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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 = \"Arrhythmia\"\n",
"cohort = \"GSE115574\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Arrhythmia\"\n",
"in_cohort_dir = \"../../input/GEO/Arrhythmia/GSE115574\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Arrhythmia/GSE115574.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Arrhythmia/gene_data/GSE115574.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Arrhythmia/clinical_data/GSE115574.csv\"\n",
"json_path = \"../../output/preprocess/Arrhythmia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "aafbda3c",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
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"id": "44318892",
"metadata": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Gene expression data from human left and right atrial tissues in patients with degenerative MR in SR and AFib.\"\n",
"!Series_summary\t\"We aimed to compare the gene expression profiles of patients with degenerative MR in SR and AFib. We used Affymetrix human gene expression microarrays for each atrium sample. We chose most homogenous groups to compare, cause of the noise overshadows in high-throughput analysis when investigating complex diseases.\"\n",
"!Series_overall_design\t\"Left and right atrial tissue samples were obtained from patients with chronic primary severe MR in permanent AFib (n=15) and sinus rhythm (n=15). Transcriptomic analysis and bioinformatics have been done on all atrial tissues. Independent datasets from GEO were included in the analysis to confirm our findings. Real-time qPCR used to validate microarray results. Atrial tissues investigated via transmission electron microscopy for ultrasutructural changes.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['disease state: atrial fibrillation patient with severe mitral regurgitation', 'disease state: sinus rhythm patient with severe mitral regurgitation'], 1: ['tissue: left atrium - heart', 'tissue: right atrium - heart']}\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": "6fff6dfe",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "090657e8",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data preview:\n",
"{0: [1.0], 1: [nan]}\n",
"Clinical data saved to ../../output/preprocess/Arrhythmia/clinical_data/GSE115574.csv\n"
]
}
],
"source": [
"# 1. Evaluate gene expression data availability \n",
"# Based on background information, this dataset contains gene expression data from human atrial tissues\n",
"is_gene_available = True\n",
"\n",
"# 2. Identify variable availability and create data type conversion functions\n",
"\n",
"# 2.1 & 2.2 For trait (Arrhythmia - atrial fibrillation)\n",
"# From sample characteristics, we see the trait key is 0, with values related to atrial fibrillation vs sinus rhythm\n",
"trait_row = 0\n",
"\n",
"def convert_trait(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary: 1 for atrial fibrillation, 0 for sinus rhythm\n",
" if 'atrial fibrillation' in value.lower():\n",
" return 1\n",
" elif 'sinus rhythm' in value.lower():\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# 2.1 & 2.2 For age\n",
"# Age information is not provided in the sample characteristics\n",
"age_row = None\n",
"\n",
"def convert_age(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" try:\n",
" # Convert to float for continuous variable\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"# 2.1 & 2.2 For gender\n",
"# Gender information is not provided in the sample characteristics\n",
"gender_row = None\n",
"\n",
"def convert_gender(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" # Convert to binary: 0 for female, 1 for male\n",
" if value in ['female', 'f']:\n",
" return 0\n",
" elif value in ['male', 'm']:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Determine trait data availability and save metadata\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort info for initial filtering\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. Extract clinical features if trait data is available\n",
"if trait_row is not None:\n",
" # Create clinical data DataFrame from the sample characteristics dictionary\n",
" # The sample characteristics dictionary is from the previous step:\n",
" # {0: ['disease state: atrial fibrillation patient with severe mitral regurgitation', \n",
" # 'disease state: sinus rhythm patient with severe mitral regurgitation'], \n",
" # 1: ['tissue: left atrium - heart', 'tissue: right atrium - heart']}\n",
" \n",
" # First, create a DataFrame with the provided characteristics\n",
" sample_chars = {\n",
" 0: ['disease state: atrial fibrillation patient with severe mitral regurgitation', \n",
" 'disease state: sinus rhythm patient with severe mitral regurgitation'],\n",
" 1: ['tissue: left atrium - heart', 'tissue: right atrium - heart']\n",
" }\n",
" \n",
" # Create a dictionary to represent the clinical data\n",
" clinical_dict = {}\n",
" for key, values in sample_chars.items():\n",
" clinical_dict[key] = values\n",
" \n",
" # Create DataFrame from the dictionary\n",
" clinical_data = pd.DataFrame(clinical_dict)\n",
" \n",
" # Select clinical features using the library function\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 clinical dataframe\n",
