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{
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"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 = \"Autoinflammatory_Disorders\"\n",
"cohort = \"GSE80060\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Autoinflammatory_Disorders\"\n",
"in_cohort_dir = \"../../input/GEO/Autoinflammatory_Disorders/GSE80060\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Autoinflammatory_Disorders/GSE80060.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Autoinflammatory_Disorders/gene_data/GSE80060.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Autoinflammatory_Disorders/clinical_data/GSE80060.csv\"\n",
"json_path = \"../../output/preprocess/Autoinflammatory_Disorders/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "34eb7157",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Gene expression data of whole blood of systemic juvenile idiopathic arthritis (SJIA) patients treated with canakinumab or placebo and age matched healthy controls\"\n",
"!Series_summary\t\"Canakinumab is a human anti-interleukin-1 beta (IL-1 beta) monoclonal antibody neutralizing IL-1 beta. Systemic juvenile idiopathic arthritis (SJIA) is a rare, multigenic, autoinflammatory disease of unknown etiology characterized by chronic arthritis; intermittent high-spiking fever, rash, and elevated levels of acute-phase reactants. Blood samples of SJIA patients were obtained from two phase 3 clinical trials conducted by the members of the Pediatric Rheumatology International Trials Organization (PRINTO) and the Pediatric Rheumatology Collaborative Study Group (PRCSG) (Clinicaltrials.gov: NCT00886769 and NCT00889863). For patients, baseline and day 3 samples were analyzed for either placebo or canakinumab (Ilaris) treatment.\"\n",
"!Series_summary\t\"Clinical response was assessed at day 15 using adapted JIA American College of Rheumatology (ACR) response criteria.\"\n",
"!Series_overall_design\t\"Overall, 206 samples were used in this study including 22 samples from healthy controls, 33 samples of placebo treated patients and 151 samples of canakinumab treated patients.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: Whole blood'], 1: ['disease status: SJIA', 'disease status: Healthy'], 2: ['subject id: SJIA_2_2513', 'subject id: SJIA_2_313', 'subject id: SJIA_2_413', 'subject id: SJIA_2_712', 'subject id: SJIA_2_812', 'subject id: SJIA_2_912', 'subject id: SJIA_2_1013', 'subject id: SJIA_2_1112', 'subject id: SJIA_2_2912', 'subject id: SJIA_2_3012', 'subject id: SJIA_2_1413', 'subject id: SJIA_2_1411', 'subject id: SJIA_2_168', 'subject id: SJIA_2_167', 'subject id: SJIA_2_1713', 'subject id: SJIA_2_1811', 'subject id: SJIA_2_185', 'subject id: SJIA_2_1912', 'subject id: SJIA_2_2213', 'subject id: SJIA_2_2313', 'subject id: SJIA_2_2312', 'subject id: SJIA_2_113', 'subject id: SJIA_2_2613', 'subject id: SJIA_2_212', 'subject id: SJIA_2_310', 'subject id: SJIA_2_36', 'subject id: SJIA_2_512', 'subject id: SJIA_2_511', 'subject id: SJIA_2_613', 'subject id: SJIA_2_612'], 3: ['visit: Day1_BL', 'visit: Day3'], 4: ['treatment: Canakinumab', 'treatment: Placebo', 'treatment: none'], 5: ['acr response at day 15: 100', 'acr response at day 15: NA', 'acr response at day 15: 30', 'acr response at day 15: 70', 'acr response at day 15: 90', 'acr response at day 15: 0', 'acr response at day 15: 50']}\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": "d4749d5c",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "64bb7a55",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical data:\n",
"{'feature_0': [nan], 'feature_1': [0.0], 'feature_2': [1.0], 'feature_3': [nan], 'feature_4': [nan], 'feature_5': [nan]}\n",
"Clinical data saved to ../../output/preprocess/Autoinflammatory_Disorders/clinical_data/GSE80060.csv\n"
]
}
],
"source": [
"# Libraries needed\n",
"import pandas as pd\n",
"import os\n",
"import json\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# 1. Determine if gene expression data is available\n",
"# Based on the background information, this is a gene expression dataset from whole blood\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable availability and data type conversion\n",
"# 2.1 Identify keys for trait, age, and gender\n",
"# For trait, we can use disease status at index 1\n",
"trait_row = 1 # Disease status (SJIA vs Healthy)\n",
"\n",
"# Age and gender are not explicitly provided in the sample characteristics\n",
"age_row = None # Age not available\n",
"gender_row = None # Gender not available\n",
"\n",
"# 2.2 Data type conversion functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert disease status to binary (1 for SJIA, 0 for Healthy)\"\"\"\n",
" if value is None:\n",
" return None\n",
" # Extract the value after the colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if 'SJIA' in value:\n",
" return 1 # SJIA\n",
" elif 'Healthy' in value:\n",
" return 0 # Healthy control\n",
" else:\n",
" return None # Unknown or not applicable\n",
"\n",
"# Since age and gender are not available, we still define placeholder functions\n",
"def convert_age(value):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" return None\n",
"\n",
"# 3. Save metadata about dataset usability\n",
"# Determine trait availability based on trait_row\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save initial filtering results\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 (only if trait_row is not None)\n",
"if trait_row is not None:\n",
" # Create a sample characteristics dictionary from the output of previous step\n",
" sample_characteristics_dict = {\n",
" 0: ['tissue: Whole blood'], \n",
" 1: ['disease status: SJIA', 'disease status: Healthy'], \n",
" 2: ['subject id: SJIA_2_2513', 'subject id: SJIA_2_313', 'subject id: SJIA_2_413', 'subject id: SJIA_2_712', \n",
" 'subject id: SJIA_2_812', 'subject id: SJIA_2_912', 'subject id: SJIA_2_1013', 'subject id: SJIA_2_1112', \n",
" 'subject id: SJIA_2_2912', 'subject id: SJIA_2_3012', 'subject id: SJIA_2_1413', 'subject id: SJIA_2_1411', \n",
" 'subject id: SJIA_2_168', 'subject id: SJIA_2_167', 'subject id: SJIA_2_1713', 'subject id: SJIA_2_1811', \n",
" 'subject id: SJIA_2_185', 'subject id: SJIA_2_1912', 'subject id: SJIA_2_2213', 'subject id: SJIA_2_2313', \n",
" 'subject id: SJIA_2_2312', 'subject id: SJIA_2_113', 'subject id: SJIA_2_2613', 'subject id: SJIA_2_212', \n",
" 'subject id: SJIA_2_310', 'subject id: SJIA_2_36', 'subject id: SJIA_2_512', 'subject id: SJIA_2_511', \n",
" 'subject id: SJIA_2_613', 'subject id: SJIA_2_612'], \n",
" 3: ['visit: Day1_BL', 'visit: Day3'], \n",
" 4: ['treatment: Canakinumab', 'treatment: Placebo', 'treatment: none'], \n",
" 5: ['acr response at day 15: 100', 'acr response at day 15: NA', 'acr response at day 15: 30', \n",
" 'acr response at day 15: 70', 'acr response at day 15: 90', 'acr response at day 15: 0', \n",
" 'acr response at day 15: 50']\n",
" }\n",
" \n",
" # From the output of previous step, determine max length needed for the DataFrame\n",
" max_len = max(len(values) for values in sample_characteristics_dict.values())\n",
" \n",
" # Create a DataFrame with appropriate dimensions\n",
" clinical_data = pd.DataFrame(index=range(max_len))\n",
" \n",
" # Add each feature as a column, padding shorter lists with None\n",
" for key, values in sample_characteristics_dict.items():\n",
" # Extend shorter lists with None values\n",
" padded_values = values + [None] * (max_len - len(values))\n",
" clinical_data[f'feature_{key}'] = padded_values\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 extracted clinical data\n",
" preview_data = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical data:\")\n",
" print(preview_data)\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save to CSV\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "199de9e0",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "dd1f4c90",
"metadata": {
"execution": {
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"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": "43c5e642",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f0eb0a5b",
"metadata": {
"execution": {
