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{
"cells": [
{
"cell_type": "code",
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"execution": {
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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 = \"Chronic_kidney_disease\"\n",
"cohort = \"GSE45980\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Chronic_kidney_disease\"\n",
"in_cohort_dir = \"../../input/GEO/Chronic_kidney_disease/GSE45980\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Chronic_kidney_disease/GSE45980.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Chronic_kidney_disease/gene_data/GSE45980.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Chronic_kidney_disease/clinical_data/GSE45980.csv\"\n",
"json_path = \"../../output/preprocess/Chronic_kidney_disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "4788314a",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7b9710f4",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"An integrative analysis of renal miRNA- and mRNA-expression signatures in progressive chronic kidney disease [discovery cohort]\"\n",
"!Series_summary\t\"MicroRNAs (miRNAs) significantly contribute to chronic kidney disease (CKD) progression via regulating mRNA expression and abundance. However, their association with clinical outcome remains poorly understood. We performed large scale miRNA and mRNA expression profiling on cryo-cut renal biopsy sections from n=43 subjects. miRNAs differentiating stable and progressive cases were determined, and putative target mRNAs showing inversely correlated expression profiles were identified and further characterized. We found a downregulation of 7 miRNAs in the progressive phenotype, and an upregulation of 29 target mRNAs which are involved in inflammatory response, cell-cell-interaction, apoptosis, and intracellular signaling. Particularly a diminished expression of miR-206 in progressive disease correlated significantly with the upregulation of the target mRNAs CCL19, CXCL1, IFNAR2, NCK2, PTK2B, PTPRC, RASGRP1, and TNFRSF25, all participating in inflammatory pathways. Progressive cases also showed a decreased expression of miR-532-3p, and an increased expression of target transcripts MAP3K14, TNFRSF10B/TRAIL-R2, TRADD, and TRAF2, all being involved in apoptosis pathways. miR-206, miR-532-3p and all 12 mRNA targets correlated with the degree of histological damage. \"\n",
"!Series_summary\t\"The identified renal miRNA- and mRNA-profiles, and biological pathways may represent regulatory mechanisms, which are commonly present in various kinds of progressive chronic kidney disease.\"\n",
"!Series_overall_design\t\"mRNA- and miRNA-profiling was performed on renal biopsy samples from human subjects with various proteinuric nephropathies, miRNA-mRNA correlations were identified for those subjects who showed a progressive decline of renal function during follow up.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['gender: male', 'gender: female'], 1: ['age (yrs): 72', 'age (yrs): 20', 'age (yrs): 64', 'age (yrs): 17', 'age (yrs): 46', 'age (yrs): 55', 'age (yrs): 74', 'age (yrs): 49', 'age (yrs): 42', 'age (yrs): 73', 'age (yrs): 63', 'age (yrs): 33', 'age (yrs): 24', 'age (yrs): 45', 'age (yrs): 70', 'age (yrs): 60', 'age (yrs): 67', 'age (yrs): 31', 'age (yrs): 53', 'age (yrs): 22', 'age (yrs): 54', 'age (yrs): 40', 'age (yrs): 38', 'age (yrs): 19', 'age (yrs): 28', 'age (yrs): 65', 'age (yrs): 58', 'age (yrs): 56', 'age (yrs): 34', 'age (yrs): 59'], 2: ['diagnosis: Diabetic Nephropathy', 'diagnosis: Focal-Segmental Glomerulosclerosis', 'diagnosis: Hypertensive Nephropathy', 'diagnosis: IgA-Nephropathy', 'diagnosis: Membranous Nephropathy', 'diagnosis: Minimal-Change Disease', 'diagnosis: Other/Unknown'], 3: ['clinical course: stable', 'clinical course: progressive']}\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": "c36c0824",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "373844ab",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:19:00.983792Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of extracted clinical features:\n",
