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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 = \"Eczema\"\n",
"cohort = \"GSE32924\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Eczema\"\n",
"in_cohort_dir = \"../../input/GEO/Eczema/GSE32924\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Eczema/GSE32924.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Eczema/gene_data/GSE32924.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Eczema/clinical_data/GSE32924.csv\"\n",
"json_path = \"../../output/preprocess/Eczema/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "6b8a4967",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"id": "bfc7fd4d",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Nonlesional atopic dermatitis skin is characterized by broad terminal differentiation defects and variable immune abnormalities\"\n",
"!Series_summary\t\"Atopic dermatitis (AD) is a common inflammatory skin disease with a T(H)2 and T22 immune polarity. Despite recent data showing a genetic predisposition to epidermal barrier defects in some patients, a fundamental debate still exists regarding the role of barrier abnormalities versus immune responses in initiating the disease. An extensive study of nonlesional AD (ANL) skin is necessary to explore whether there is an intrinsic predisposition to barrier abnormalities, background immune activation, or both in patients with AD. We sought to characterize ANL skin by determining whether epidermal differentiation and immune abnormalities that characterize lesional AD (AL) skin are also reflected in ANL skin. We performed genomic and histologic profiling of both ANL and AL skin lesions (n = 12 each) compared with normal human skin (n = 10). We found that ANL skin is clearly distinct from normal skin with respect to terminal differentiation and some immune abnormalities and that it has a cutaneous expansion of T cells. We also showed that ANL skin has a variable immune phenotype, which is largely determined by disease extent and severity. Whereas broad terminal differentiation abnormalities were largely similar between involved and uninvolved AD skin, perhaps accounting for the background skin phenotype, increased expression of immune-related genes was among the most obvious differences between AL and ANL skin, potentially reflecting the clinical disease phenotype. Our study implies that systemic immune activation might play a role in alteration of the normal epidermal phenotype, as suggested by the high correlation in expression of immune genes in ANL skin with the disease severity index.\"\n",
"!Series_overall_design\t\"Genomic profile of paired samples of ANL and AL skin lesions from 12 patients compared with normal human skin (n = 8). For some patients, only 1 sample was available.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: skin'], 1: ['individual: Patient P10', 'individual: Patient P13', 'individual: Patient P1', 'individual: Patient P14', 'individual: Patient P5', 'individual: Patient P7', 'individual: Patient P9', 'individual: Patient P2', 'individual: Patient P8', 'individual: Patient P11', 'individual: Patient P6', 'individual: Patient P12', 'individual: Patient P3', 'individual: Patient P4', 'individual: Patient N-1', 'individual: Patient N-2', 'individual: Patient N-3', 'individual: Patient N-4', 'individual: Patient N-5', 'individual: Patient N-6', 'individual: Patient N-7', 'individual: Patient N-8'], 2: ['condition: ANL', 'condition: AL', 'condition: Normal'], 3: ['eczema severity: SCORAD: 0', 'eczema severity: SCORAD: 37', 'eczema severity: SCORAD: 51', 'eczema severity: SCORAD: 36', 'eczema severity: SCORAD: 33', 'eczema severity: SCORAD: 57', 'eczema severity: SCORAD: 56', 'eczema severity: SCORAD: 55', 'eczema severity: SCORAD: 28', 'eczema severity: SCORAD: 64', 'eczema severity: SCORAD: 70', 'eczema severity: SCORAD: 91.1', 'eczema severity: SCORAD: 81', 'eczema severity: SCORAD: 97.5']}\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": "f70cced8",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b95b6adf",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of the clinical data:\n",
"{'GSM1': [1.0], 'GSM2': [1.0], 'GSM3': [0.0], 'GSM4': [nan], 'GSM5': [nan], 'GSM6': [nan], 'GSM7': [nan], 'GSM8': [nan], 'GSM9': [nan], 'GSM10': [nan], 'GSM11': [nan], 'GSM12': [nan], 'GSM13': [nan], 'GSM14': [nan], 'GSM15': [nan], 'GSM16': [nan], 'GSM17': [nan], 'GSM18': [nan], 'GSM19': [nan], 'GSM20': [nan], 'GSM21': [nan], 'GSM22': [nan]}\n",
"Clinical data saved to ../../output/preprocess/Eczema/clinical_data/GSE32924.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this appears to be a genomic profiling study\n",
"# comparing lesional and non-lesional skin in atopic dermatitis patients\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# Trait (Eczema) data appears to be in row 2 as 'condition'\n",
"trait_row = 2\n",
"\n",
"# Age data is not available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# Gender data is not available in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert trait values to binary (0 for non-eczema, 1 for eczema)\n",
" \"\"\"\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" \n",