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"source": [
"# The gene identifiers appear to be probe set IDs from an Affymetrix microarray\n",
"# These are not human gene symbols and would need to be mapped to proper gene symbols\n",
"\n",
"# Looking at identifiers like '1007_s_at', '1053_at', etc., these follow the pattern\n",
"# of Affymetrix probe IDs which need to be mapped to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "df86a07c",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "36be6348",
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"name": "stdout",
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"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": "0b7d96cc",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping preview (first 5 rows):\n",
" ID Gene\n",
"0 1007_s_at DDR1 /// MIR4640\n",
"1 1053_at RFC2\n",
"2 117_at HSPA6\n",
"3 121_at PAX8\n",
"4 1255_g_at GUCA1A\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of genes after mapping: 21278\n",
"Sample of gene expression data after mapping (first 5 genes):\n",
" GSM2111993 GSM2111994 GSM2111995 GSM2111996 GSM2111997 \\\n",
"Gene \n",
"A1BG 20.531274 14.741187 11.079894 10.599425 11.433046 \n",
"A1BG-AS1 88.194790 47.904730 81.249540 43.318430 47.088590 \n",
"A1CF 7.832409 7.952191 10.122947 7.881265 8.623113 \n",
"A2M 75.558405 56.413898 77.866086 53.556840 61.194481 \n",
"A2M-AS1 172.515520 345.047360 205.631310 344.107630 429.584250 \n",
"\n",
" GSM2111998 GSM2111999 GSM2112000 GSM2112001 GSM2112002 ... \\\n",
"Gene ... \n",
"A1BG 15.974882 16.569076 14.513591 10.338256 13.122920 ... \n",
"A1BG-AS1 60.742230 71.671910 65.129780 69.265340 56.305330 ... \n",
"A1CF 10.524899 8.914323 8.883382 7.754067 9.206250 ... \n",
"A2M 63.169296 47.926185 66.119639 63.004756 61.837691 ... \n",
"A2M-AS1 205.832720 134.525120 379.994240 408.396170 371.336110 ... \n",
"\n",
" GSM2112189 GSM2112190 GSM2112191 GSM2112192 GSM2112193 \\\n",
"Gene \n",
"A1BG 14.387063 21.652020 14.041388 13.878591 13.629006 \n",
"A1BG-AS1 104.179640 201.202520 106.011750 123.695760 96.545120 \n",
"A1CF 7.844039 11.298281 8.708103 8.625491 7.128476 \n",
"A2M 58.109497 70.152277 59.369086 50.004444 47.648580 \n",
"A2M-AS1 389.584460 210.550010 311.475620 161.494760 207.657620 \n",
"\n",
" GSM2112194 GSM2112195 GSM2112196 GSM2112197 GSM2112198 \n",
"Gene \n",
"A1BG 16.496872 14.003997 14.148560 15.815815 15.271545 \n",
"A1BG-AS1 114.291030 120.550520 110.536450 100.031520 127.508690 \n",
"A1CF 9.355447 9.080456 8.572888 8.933971 8.768014 \n",
"A2M 40.669336 55.749847 41.180565 52.927077 38.665257 \n",
"A2M-AS1 103.648910 71.843920 86.290200 43.169810 86.361580 \n",
"\n",
"[5 rows x 206 columns]\n"
]
}
],
"source": [
"# 1. Observe gene identifiers and annotation to identify relevant columns\n",
"# The gene expression data has identifiers like '1007_s_at' in the index (using 'ID' as key)\n",
"# The gene annotation dataframe has a column 'ID' with the same format identifiers\n",
"# The 'Gene Symbol' column contains the gene symbols we need to map to\n",
"\n",
"# 2. Get gene mapping dataframe\n",
"# Extract relevant columns for mapping from the gene annotation dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
"\n",
"# Preview the mapping dataframe\n",
"print(\"Gene mapping preview (first 5 rows):\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print the number of genes after mapping\n",
"print(f\"Number of genes after mapping: {len(gene_data)}\")\n",
"\n",
"# Show a sample of the mapped gene expression data\n",
"print(\"Sample of gene expression data after mapping (first 5 genes):\")\n",
"print(gene_data.head())\n"
]
},
{
"cell_type": "markdown",
"id": "e7b2c082",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "267197fe",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original gene count: 21278\n",
"Normalized gene count: 19845\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Autoinflammatory_Disorders/gene_data/GSE80060.csv\n",
"Linking clinical and genetic data failed - no valid rows or trait column missing\n",