"{'GSM1121040': [0.0, 72.0, 1.0], 'GSM1121041': [0.0, 20.0, 0.0], 'GSM1121042': [0.0, 64.0, 0.0], 'GSM1121043': [0.0, 17.0, 1.0], 'GSM1121044': [0.0, 46.0, 1.0], 'GSM1121045': [0.0, 55.0, 1.0], 'GSM1121046': [0.0, 74.0, 1.0], 'GSM1121047': [0.0, 49.0, 1.0], 'GSM1121048': [0.0, 20.0, 1.0], 'GSM1121049': [0.0, 42.0, 1.0], 'GSM1121050': [0.0, 73.0, 0.0], 'GSM1121051': [0.0, 63.0, 0.0], 'GSM1121052': [0.0, 33.0, 0.0], 'GSM1121053': [0.0, 74.0, 1.0], 'GSM1121054': [0.0, 24.0, 1.0], 'GSM1121055': [0.0, 45.0, 1.0], 'GSM1121056': [0.0, 70.0, 1.0], 'GSM1121057': [0.0, 60.0, 1.0], 'GSM1121058': [0.0, 67.0, 0.0], 'GSM1121059': [0.0, 31.0, 0.0], 'GSM1121060': [0.0, 53.0, 0.0], 'GSM1121061': [0.0, 67.0, 0.0], 'GSM1121062': [0.0, 22.0, 0.0], 'GSM1121063': [0.0, 54.0, 0.0], 'GSM1121064': [0.0, 40.0, 1.0], 'GSM1121065': [0.0, 38.0, 0.0], 'GSM1121066': [0.0, 19.0, 1.0], 'GSM1121067': [0.0, 28.0, 0.0], 'GSM1121068': [0.0, 65.0, 1.0], 'GSM1121069': [0.0, 74.0, 1.0], 'GSM1121070': [0.0, 65.0, 1.0], 'GSM1121071': [1.0, 54.0, 1.0], 'GSM1121072': [1.0, 58.0, 1.0], 'GSM1121073': [1.0, 56.0, 1.0], 'GSM1121074': [1.0, 34.0, 1.0], 'GSM1121075': [1.0, 31.0, 1.0], 'GSM1121076': [1.0, 64.0, 0.0], 'GSM1121077': [1.0, 59.0, 1.0], 'GSM1121078': [1.0, 70.0, 1.0], 'GSM1121079': [1.0, 58.0, 1.0], 'GSM1121080': [1.0, 67.0, 0.0], 'GSM1121081': [1.0, 54.0, 1.0], 'GSM1121082': [1.0, 61.0, 1.0]}\n",
"Clinical features saved to ../../output/preprocess/Chronic_kidney_disease/clinical_data/GSE45980.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains mRNA expression profiling\n",
"# which is suitable for our study\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Data Availability\n",
"# For trait: The key is 3, which refers to \"clinical course: stable/progressive\"\n",
"# For age: The key is 1, which contains \"age (yrs): X\"\n",
"# For gender: The key is 0, which contains \"gender: male/female\"\n",
"trait_row = 3\n",
"age_row = 1\n",
"gender_row = 0\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait (clinical course) to binary.\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" value = value.lower()\n",
" if \"clinical course:\" in value:\n",
" value = value.split(\"clinical course:\")[1].strip()\n",
" \n",
" if \"progressive\" in value:\n",
" return 1 # Progressive CKD\n",
" elif \"stable\" in value:\n",
" return 0 # Stable CKD\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous numeric value.\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" try:\n",
" if \"age (yrs):\" in value:\n",
" age_str = value.split(\"age (yrs):\")[1].strip()\n",
" return float(age_str)\n",
" return None\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary (0 for female, 1 for male).\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" value = value.lower()\n",
" if \"gender:\" in value:\n",
" value = value.split(\"gender:\")[1].strip()\n",
" \n",
" if \"female\" in value:\n",
" return 0\n",
" elif \"male\" in value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\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",
"if trait_row is not None:\n",
" # Extract clinical features\n",
" clinical_features = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the extracted clinical features\n",
" print(\"Preview of extracted clinical features:\")\n",
" print(preview_df(clinical_features))\n",
" \n",
" # Save the clinical features to a CSV file\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "5ae47aa3",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f251d609",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOFT file: ../../input/GEO/Chronic_kidney_disease/GSE45980/GSE45980_family.soft.gz\n",