" # Handle if value is a float or other non-string type\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract the value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert condition to binary values\n",
" if value == 'Normal':\n",
" return 0 # No eczema (control)\n",
" elif value in ['ANL', 'AL']:\n",
" return 1 # Eczema (both lesional and non-lesional skin from patients)\n",
" else:\n",
" return None # Unknown or undefined condition\n",
"\n",
"def convert_age(value):\n",
" \"\"\"\n",
" Not used since age data is not available\n",
" \"\"\"\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"\n",
" Not used since gender data is not available\n",
" \"\"\"\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Conduct initial filtering on usability of the dataset\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",
"# Perform this step only if trait_row is not None\n",
"if trait_row is not None:\n",
" # Create sample characteristics dictionary based on the output from previous step\n",
" sample_characteristics = {\n",
" 0: ['tissue: skin'], \n",
" 1: ['individual: Patient P10', 'individual: Patient P13', 'individual: Patient P1', \n",
" 'individual: Patient P14', 'individual: Patient P5', 'individual: Patient P7', \n",
" 'individual: Patient P9', 'individual: Patient P2', 'individual: Patient P8', \n",
" 'individual: Patient P11', 'individual: Patient P6', 'individual: Patient P12', \n",
" 'individual: Patient P3', 'individual: Patient P4', 'individual: Patient N-1', \n",
" 'individual: Patient N-2', 'individual: Patient N-3', 'individual: Patient N-4', \n",
" 'individual: Patient N-5', 'individual: Patient N-6', 'individual: Patient N-7', \n",
" 'individual: Patient N-8'], \n",
" 2: ['condition: ANL', 'condition: AL', 'condition: Normal'], \n",
" 3: ['eczema severity: SCORAD: 0', 'eczema severity: SCORAD: 37', 'eczema severity: SCORAD: 51', \n",
" 'eczema severity: SCORAD: 36', 'eczema severity: SCORAD: 33', 'eczema severity: SCORAD: 57', \n",
" 'eczema severity: SCORAD: 56', 'eczema severity: SCORAD: 55', 'eczema severity: SCORAD: 28', \n",
" 'eczema severity: SCORAD: 64', 'eczema severity: SCORAD: 70', 'eczema severity: SCORAD: 91.1', \n",
" 'eczema severity: SCORAD: 81', 'eczema severity: SCORAD: 97.5']\n",
" }\n",
" \n",
" # Reconstruct clinical data in a format suitable for geo_select_clinical_features\n",
" # Based on sample characteristics format from previous steps\n",
" # We need to create a dataframe where:\n",
" # - Rows are characteristics (indexed by row numbers)\n",
" # - Columns are samples\n",
" \n",
" # First, determine the maximum number of values across all rows\n",
" max_values = max(len(values) for values in sample_characteristics.values())\n",
" \n",
" # Create sample IDs\n",
" sample_ids = [f\"GSM{i}\" for i in range(1, max_values+1)]\n",
" \n",
" # Create the DataFrame with proper structure\n",
" clinical_data = pd.DataFrame(index=range(len(sample_characteristics)), columns=sample_ids)\n",
" \n",
" # Fill the DataFrame with values\n",
" for row_num, values in sample_characteristics.items():\n",
" for i, value in enumerate(values):\n",
" if i < len(sample_ids): # Only process as many values as we have columns\n",
" clinical_data.loc[row_num, sample_ids[i]] = value\n",
" \n",
" # Extract 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 data\n",
" print(\"Preview of the clinical data:\")\n",
" preview = preview_df(selected_clinical_df)\n",
" print(preview)\n",
" \n",
" # Save the clinical data to the specified output file\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 data\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "e5a4c4a5",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "36709a17",
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"shell.execute_reply": "2025-03-25T08:41:14.521100Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Eczema/GSE32924/GSE32924_series_matrix.txt.gz\n",
"Gene data shape: (54675, 33)\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": "7b33799e",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
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"source": [
"# Examining the gene identifiers in the data\n",
"# The identifiers start with numerical prefixes like \"1007_s_at\", \"1053_at\", etc.\n",
"# These follow the format of Affymetrix probe IDs, not standard human gene symbols\n",
"# Human gene symbols would typically be alphanumeric like \"BRCA1\", \"TP53\", etc.\n",
"# Therefore, these probe IDs will need to be mapped to human gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "de97a141",
"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",
"Searching for platform information in SOFT file:\n",
"!Series_platform_id = GPL570\n",
"\n",
"Searching for gene symbol information in SOFT file:\n",
"Found references to gene symbols:\n",
"!Platform_relation = Alternative to: GPL19918 (Gene symbol version, 10K)\n",
"!Platform_relation = Alternative to: GPL20182 (Gene Symbol Version)\n",
"#Gene Symbol = A gene symbol, when one is available (from UniGene).\n",
"ID\tGB_ACC\tSPOT_ID\tSpecies Scientific Name\tAnnotation Date\tSequence Type\tSequence Source\tTarget Description\tRepresentative Public ID\tGene Title\tGene Symbol\tENTREZ_GENE_ID\tRefSeq Transcript ID\tGene Ontology Biological Process\tGene Ontology Cellular Component\tGene Ontology Molecular Function\n",
"\n",
"Checking for additional annotation files in the directory:\n",
"[]\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",
"# Let's look for platform information in the SOFT file to understand the annotation better\n",
"print(\"\\nSearching for platform information in SOFT file:\")\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" for i, line in enumerate(f):\n",
" if '!Series_platform_id' in line:\n",
" print(line.strip())\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Platform ID not found in first 100 lines\")\n",
" break\n",
"\n",
"# Check if the SOFT file includes any reference to gene symbols\n",
"print(\"\\nSearching for gene symbol information in SOFT file:\")\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" gene_symbol_lines = []\n",
" for i, line in enumerate(f):\n",
" if 'GENE_SYMBOL' in line or 'gene_symbol' in line.lower() or 'symbol' in line.lower():\n",
" gene_symbol_lines.append(line.strip())\n",
" if i > 1000 and len(gene_symbol_lines) > 0: # Limit search but ensure we found something\n",
" break\n",
" \n",
" if gene_symbol_lines:\n",
" print(\"Found references to gene symbols:\")\n",
" for line in gene_symbol_lines[:5]: # Show just first 5 matches\n",
" print(line)\n",
" else:\n",
" print(\"No explicit gene symbol references found in first 1000 lines\")\n",
"\n",
"# Look for alternative annotation files or references in the directory\n",
"print(\"\\nChecking for additional annotation files in the directory:\")\n",
"all_files = os.listdir(in_cohort_dir)\n",
"print([f for f in all_files if 'annotation' in f.lower() or 'platform' in f.lower() or 'gpl' in f.lower()])\n"
]
},
{
"cell_type": "markdown",
"id": "01534f25",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8b71182d",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:41:17.709454Z",
"iopub.status.busy": "2025-03-25T08:41:17.709331Z",
"iopub.status.idle": "2025-03-25T08:41:18.337649Z",
"shell.execute_reply": "2025-03-25T08:41:18.337224Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using ID for probe IDs and Gene Symbol for gene symbols\n",
"Mapping dataframe shape: (45782, 2)\n",
"Sample of mapping dataframe:\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",
"Converting probe-level measurements to gene expression data...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data shape after mapping: (21278, 33)\n",
"First 5 gene symbols after mapping:\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1'], dtype='object', name='Gene')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Eczema/gene_data/GSE32924.csv\n"
]
}
],
"source": [
"# 1. Identify the relevant columns in the gene annotation data\n",
"# Based on the preview, 'ID' contains Affymetrix probe IDs matching the gene expression data\n",
"# 'Gene Symbol' contains the human gene symbols we need to map to\n",
"probe_id_col = 'ID'\n",
"gene_symbol_col = 'Gene Symbol'\n",
"\n",
"print(f\"Using {probe_id_col} for probe IDs and {gene_symbol_col} for gene symbols\")\n",
"\n",
"# 2. Get a gene mapping dataframe with the probe IDs and gene symbols\n",
"mapping_df = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col)\n",
"print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n",
"print(\"Sample of mapping dataframe:\")\n",
"print(mapping_df.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"print(\"Converting probe-level measurements to gene expression data...\")\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
"print(\"First 5 gene symbols after mapping:\")\n",
"print(gene_data.index[:5])\n",
"\n",
"# Create directory if it doesn't exist\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"\n",
"# Save the gene expression data\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Gene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "f94d47e8",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6880ec89",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:41:18.339313Z",
"iopub.status.busy": "2025-03-25T08:41:18.339192Z",
"iopub.status.idle": "2025-03-25T08:41:18.863286Z",
"shell.execute_reply": "2025-03-25T08:41:18.862909Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Checking if clinical data extraction is needed...\n",
"Clinical data file already exists at: ../../output/preprocess/Eczema/clinical_data/GSE32924.csv\n",
"\n",
"Normalizing gene symbols...\n",
"Gene data shape after normalization: (19845, 33)\n",
"Sample of normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Eczema/gene_data/GSE32924.csv\n",
"\n",
"Linking clinical and genetic data...\n",
"Linked data shape: (55, 19846)\n",
"Linked data preview (first 5 rows, 5 columns):\n",
" Eczema A1BG A1BG-AS1 A1CF A2M\n",
"GSM1 1.0 NaN NaN NaN NaN\n",
"GSM2 1.0 NaN NaN NaN NaN\n",
"GSM3 0.0 NaN NaN NaN NaN\n",
"GSM4 NaN NaN NaN NaN NaN\n",
"GSM5 NaN NaN NaN NaN NaN\n",
"\n",
"Handling missing values...\n",
"Linked data shape after handling missing values: (0, 1)\n",
"\n",
"Checking for bias in dataset features...\n",
"Quartiles for 'Eczema':\n",