" print(\"Clinical data preview:\")\n",
" print(preview_df(selected_clinical_df))\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 the clinical dataframe 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": "5afa4d92",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2bc1d5ba",
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"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Arrhythmia/GSE115574/GSE115574_series_matrix.txt.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape: (54675, 59)\n",
"First 20 gene/probe identifiers:\n",
"Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n",
" '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n",
" '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n",
" '1552263_at', '1552264_a_at', '1552266_at'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Get the SOFT and matrix file paths again \n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"Matrix file found: {matrix_file}\")\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" \n",
" # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "97671957",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
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"source": [
"# Observe gene identifiers from the previous step output\n",
"# These identifiers appear to be Affymetrix probe IDs (e.g., '1007_s_at', '1053_at')\n",
"# rather than standard human gene symbols (which would be like BRCA1, TP53, etc.)\n",
"# Affymetrix probe IDs typically need to be mapped to gene symbols for biological interpretation\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "050e102d",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
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"name": "stdout",
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"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', '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",
"\n",
"Analyzing SPOT_ID.1 column for gene symbols:\n",
"\n",
"Gene data ID prefix: 1007\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'ID' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'GB_ACC' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'Target Description' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'Representative Public ID' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'Gene Title' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'Gene Symbol' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'ENTREZ_GENE_ID' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'RefSeq Transcript ID' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'Gene Ontology Biological Process' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Checking for columns containing transcript or gene related terms:\n",
"Column 'Species Scientific Name' may contain gene-related information\n",
"Sample values: ['Homo sapiens', 'Homo sapiens', 'Homo sapiens']\n",
"Column 'Target Description' may contain gene-related information\n",
"Sample values: ['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\"]\n",
"Column 'Gene Title' may contain gene-related information\n",
"Sample values: ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\"]\n",
"Column 'Gene Symbol' may contain gene-related information\n",
"Sample values: ['DDR1 /// MIR4640', 'RFC2', 'HSPA6']\n",
"Column 'ENTREZ_GENE_ID' may contain gene-related information\n",
"Sample values: ['780 /// 100616237', '5982', '3310']\n",
"Column 'RefSeq Transcript ID' may contain gene-related information\n",
"Sample values: ['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']\n",
"Column 'Gene Ontology Biological Process' may contain gene-related information\n",
"Sample values: ['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']\n",
"Column 'Gene Ontology Cellular Component' may contain gene-related information\n",
"Sample values: ['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']\n",
"Column 'Gene Ontology Molecular Function' may contain gene-related information\n",
"Sample values: ['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"
]
}
],
"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. 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=5))\n",
"\n",
"# Check for gene information in the SPOT_ID.1 column which appears to contain gene names\n",
"print(\"\\nAnalyzing SPOT_ID.1 column for gene symbols:\")\n",
"if 'SPOT_ID.1' in gene_annotation.columns:\n",
" # Extract a few sample values\n",
" sample_values = gene_annotation['SPOT_ID.1'].head(3).tolist()\n",
" for i, value in enumerate(sample_values):\n",
" print(f\"Sample {i+1} excerpt: {value[:200]}...\") # Print first 200 chars\n",
" # Test the extract_human_gene_symbols function on these values\n",
" symbols = extract_human_gene_symbols(value)\n",
" print(f\" Extracted gene symbols: {symbols}\")\n",
"\n",
"# Try to find the probe IDs in the gene annotation\n",
"gene_data_id_prefix = gene_data.index[0].split('_')[0] # Get prefix of first gene ID\n",
"print(f\"\\nGene data ID prefix: {gene_data_id_prefix}\")\n",
"\n",
"# Look for columns that might match the gene data IDs\n",
"for col in gene_annotation.columns:\n",
" if gene_annotation[col].astype(str).str.contains(gene_data_id_prefix).any():\n",
" print(f\"Column '{col}' contains values matching gene data ID pattern\")\n",
"\n",