"Abnormality detected in the cohort: GSE80060. Preprocessing failed.\n",
"The dataset was determined to be not usable for analysis.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"# First, normalize gene symbols using the function from the library\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Original gene count: {len(gene_data)}\")\n",
"print(f\"Normalized gene count: {len(normalized_gene_data)}\")\n",
"\n",
"# Create directory for the gene data file if it doesn't exist\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"\n",
"# Save the normalized gene data to a CSV file\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. We need to first load or recreate the selected_clinical_df\n",
"try:\n",
" # Try to load the previously saved clinical data\n",
" selected_clinical_df = pd.read_csv(out_clinical_data_file)\n",
"except:\n",
" # If loading fails, recreate the 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 extracted clinical features\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
"\n",
" # Save the 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)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
"\n",
"# Check if linking was successful\n",
"if len(linked_data) == 0 or trait not in linked_data.columns:\n",
" print(\"Linking clinical and genetic data failed - no valid rows or trait column missing\")\n",
" # Set is_usable to False and save cohort info\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=True, # Consider it biased if linking fails\n",
" df=pd.DataFrame({trait: [], 'Gender': []}), \n",
" note=\"Data linking failed - unable to process gene expression data appropriately.\"\n",
" )\n",
" print(\"The dataset was determined to be not usable for analysis.\")\n",
"else:\n",
" # 3. Handle missing values in the linked data\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" \n",
" print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" # 4. Determine whether the trait and demographic features are severely biased\n",
" trait_type = 'binary' if len(linked_data[trait].unique()) <= 2 else 'continuous'\n",
" if trait_type == 'binary':\n",
" if len(linked_data[trait].value_counts()) >= 2:\n",
" is_trait_biased = judge_binary_variable_biased(linked_data, trait)\n",
" else:\n",
" print(f\"Trait '{trait}' has only one unique value, considering it biased.\")\n",
" is_trait_biased = True\n",
" else:\n",
" is_trait_biased = judge_continuous_variable_biased(linked_data, trait)\n",
" \n",
" # Remove biased demographic features\n",
" unbiased_linked_data = linked_data.copy()\n",
" if 'Age' in unbiased_linked_data.columns:\n",
" age_biased = judge_continuous_variable_biased(unbiased_linked_data, 'Age')\n",
" if age_biased:\n",
" print(f\"The distribution of the feature \\'Age\\' in this dataset is severely biased.\")\n",
" unbiased_linked_data = unbiased_linked_data.drop(columns=['Age'])\n",
" \n",
" if 'Gender' in unbiased_linked_data.columns:\n",
" if len(unbiased_linked_data['Gender'].value_counts()) >= 2:\n",
" gender_biased = judge_binary_variable_biased(unbiased_linked_data, 'Gender')\n",
" if gender_biased:\n",
" print(f\"The distribution of the feature \\'Gender\\' in this dataset is severely biased.\")\n",
" unbiased_linked_data = unbiased_linked_data.drop(columns=['Gender'])\n",
" else:\n",
" print(f\"Gender has only one unique value, considering it biased and removing.\")\n",
" unbiased_linked_data = unbiased_linked_data.drop(columns=['Gender'])\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=True, \n",
" is_biased=is_trait_biased, \n",
" df=unbiased_linked_data, \n",
" note=\"Dataset contains gene expression data from whole blood of systemic juvenile idiopathic arthritis (SJIA) patients treated with canakinumab or placebo and healthy controls.\"\n",
" )\n",
" \n",
" # 6. If the linked data is usable, save it as a CSV file.\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\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(\"The dataset was determined to be not usable for analysis due to bias in the trait distribution.\")"
]
}
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