"Matrix file: ../../input/GEO/Chronic_kidney_disease/GSE45980/GSE45980_series_matrix.txt.gz\n",
"Gene data shape: (9665, 43)\n",
"First 20 gene/probe identifiers:\n",
"['A_23_P100001', 'A_23_P100240', 'A_23_P100315', 'A_23_P100326', 'A_23_P100355', 'A_23_P100392', 'A_23_P100486', 'A_23_P100501', 'A_23_P100660', 'A_23_P100704', 'A_23_P100764', 'A_23_P100963', 'A_23_P101111', 'A_23_P101332', 'A_23_P10135', 'A_23_P101407', 'A_23_P101480', 'A_23_P101516', 'A_23_P101532', 'A_23_P101551']\n"
]
}
],
"source": [
"# 1. Identify the paths to the SOFT file and the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"SOFT file: {soft_file}\")\n",
"print(f\"Matrix file: {matrix_file}\")\n",
"\n",
"# Set gene availability flag\n",
"is_gene_available = True # Assume gene data is available\n",
"\n",
"# Extract gene data\n",
"try:\n",
" # Extract gene data from the matrix file\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" \n",
" # Print the first 20 gene/probe identifiers\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20].tolist())\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n",
" print(f\"File path: {matrix_file}\")\n",
" print(\"Please check if the file exists and contains the expected markers.\")\n"
]
},
{
"cell_type": "markdown",
"id": "0a0cd049",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "17781454",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# The identifiers like 'A_23_P100001' are probe IDs from Agilent microarrays\n",
"# They are not human gene symbols and require mapping to gene symbols\n",
"# These are specific probe identifiers used in Agilent microarray platforms\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "d25dca25",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e0dc13a8",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"Columns in gene annotation: ['ID', 'SPOT_ID', 'CONTROL_TYPE', 'REFSEQ', 'GB_ACC', 'GENE', 'GENE_SYMBOL', 'GENE_NAME', 'UNIGENE_ID', 'ENSEMBL_ID', 'TIGR_ID', 'ACCESSION_STRING', 'CHROMOSOMAL_LOCATION', 'CYTOBAND', 'DESCRIPTION', 'GO_ID', 'SEQUENCE']\n",
"{'ID': ['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107', '(+)E1A_r60_a135'], 'SPOT_ID': ['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107', '(+)E1A_r60_a135'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'pos', 'pos'], 'REFSEQ': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan], 'GENE': [nan, nan, nan, nan, nan], 'GENE_SYMBOL': [nan, nan, nan, nan, nan], 'GENE_NAME': [nan, nan, nan, nan, nan], 'UNIGENE_ID': [nan, nan, nan, nan, nan], 'ENSEMBL_ID': [nan, nan, nan, nan, nan], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': [nan, nan, nan, nan, nan], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, nan, nan], 'CYTOBAND': [nan, nan, nan, nan, nan], 'DESCRIPTION': [nan, nan, nan, nan, nan], 'GO_ID': [nan, nan, nan, nan, nan], 'SEQUENCE': [nan, nan, nan, nan, nan]}\n",
"\n",
"Examining potential gene mapping columns:\n",
"\n",
"Sample mappings from 'ID' to 'GENE_SYMBOL':\n",
" ID GENE_SYMBOL\n",
"0 (+)E1A_r60_1 NaN\n",
"1 (+)E1A_r60_3 NaN\n",
"2 (+)E1A_r60_a104 NaN\n",
"3 (+)E1A_r60_a107 NaN\n",
"4 (+)E1A_r60_a135 NaN\n",
"5 (+)E1A_r60_a20 NaN\n",
"6 (+)E1A_r60_a22 NaN\n",
"7 (+)E1A_r60_a97 NaN\n",
"8 (+)E1A_r60_n11 NaN\n",
"9 (+)E1A_r60_n9 NaN\n",
"\n",
"Number of probes with gene symbol mappings: 29833\n",
"Sample of valid mappings:\n",
" ID GENE_SYMBOL\n",
"11 A_23_P100001 FAM174B\n",
"12 A_23_P100022 SV2B\n",
"13 A_23_P100056 RBPMS2\n",
"14 A_23_P100074 AVEN\n",
"15 A_23_P100127 CASC5\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
"print(\"\\nGene annotation preview:\")\n",
"print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
"print(preview_df(gene_annotation, n=5))\n",
"\n",
"# Look more closely at columns that might contain gene information\n",
