samples are pancreatic cancer).\")\n", "unbiased_linked_data = linked_data\n", "\n", "# 5. Conduct quality check and save the cohort information\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=False, # We previously determined trait data is not available (no variation)\n", " is_biased=is_trait_biased, \n", " df=unbiased_linked_data,\n", " note=\"Dataset contains gene expression data for pancreatic cancer cell lines, but lacks normal controls.\"\n", ")\n", "\n", "# 6. Since the data is not usable (no trait variation), don't save it\n", "if is_usable:\n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " # Save the data\n", " unbiased_linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Data quality check failed. Dataset lacks necessary trait variation for association studies.\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }