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{
"cells": [
{
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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 = \"Bladder_Cancer\"\n",
"cohort = \"GSE222073\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Bladder_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Bladder_Cancer/GSE222073\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Bladder_Cancer/GSE222073.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Bladder_Cancer/gene_data/GSE222073.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Bladder_Cancer/clinical_data/GSE222073.csv\"\n",
"json_path = \"../../output/preprocess/Bladder_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "408fde1c",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bf1b0f8b",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Patterns of metastasis and recurrence in urothelial cancer molecular subtypes\"\n",
"!Series_summary\t\"This series contains the gene expression data from urothelial bladder cancer samples from Swedish patients that were used to analyze metastatic sites. Included patients have a recurrence or distant metastasis before or after treatment with chemotherapy. Patients with only lymph-node metastases are not included. A previous series (GSE169455) contains data from all patients that recieved two or more cycles of neoadjuvant chemotherapy with curative intent. Patients in that series that developed distant recurrence are re-analyzed here. A few samples from a previous cystectomy series (GSE83586) are also included as re-analysis. In addition, the current series contains data from patients treated with palliative first-line chemotherapy, curative adjuvant chemotherapy, or < 2 cycles of neoadjuvant chemotherapy.\"\n",
"!Series_summary\t\"Raw data should be adjusted in data processing for batch variables: Labeling batch and Labeling kit.\"\n",
"!Series_overall_design\t\"Retrospective cohort study aiming to study metastatic sites and chemotherapy response in muscle-invasive bladder cancer.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['labeling kit: SensationPlus FFPE Amplification and WT labeling kit', 'labeling kit: GeneChip WT Pico kit'], 1: ['labeling batch: 3', 'labeling batch: 4', 'labeling batch: 5', 'labeling batch: 6', 'labeling batch: 7', 'labeling batch: 8', 'labeling batch: 9', 'labeling batch: 10', 'labeling batch: 11', 'labeling batch: 13', 'labeling batch: 14', 'labeling batch: 15', 'labeling batch: 16', 'labeling batch: 17', 'labeling batch: 18', 'labeling batch: 19', 'labeling batch: 20', 'labeling batch: 21', 'labeling batch: 22', 'labeling batch: 23', 'labeling batch: 24', 'labeling batch: 25', 'labeling batch: 26', 'labeling batch: 27'], 2: ['clinical tnm staging: cTxN0M1', 'clinical tnm staging: cT3N0M0', 'clinical tnm staging: pT4aN1M0', 'clinical tnm staging: cT2N0M0', 'clinical tnm staging: cT4bN0M0', 'clinical tnm staging: cTxN2M1', 'clinical tnm staging: cTxN3M1', 'clinical tnm staging: cT3bN0M0', 'clinical tnm staging: cTxNxM1', 'clinical tnm staging: cT2N2M0', 'clinical tnm staging: CT3bN0M0', 'clinical tnm staging: cT4bN1M0', 'clinical tnm staging: pT3bN2M0', 'clinical tnm staging: cT1N3M1', 'clinical tnm staging: cT3N1M0', 'clinical tnm staging: cT4aN0M0', 'clinical tnm staging: cT4bN2M0', 'clinical tnm staging: cT4N0M0', 'clinical tnm staging: cT1N0M1', 'clinical tnm staging: cT2N0M1', 'clinical tnm staging: cT2N1M0', 'clinical tnm staging: cT3bN0M1', 'clinical tnm staging: cT3N1M1', 'clinical tnm staging: pT1N2M0', 'clinical tnm staging: pT4aN2M0', 'clinical tnm staging: cT3N2M1', 'clinical tnm staging: cT3aN2M0', 'clinical tnm staging: cT2N3M1', 'clinical tnm staging: pT2N2M0', 'clinical tnm staging: cT2N2M1'], 3: ['chemotherapy type: palliative', 'chemotherapy type: neoadjuvant', 'chemotherapy type: adjuvant', 'chemotherapy type: induction', 'chemotherapy type: curative radiochemotherapy', 'chemotherapy type: induction + radiotherapy'], 4: ['lundtax rna-subtype: UroC', 'lundtax rna-subtype: GU', 'lundtax rna-subtype: UroB', 'lundtax rna-subtype: UroA', 'lundtax rna-subtype: ScNE', 'lundtax rna-subtype: BASQ', 'lundtax rna-subtype: Mes'], 5: ['lundtax ihc-subtype: Uro', 'lundtax ihc-subtype: GU', 'lundtax ihc-subtype: BASQ', 'lundtax ihc-subtype: ScNE', 'lundtax ihc-subtype: Mes'], 6: ['consensus classifier subtype: LumNS', 'consensus classifier subtype: LumU', 'consensus classifier subtype: BASQ', 'consensus classifier subtype: StromaRich', 'consensus classifier subtype: LumP', 'consensus classifier subtype: NE_like'], 7: ['rm-lymphnode: no', 'rm-lymphnode: yes'], 8: ['rm-local: no', 'rm-local: yes'], 9: ['rm-lung: no', 'rm-lung: yes'], 10: ['rm-liver: no', 'rm-liver: yes'], 11: ['rm-bone: yes', 'rm-bone: no'], 12: ['rm-other: no', 'rm-other: yes']}\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": "caacbf57",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "61f0d6f2",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data file not found at ../../input/GEO/Bladder_Cancer/GSE222073/clinical_data.csv\n",
"Skipping clinical feature extraction.\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# From the background information, it appears this dataset contains gene expression data for urothelial bladder cancer\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# For trait (Bladder Cancer)\n",
"# From the provided sample characteristics, we can use bone metastasis information as our trait\n",
"trait_row = 11 # Key 11 contains 'rm-bone: yes/no' data\n",
"\n",
"# Age is not explicitly mentioned in the sample characteristics\n",
"age_row = None \n",
"\n",
"# Gender is not explicitly mentioned in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"# For trait (bone metastasis in bladder cancer)\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 yes, 0 for no)\n",
" if value.lower() == 'yes':\n",
" return 1\n",
" elif value.lower() == 'no':\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# Age conversion function (not used as age is not available)\n",
"def convert_age(value):\n",
" return None\n",
"\n",
"# Gender conversion function (not used as gender is not available)\n",
"def convert_gender(value):\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine if trait data is available\n",
"is_trait_available = trait_row is not None\n",
"# Initial filtering on usability\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",
" try:\n",
" # Check if the clinical data file exists\n",
" if os.path.exists(f\"{in_cohort_dir}/clinical_data.csv\"):\n",
" # Load the clinical data and extract features\n",
" clinical_data = pd.read_csv(f\"{in_cohort_dir}/clinical_data.csv\")\n",
" \n",
" # Use the library function to extract features\n",
" selected_clinical = 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 features\n",
" print(preview_df(selected_clinical))\n",
" \n",
" # Save the clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" else:\n",
" print(f\"Clinical data file not found at {in_cohort_dir}/clinical_data.csv\")\n",
" print(\"Skipping clinical feature extraction.\")\n",
" except Exception as e:\n",
" print(f\"Error processing clinical data: {e}\")\n",
" is_trait_available = False\n"
]
},
{
"cell_type": "markdown",
"id": "4f106830",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "766f954b",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['1-Mar', '2-Mar', '3-Mar', '4-Mar', '5-Mar', '6-Mar', '7-Mar', 'A2M',\n",
" 'A2ML1', 'A4GALT', 'A4GNT', 'AAAS', 'AACS', 'AADAT', 'AAGAB', 'AAK1',\n",