"print(\"\\nExamining potential gene mapping columns:\")\n",
"# Based on the output, 'ID' and 'GENE_SYMBOL' are likely the mapping columns we need\n",
"gene_id_col = 'ID'\n",
"gene_symbol_col = 'GENE_SYMBOL'\n",
"\n",
"if gene_id_col in gene_annotation.columns and gene_symbol_col in gene_annotation.columns:\n",
" print(f\"\\nSample mappings from '{gene_id_col}' to '{gene_symbol_col}':\")\n",
" sample_mappings = gene_annotation[[gene_id_col, gene_symbol_col]].head(10)\n",
" print(sample_mappings)\n",
" \n",
" # Check for non-null mappings to confirm data quality\n",
" non_null_mappings = gene_annotation[[gene_id_col, gene_symbol_col]].dropna(subset=[gene_symbol_col])\n",
" print(f\"\\nNumber of probes with gene symbol mappings: {len(non_null_mappings)}\")\n",
" print(f\"Sample of valid mappings:\")\n",
" print(non_null_mappings.head(5))\n",
"else:\n",
" print(\"Required mapping columns not found in the annotation data.\")\n"
]
},
{
"cell_type": "markdown",
"id": "9eacf94f",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
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"name": "stdout",
"output_type": "stream",
"text": [
"Using mapping from 'ID' to 'GENE_SYMBOL'\n",
"Gene mapping dataframe shape: (29833, 2)\n",
"Sample of gene mapping:\n",
" ID Gene\n",
"11 A_23_P100001 FAM174B\n",
"12 A_23_P100022 SV2B\n",
"13 A_23_P100056 RBPMS2\n",
"14 A_23_P100074 AVEN\n",
"15 A_23_P100127 CASC5\n",
"Gene expression data shape after mapping: (8182, 43)\n",
"First 5 gene symbols after mapping:\n",
"['A1BG', 'A4GALT', 'AAAS', 'AACS', 'AADACL3']\n",
"\n",
"Preview of gene expression data after mapping:\n",
" GSM1121040 GSM1121041 GSM1121042 GSM1121043 GSM1121044\n",
"Gene \n",
"A1BG 5.819 6.883 5.939 5.627 5.884\n",
"A4GALT 10.260 9.439 9.304 9.026 8.972\n",
"AAAS 6.096 6.338 6.158 5.887 5.926\n",
"AACS 6.395 6.298 6.431 6.227 5.974\n",
"AADACL3 6.082 6.453 5.749 5.782 6.057\n"
]
}
],
"source": [
"# 1. Determine the columns for gene identifier and gene symbol mapping\n",
"probe_id_col = 'ID' # This contains identifiers like 'A_23_P100001' matching the gene expression data\n",
"gene_symbol_col = 'GENE_SYMBOL' # This contains the gene symbols we need\n",
"\n",
"print(f\"Using mapping from '{probe_id_col}' to '{gene_symbol_col}'\")\n",
"\n",
"# 2. Extract the gene mapping dataframe with the two relevant columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col)\n",
"print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
"print(\"Sample of gene mapping:\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\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].tolist())\n",
"\n",
"# Show the first few values for a few genes to verify the data\n",
"print(\"\\nPreview of gene expression data after mapping:\")\n",
"print(gene_data.iloc[:5, :5])\n"
]
},
{
"cell_type": "markdown",
"id": "154dd42c",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
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"id": "5b5252e1",
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"iopub.status.idle": "2025-03-25T08:19:04.343051Z",
"shell.execute_reply": "2025-03-25T08:19:04.342664Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Normalizing gene symbols...\n",
"Gene data shape after normalization: (8116, 43)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Chronic_kidney_disease/gene_data/GSE45980.csv\n",
"Loaded clinical data from ../../output/preprocess/Chronic_kidney_disease/clinical_data/GSE45980.csv\n",
"Clinical data shape: (3, 43)\n",
"\n",
"Linking clinical and genetic data...\n",
"Linked data shape: (43, 8119)\n",
"Linked data preview (first 5 rows, first 5 columns):\n",
" 0 1 2 A1BG A4GALT\n",
"GSM1121040 0.0 72.0 1.0 5.819 10.260\n",
"GSM1121041 0.0 20.0 0.0 6.883 9.439\n",
"GSM1121042 0.0 64.0 0.0 5.939 9.304\n",
"GSM1121043 0.0 17.0 1.0 5.627 9.026\n",
"GSM1121044 0.0 46.0 1.0 5.884 8.972\n",