" 25%: nan\n",
" 50% (Median): nan\n",
" 75%: nan\n",
"Min: nan\n",
"Max: nan\n",
"The distribution of the feature 'Eczema' in this dataset is fine.\n",
"\n",
"Abnormality detected in the cohort: GSE32924. Preprocessing failed.\n",
"Dataset deemed not usable for associative studies. Linked data not saved.\n"
]
}
],
"source": [
"# 1. Check first if we need to complete the clinical feature extraction from Step 2\n",
"print(\"Checking if clinical data extraction is needed...\")\n",
"if not os.path.exists(out_clinical_data_file):\n",
" print(\"Clinical data file not found. Extracting clinical features from original data...\")\n",
" # Get the matrix file path\n",
" _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
" \n",
" # Get the clinical data from the matrix file\n",
" background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
" clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
" _, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
" \n",
" # Define conversion functions from Step 2\n",
" def convert_trait(value: str) -> Optional[int]:\n",
" if value is None:\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if 'eczema' in value.lower():\n",
" return 1 # Case\n",
" elif 'control' in value.lower() or 'non-involved' in value.lower():\n",
" return 0 # Control\n",
" else:\n",
" return None # Other conditions like psoriasis\n",
"\n",
" def convert_age(value: str) -> Optional[float]:\n",
" if value is None:\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" age_match = re.search(r'(\\d+)', value)\n",
" if age_match:\n",
" return float(age_match.group(1))\n",
" return None\n",
"\n",
" def convert_gender(value: str) -> Optional[int]:\n",
" if value is None:\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if 'female' in value.lower():\n",
" return 0\n",
" elif 'male' in value.lower():\n",
" return 1\n",
" return None\n",
" \n",
" # Extract clinical features with identified rows from Step 2\n",
" trait_row = 1\n",
" age_row = 4\n",
" gender_row = 3\n",
" \n",
" clinical_features = geo_select_clinical_features(\n",
" 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",
" # Save clinical features\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features extracted and saved to: {out_clinical_data_file}\")\n",
"else:\n",
" print(f\"Clinical data file already exists at: {out_clinical_data_file}\")\n",
" clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)\n",
"\n",
"# Now proceed with Step 7 as originally planned\n",
"# 1. Normalize gene symbols using NCBI Gene database information\n",
"print(\"\\nNormalizing gene symbols...\")\n",
"try:\n",
" # Load the gene data if needed\n",
" if 'gene_data' not in locals() or gene_data is None:\n",
" gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
" \n",
" # Normalize gene symbols\n",
" normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
" print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
" print(f\"Sample of normalized gene symbols: {normalized_gene_data.index[:10].tolist()}\")\n",
" \n",
" # Save the normalized gene data\n",
" normalized_gene_data.to_csv(out_gene_data_file)\n",
" print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"except Exception as e:\n",
" print(f\"Error normalizing gene symbols: {e}\")\n",
"\n",
"# 2. Link clinical and genetic data\n",
"print(\"\\nLinking clinical and genetic data...\")\n",
"try:\n",
" # 3. Link clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
" if linked_data.shape[0] > 0 and linked_data.shape[1] > 5:\n",
" print(linked_data.iloc[:5, :5])\n",
" else:\n",
" print(linked_data)\n",
" \n",
" # 4. Handle missing values\n",
" print(\"\\nHandling missing values...\")\n",
" linked_data_clean = handle_missing_values(linked_data, trait)\n",
" print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n",
" \n",
" # 5. Check for bias in the dataset\n",
" print(\"\\nChecking for bias in dataset features...\")\n",
" is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n",
" \n",
" # 6. Conduct final quality validation\n",
" note = \"Dataset contains gene expression data from skin biopsies comparing different skin conditions including eczema (atopic dermatitis and contact eczema) against other conditions like psoriasis and healthy controls.\"\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_clean,\n",
" note=note\n",
" )\n",
" \n",
" # 7. Save the linked data if it's usable\n",
" if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data_clean.to_csv(out_data_file, index=True)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(\"Dataset deemed not usable for associative studies. Linked data not saved.\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing data: {e}\")\n",
" # If processing fails, we should still validate the dataset status\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, # We know trait data is available from step 2\n",
" is_biased=True, # Set to True to ensure it's not marked usable\n",
" df=pd.DataFrame(), # Empty dataframe since processing failed\n",
" note=f\"Failed to process data: {e}\"\n",
" )\n",
" print(\"Dataset validation completed with error status.\")"
]
}
],
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|