" 'AAMDC', 'AAMP', 'AANAT', 'AAR2'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "b9310392",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ff9e60c8",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:57:29.522077Z",
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"shell.execute_reply": "2025-03-25T06:57:29.523869Z"
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},
"outputs": [],
"source": [
"# Examining the gene identifiers in the expression data\n",
"\n",
"# Based on the sample of gene identifiers shown, I observe:\n",
"# - Many entries like \"A2M\", \"AAAS\", \"AAMP\" which appear to be standard HGNC gene symbols\n",
"# - Some unusual entries like \"1-Mar\", \"2-Mar\" etc. which are not standard gene symbols \n",
"# (these are likely MARCH family genes that have been incorrectly formatted)\n",
"\n",
"# Since most identifiers appear to be gene symbols already but with some formatting issues,\n",
"# I'll recommend mapping to ensure consistency\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "c966be05",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "98aace6e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:57:29.525732Z",
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"shell.execute_reply": "2025-03-25T06:57:31.657934Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['LOC100287497', 'SAMD11', 'KLHL17', 'PLEKHN1', 'ISG15'], 'ORF': ['LOC100287497', 'SAMD11', 'KLHL17', 'PLEKHN1', 'ISG15']}\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "1ba0d055",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ca948e83",
"metadata": {
"execution": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mapped gene data preview (first 5 genes):\n",
"Index(['A2M', 'A2ML1', 'A4GALT', 'A4GNT', 'AAAS'], dtype='object', name='Gene')\n",
"Total number of genes after mapping: 13409\n"
]
}
],
"source": [
"# 1. After observing the data, it seems that:\n",
"# - The gene expression data uses gene symbols directly as identifiers (e.g., A2M, AAAS)\n",
"# - The gene annotation data has 'ID' and 'ORF' columns which both contain gene identifiers\n",
"\n",
"# Since the gene annotation preview data shows symbols like 'SAMD11', 'KLHL17', etc.\n",
"# which are standard gene symbols, I'll use 'ID' as both the probe column and the gene column\n",
"# for consistent mapping\n",
"\n",
"# 2. Get a gene mapping dataframe\n",
"mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n",
"gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
"\n",
"# Preview the mapped gene data\n",
"print(\"Mapped gene data preview (first 5 genes):\")\n",
"print(gene_data.index[:5])\n",
"print(f\"Total number of genes after mapping: {len(gene_data)}\")\n"
]
},
{
"cell_type": "markdown",
"id": "8ff31fc0",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8fbe72eb",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:57:44.798822Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original gene count: 13409\n",
"Normalized gene count: 13362\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Bladder_Cancer/gene_data/GSE222073.csv\n",
"Clinical data structure:\n",
"(13, 147)\n",
"First few rows of clinical data:\n",
" !Sample_geo_accession \\\n",
"0 !Sample_characteristics_ch1 \n",
"1 !Sample_characteristics_ch1 \n",
"2 !Sample_characteristics_ch1 \n",
"3 !Sample_characteristics_ch1 \n",
"4 !Sample_characteristics_ch1 \n",
"\n",
" GSM6914278 \\\n",
"0 labeling kit: SensationPlus FFPE Amplification... \n",
"1 labeling batch: 3 \n",
"2 clinical tnm staging: cTxN0M1 \n",
"3 chemotherapy type: palliative \n",
"4 lundtax rna-subtype: UroC \n",
"\n",
" GSM6914279 \\\n",
"0 labeling kit: SensationPlus FFPE Amplification... \n",
"1 labeling batch: 4 \n",
"2 clinical tnm staging: cT3N0M0 \n",
"3 chemotherapy type: neoadjuvant \n",
"4 lundtax rna-subtype: GU \n",
"\n",
" GSM6914280 \\\n",
"0 labeling kit: SensationPlus FFPE Amplification... \n",
"1 labeling batch: 4 \n",
"2 clinical tnm staging: pT4aN1M0 \n",
"3 chemotherapy type: adjuvant \n",
"4 lundtax rna-subtype: GU \n",
"\n",
" GSM6914281 \\\n",
"0 labeling kit: SensationPlus FFPE Amplification... \n",
"1 labeling batch: 5 \n",
"2 clinical tnm staging: cT3N0M0 \n",
"3 chemotherapy type: neoadjuvant \n",
"4 lundtax rna-subtype: UroB \n",
"\n",
" GSM6914282 \\\n",
"0 labeling kit: SensationPlus FFPE Amplification... \n",
"1 labeling batch: 6 \n",
"2 clinical tnm staging: cT2N0M0 \n",
"3 chemotherapy type: neoadjuvant \n",
"4 lundtax rna-subtype: GU \n",
"\n",
" GSM6914283 \\\n",
"0 labeling kit: SensationPlus FFPE Amplification... \n",
"1 labeling batch: 6 \n",
"2 clinical tnm staging: cT4bN0M0 \n",
"3 chemotherapy type: induction \n",
"4 lundtax rna-subtype: GU \n",
"\n",
" GSM6914284 \\\n",
"0 labeling kit: SensationPlus FFPE Amplification... \n",
"1 labeling batch: 7 \n",
"2 clinical tnm staging: cTxN2M1 \n",
"3 chemotherapy type: palliative \n",
"4 lundtax rna-subtype: UroA \n",
"\n",
" GSM6914285 \\\n",
"0 labeling kit: SensationPlus FFPE Amplification... \n",
"1 labeling batch: 8 \n",
"2 clinical tnm staging: cT2N0M0 \n",
"3 chemotherapy type: neoadjuvant \n",
"4 lundtax rna-subtype: UroA \n",
"\n",
" GSM6914286 ... \\\n",
"0 labeling kit: SensationPlus FFPE Amplification... ... \n",
"1 labeling batch: 8 ... \n",
"2 clinical tnm staging: cTxN0M1 ... \n",
"3 chemotherapy type: palliative ... \n",
"4 lundtax rna-subtype: GU ... \n",
"\n",
" GSM6914414 GSM6914415 \\\n",
"0 labeling kit: GeneChip WT Pico kit labeling kit: GeneChip WT Pico kit \n",
"1 labeling batch: 25 labeling batch: 25 \n",
"2 clinical tnm staging: cT3bN0M1 clinical tnm staging: cT4N2M1 \n",
"3 chemotherapy type: palliative chemotherapy type: palliative \n",
"4 lundtax rna-subtype: Mes lundtax rna-subtype: BASQ \n",
"\n",
" GSM6914416 GSM6914417 \\\n",
"0 labeling kit: GeneChip WT Pico kit labeling kit: GeneChip WT Pico kit \n",
"1 labeling batch: 25 labeling batch: 26 \n",
"2 clinical tnm staging: cT4aN0M1 clinical tnm staging: cTxN0M1 \n",
"3 chemotherapy type: palliative chemotherapy type: palliative \n",
"4 lundtax rna-subtype: UroA lundtax rna-subtype: UroA \n",
"\n",
" GSM6914418 GSM6914419 \\\n",
"0 labeling kit: GeneChip WT Pico kit labeling kit: GeneChip WT Pico kit \n",
"1 labeling batch: 26 labeling batch: 27 \n",
"2 clinical tnm staging: cT3bN0M0 clinical tnm staging: pT3bN1M0 \n",
"3 chemotherapy type: neoadjuvant chemotherapy type: adjuvant \n",
"4 lundtax rna-subtype: UroB lundtax rna-subtype: GU \n",
"\n",
" GSM6914420 GSM6914421 \\\n",
"0 labeling kit: GeneChip WT Pico kit labeling kit: GeneChip WT Pico kit \n",
"1 labeling batch: 27 labeling batch: 27 \n",
"2 clinical tnm staging: cTxN0M1 clinical tnm staging: cTxN3M1 \n",
"3 chemotherapy type: palliative chemotherapy type: palliative \n",
"4 lundtax rna-subtype: BASQ lundtax rna-subtype: UroB \n",
"\n",
" GSM6914422 GSM6914423 \n",
"0 labeling kit: GeneChip WT Pico kit labeling kit: GeneChip WT Pico kit \n",
"1 labeling batch: 27 labeling batch: 27 \n",
"2 clinical tnm staging: pT2aN1M0 clinical tnm staging: cT3N1M1 \n",
"3 chemotherapy type: adjuvant chemotherapy type: palliative \n",
"4 lundtax rna-subtype: UroC lundtax rna-subtype: BASQ \n",
"\n",
"[5 rows x 147 columns]\n",
"Clinical data shape after extraction: (1, 146)\n",
"First few sample IDs in clinical data:\n",
"['GSM6914278', 'GSM6914279', 'GSM6914280', 'GSM6914281', 'GSM6914282']\n",
"First few sample IDs in gene data:\n",
"['GSM6914278', 'GSM6914279', 'GSM6914280', 'GSM6914281', 'GSM6914282']\n",
"Number of common samples between clinical and gene data: 146\n",
"Clinical data saved to ../../output/preprocess/Bladder_Cancer/clinical_data/GSE222073.csv\n",
"Linked data shape: (146, 13363)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (146, 13363)\n",
"For the feature 'Bladder_Cancer', the least common label is '1.0' with 53 occurrences. This represents 36.30% of the dataset.\n",
"The distribution of the feature 'Bladder_Cancer' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Bladder_Cancer/GSE222073.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"# First, normalize gene symbols using the function from the library\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Original gene count: {len(gene_data)}\")\n",
"print(f\"Normalized gene count: {len(normalized_gene_data)}\")\n",
"\n",
"# Create directory for the gene data file if it doesn't exist\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"\n",
"# Save the normalized gene data to a CSV file\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Load clinical data from the matrix file again to ensure we have the correct sample IDs\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",
"print(\"Clinical data structure:\")\n",
"print(clinical_data.shape)\n",
"print(\"First few rows of clinical data:\")\n",
"print(clinical_data.head())\n",
"\n",
"# Extract clinical features with the correct sample IDs\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\"Clinical data shape after extraction: {selected_clinical_df.shape}\")\n",
"print(\"First few sample IDs in clinical data:\")\n",
"print(list(selected_clinical_df.columns)[:5])\n",
"print(\"First few sample IDs in gene data:\")\n",
"print(list(normalized_gene_data.columns)[:5])\n",
"\n",
"# Check for column overlap\n",
"common_samples = set(selected_clinical_df.columns).intersection(set(normalized_gene_data.columns))\n",
"print(f\"Number of common samples between clinical and gene data: {len(common_samples)}\")\n",
"\n",
"# Save the clinical data for inspection\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# Check if linking was successful\n",
"if len(linked_data) == 0 or trait not in linked_data.columns:\n",
" print(\"Linking clinical and genetic data failed - no valid rows or trait column missing\")\n",
" \n",
" # Check what columns are in the linked data\n",
" if len(linked_data.columns) > 0:\n",
" print(\"Columns in linked data:\")\n",
" print(list(linked_data.columns)[:10]) # Print first 10 columns\n",
" \n",
" # Set is_usable to False and save cohort info\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=True, \n",
" is_biased=True, # Consider it biased if linking fails\n",
" df=pd.DataFrame({trait: [], 'Gender': []}), \n",
" note=\"Data linking failed - unable to match sample IDs between clinical and gene expression data.\"\n",
" )\n",
" print(\"The dataset was determined to be not usable for analysis.\")\n",
"else:\n",
" # 3. Handle missing values in the linked data\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" \n",
" print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" # 4. Determine whether the trait and demographic features are severely biased\n",
" is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" \n",
" # 5. Conduct quality check and save the cohort information.\n",
" note = \"Dataset contains gene expression data from bladder cancer samples with molecular subtyping information.\"\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=True, \n",
" is_biased=is_trait_biased, \n",
" df=linked_data, \n",
" note=note\n",
" )\n",
" \n",
" # 6. If the linked data is usable, save it as a CSV file.\n",
" if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(\"The dataset was determined to be not usable for analysis due to bias in the trait distribution.\")"
]
}
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