"Linked data after renaming columns:\n",
" Chronic_kidney_disease Age Gender A1BG A4GALT\n",
"GSM1121040 0.0 72.0 1.0 5.819 10.260\n",
"GSM1121041 0.0 20.0 0.0 6.883 9.439\n",
"GSM1121042 0.0 64.0 0.0 5.939 9.304\n",
"GSM1121043 0.0 17.0 1.0 5.627 9.026\n",
"GSM1121044 0.0 46.0 1.0 5.884 8.972\n",
"\n",
"Handling missing values...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (43, 8119)\n",
"\n",
"Checking for bias in trait and demographic features...\n",
"For the feature 'Chronic_kidney_disease', the least common label is '1.0' with 12 occurrences. This represents 27.91% of the dataset.\n",
"The distribution of the feature 'Chronic_kidney_disease' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 36.0\n",
" 50% (Median): 55.0\n",
" 75%: 65.0\n",
"Min: 17.0\n",
"Max: 74.0\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '0.0' with 15 occurrences. This represents 34.88% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"\n",
"Conducting final quality validation...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Chronic_kidney_disease/GSE45980.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"print(\"\\nNormalizing gene symbols...\")\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
"\n",
"# Save the normalized gene data\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\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Load clinical data from previously saved file\n",
"try:\n",
" # Try to load the clinical data from the file saved in Step 2\n",
" selected_clinical_df = pd.read_csv(out_clinical_data_file)\n",
" print(f\"Loaded clinical data from {out_clinical_data_file}\")\n",
"except FileNotFoundError:\n",
" # If not available, re-extract it using the correct row indices from Step 2\n",
" background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
" trait_row = 3 # Correct row index from Step 2\n",
" age_row = 1\n",
" gender_row = 0\n",
" \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",
" # Save the clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data extracted and saved to {out_clinical_data_file}\")\n",
"\n",
"print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
"\n",
"# 3. Link the clinical and genetic data\n",
"print(\"\\nLinking clinical and genetic data...\")\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(\"Linked data preview (first 5 rows, first 5 columns):\")\n",
"print(linked_data.iloc[:5, :5])\n",
"\n",
"# Rename the clinical columns to match expected names\n",
"linked_data = linked_data.rename(columns={0: trait, 1: \"Age\", 2: \"Gender\"})\n",
"print(\"Linked data after renaming columns:\")\n",
"print(linked_data.iloc[:5, :5])\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"print(\"\\nHandling missing values...\")\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 5. Determine if the trait and demographic features are biased\n",
"print(\"\\nChecking for bias in trait and demographic features...\")\n",
"is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 6. Conduct final quality validation and save relevant information\n",
"print(\"\\nConducting final quality validation...\")\n",
"is_gene_available = True # We've confirmed gene data is available in previous steps\n",
"is_trait_available = True # We've confirmed trait data is available in previous steps\n",
"\n",
"note = \"This dataset contains gene expression data from kidney biopsies. It classifies samples based on clinical course (stable or progressive chronic kidney disease).\"\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=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=note\n",
")\n",
"\n",
"# 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.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Linked data not saved as dataset is not usable for the current trait study.\")"
]
}
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