"# Check if there's any column that might contain transcript or gene IDs\n",
"print(\"\\nChecking for columns containing transcript or gene related terms:\")\n",
"for col in gene_annotation.columns:\n",
" if any(term in col.upper() for term in ['GENE', 'TRANSCRIPT', 'SYMBOL', 'NAME', 'DESCRIPTION']):\n",
" print(f\"Column '{col}' may contain gene-related information\")\n",
" # Show sample values\n",
" print(f\"Sample values: {gene_annotation[col].head(3).tolist()}\")\n"
]
},
{
"cell_type": "markdown",
"id": "d4ce03ce",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3cfa94cf",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:32:37.428722Z",
"iopub.status.busy": "2025-03-25T06:32:37.428603Z",
"iopub.status.idle": "2025-03-25T06:32:38.803319Z",
"shell.execute_reply": "2025-03-25T06:32:38.802803Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping dataframe shape: (45782, 2)\n",
"Gene mapping preview:\n",
"{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'Gene': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A']}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression dataframe shape after mapping: (21278, 59)\n",
"First few genes after mapping:\n",
"['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n",
"Gene expression dataframe shape after normalization: (19845, 59)\n",
"First few genes after normalization:\n",
"['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data saved to ../../output/preprocess/Arrhythmia/gene_data/GSE115574.csv\n"
]
}
],
"source": [
"# 1. Determine which columns in the gene annotation dataframe contain the gene IDs and gene symbols\n",
"# From the previous output, we can see:\n",
"# - 'ID' column contains the probe identifiers that match the gene expression data (e.g., '1007_s_at')\n",
"# - 'Gene Symbol' column contains the gene symbols (e.g., 'DDR1 /// MIR4640')\n",
"\n",
"# 2. Get gene mapping dataframe by extracting the ID and Gene Symbol columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
"print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
"print(\"Gene mapping preview:\")\n",
"print(preview_df(gene_mapping, n=5))\n",
"\n",
"# 3. Apply gene mapping to convert probe-level data to gene-level data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(f\"Gene expression dataframe shape after mapping: {gene_data.shape}\")\n",
"print(\"First few genes after mapping:\")\n",
"print(gene_data.index[:10].tolist())\n",
"\n",
"# 4. Normalize gene symbols to standard format\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene expression dataframe shape after normalization: {gene_data.shape}\")\n",
"print(\"First few genes after normalization:\")\n",
"print(gene_data.index[:10].tolist())\n",
"\n",
"# Save the gene expression data to CSV\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 data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "d1c865c8",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "86aa454d",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:32:38.804572Z",
"iopub.status.busy": "2025-03-25T06:32:38.804458Z",
"iopub.status.idle": "2025-03-25T06:32:50.280067Z",
"shell.execute_reply": "2025-03-25T06:32:50.279390Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape before normalization: (19845, 59)\n",
"Gene data shape after normalization: (19845, 59)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Arrhythmia/gene_data/GSE115574.csv\n",
"Original clinical data preview:\n",
" !Sample_geo_accession \\\n",
"0 !Sample_characteristics_ch1 \n",
"1 !Sample_characteristics_ch1 \n",
"\n",
" GSM3182680 \\\n",
"0 disease state: atrial fibrillation patient wit... \n",
"1 tissue: left atrium - heart \n",
"\n",
" GSM3182681 \\\n",
"0 disease state: atrial fibrillation patient wit... \n",
"1 tissue: right atrium - heart \n",
"\n",
" GSM3182682 \\\n",
"0 disease state: atrial fibrillation patient wit... \n",
"1 tissue: left atrium - heart \n",
"\n",
" GSM3182683 \\\n",
"0 disease state: atrial fibrillation patient wit... \n",
"1 tissue: right atrium - heart \n",
"\n",
" GSM3182684 \\\n",
"0 disease state: atrial fibrillation patient wit... \n",
"1 tissue: left atrium - heart \n",
"\n",
" GSM3182685 \\\n",
"0 disease state: atrial fibrillation patient wit... \n",
"1 tissue: right atrium - heart \n",
"\n",
" GSM3182686 \\\n",
"0 disease state: atrial fibrillation patient wit... \n",
"1 tissue: left atrium - heart \n",
"\n",
" GSM3182687 \\\n",
"0 disease state: atrial fibrillation patient wit... \n",
"1 tissue: right atrium - heart \n",
"\n",
" GSM3182688 ... \\\n",
"0 disease state: atrial fibrillation patient wit... ... \n",
"1 tissue: left atrium - heart ... \n",
"\n",
" GSM3182729 \\\n",
"0 disease state: sinus rhythm patient with sever... \n",
"1 tissue: right atrium - heart \n",
"\n",
" GSM3182730 \\\n",
"0 disease state: sinus rhythm patient with sever... \n",
"1 tissue: left atrium - heart \n",
"\n",
" GSM3182731 \\\n",
"0 disease state: sinus rhythm patient with sever... \n",
"1 tissue: right atrium - heart \n",
"\n",
" GSM3182732 \\\n",
"0 disease state: sinus rhythm patient with sever... \n",
"1 tissue: left atrium - heart \n",
"\n",
" GSM3182733 \\\n",
"0 disease state: sinus rhythm patient with sever... \n",
"1 tissue: right atrium - heart \n",
"\n",
" GSM3182734 \\\n",
"0 disease state: sinus rhythm patient with sever... \n",
"1 tissue: left atrium - heart \n",
"\n",
" GSM3182735 \\\n",
"0 disease state: sinus rhythm patient with sever... \n",
"1 tissue: right atrium - heart \n",
"\n",
" GSM3182736 \\\n",
"0 disease state: sinus rhythm patient with sever... \n",
"1 tissue: left atrium - heart \n",
"\n",
" GSM3182737 \\\n",
"0 disease state: sinus rhythm patient with sever... \n",
"1 tissue: right atrium - heart \n",
"\n",
" GSM3182738 \n",
"0 disease state: sinus rhythm patient with sever... \n",
"1 tissue: right atrium - heart \n",
"\n",
"[2 rows x 60 columns]\n",
"Selected clinical data shape: (1, 59)\n",
"Clinical data preview:\n",
" GSM3182680 GSM3182681 GSM3182682 GSM3182683 GSM3182684 \\\n",
"Arrhythmia 1.0 1.0 1.0 1.0 1.0 \n",
"\n",
" GSM3182685 GSM3182686 GSM3182687 GSM3182688 GSM3182689 ... \\\n",
"Arrhythmia 1.0 1.0 1.0 1.0 1.0 ... \n",
"\n",
" GSM3182729 GSM3182730 GSM3182731 GSM3182732 GSM3182733 \\\n",
"Arrhythmia 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" GSM3182734 GSM3182735 GSM3182736 GSM3182737 GSM3182738 \n",
"Arrhythmia 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
"[1 rows x 59 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape before processing: (59, 19846)\n",
"Linked data preview (first 5 rows, 5 columns):\n",
" Arrhythmia A1BG A1BG-AS1 A1CF A2M\n",
"GSM3182680 1.0 4.926930 3.103542 5.365683 13.884166\n",
"GSM3182681 1.0 4.932586 3.398962 5.590272 13.669718\n",
"GSM3182682 1.0 4.606200 3.103393 5.579943 13.626048\n",
"GSM3182683 1.0 5.171341 3.291102 5.255341 13.419765\n",
"GSM3182684 1.0 4.944955 3.197841 5.753891 13.931109\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (59, 19846)\n",
"For the feature 'Arrhythmia', the least common label is '1.0' with 28 occurrences. This represents 47.46% of the dataset.\n",
"The distribution of the feature 'Arrhythmia' in this dataset is fine.\n",
"\n",
"Data shape after removing biased features: (59, 19846)\n",
"A new JSON file was created at: ../../output/preprocess/Arrhythmia/cohort_info.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Arrhythmia/GSE115574.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"# Use normalize_gene_symbols_in_index to standardize gene symbols\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
"print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
"\n",
"# Save the normalized gene data to file\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
"\n",
"# Load the actual clinical data from the matrix file that was previously obtained in Step 1\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",
"# Get preview of clinical data to understand its structure\n",
"print(\"Original clinical data preview:\")\n",
"print(clinical_data.head())\n",
"\n",
"# 2. If we have trait data available, proceed with linking\n",
"if trait_row is not None:\n",
" # Extract clinical features using the original clinical data\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",
" print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
" print(\"Clinical data preview:\")\n",
" print(selected_clinical_df.head())\n",
"\n",
" # Link the clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
" print(f\"Linked data shape before processing: {linked_data.shape}\")\n",
" print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
" print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Empty dataframe\")\n",
"\n",
" # 3. Handle missing values\n",
" try:\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
" except Exception as e:\n",
" print(f\"Error handling missing values: {e}\")\n",
" linked_data = pd.DataFrame() # Create empty dataframe if error occurs\n",
"\n",
" # 4. Check for bias in features\n",
" if not linked_data.empty and linked_data.shape[0] > 0:\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" print(f\"Data shape after removing biased features: {linked_data.shape}\")\n",
" else:\n",
" is_biased = True\n",
" print(\"Cannot check for bias as dataframe is empty or has no rows after missing value handling\")\n",
"\n",
" # 5. Validate and save cohort information\n",
" note = \"\"\n",
" if linked_data.empty or linked_data.shape[0] == 0:\n",
" note = \"Dataset contains gene expression data related to atrial fibrillation after cardiac surgery, but linking clinical and genetic data failed, possibly due to mismatched sample IDs.\"\n",
" else:\n",
" note = \"Dataset contains gene expression data for atrial fibrillation after cardiac surgery, which is relevant to arrhythmia research.\"\n",
" \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_biased,\n",
" df=linked_data,\n",
" note=note\n",
" )\n",
"\n",
" # 6. Save the linked data if 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(\"Dataset is not usable for analysis. No linked data file saved.\")\n",
"else:\n",
" # If no trait data available, validate with trait_available=False\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,\n",
" is_biased=True, # Set to True since we can't use data without trait\n",
" df=pd.DataFrame(), # Empty DataFrame\n",
" note=\"Dataset contains gene expression data but lacks proper clinical trait information for arrhythmia analysis.\"\n",
" )\n",
" \n",
" print(\"Dataset is not usable for arrhythmia analysis due to lack of clinical trait data. No linked data file saved.\")"
]
}
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