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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "54681efd",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:01:53.543600Z",
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"shell.execute_reply": "2025-03-25T07:01:53.705828Z"
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"outputs": [],
"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 = \"Breast_Cancer\"\n",
"cohort = \"GSE225328\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Breast_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Breast_Cancer/GSE225328\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Breast_Cancer/GSE225328.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Breast_Cancer/gene_data/GSE225328.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Breast_Cancer/clinical_data/GSE225328.csv\"\n",
"json_path = \"../../output/preprocess/Breast_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "a38711d6",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "62e23e20",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:01:53.707710Z",
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"shell.execute_reply": "2025-03-25T07:01:53.734694Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Transcriptome profiling in early-stage luminal breast cancer\"\n",
"!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
"!Series_overall_design\t\"Refer to individual Series\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['disease: early-stage luminal breast cancer'], 1: ['Sex: female']}\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": "ad985c48",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "de5ab3c3",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:01:53.736344Z",
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"shell.execute_reply": "2025-03-25T07:01:53.750474Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of clinical features:\n",
"{'GSM7043537': [1.0, 0.0], 'GSM7043538': [1.0, 0.0], 'GSM7043539': [1.0, 0.0], 'GSM7043540': [1.0, 0.0], 'GSM7043541': [1.0, 0.0], 'GSM7043542': [1.0, 0.0], 'GSM7043543': [1.0, 0.0], 'GSM7043544': [1.0, 0.0], 'GSM7043545': [1.0, 0.0], 'GSM7043546': [1.0, 0.0], 'GSM7043547': [1.0, 0.0], 'GSM7043548': [1.0, 0.0], 'GSM7043549': [1.0, 0.0], 'GSM7043550': [1.0, 0.0], 'GSM7043551': [1.0, 0.0], 'GSM7043552': [1.0, 0.0], 'GSM7043553': [1.0, 0.0], 'GSM7043554': [1.0, 0.0], 'GSM7043555': [1.0, 0.0], 'GSM7043556': [1.0, 0.0], 'GSM7043557': [1.0, 0.0], 'GSM7043558': [1.0, 0.0], 'GSM7043559': [1.0, 0.0], 'GSM7043560': [1.0, 0.0], 'GSM7043561': [1.0, 0.0], 'GSM7043562': [1.0, 0.0], 'GSM7043563': [1.0, 0.0], 'GSM7043564': [1.0, 0.0], 'GSM7043565': [1.0, 0.0], 'GSM7043566': [1.0, 0.0], 'GSM7043567': [1.0, 0.0], 'GSM7043568': [1.0, 0.0], 'GSM7043569': [1.0, 0.0], 'GSM7043570': [1.0, 0.0], 'GSM7043571': [1.0, 0.0], 'GSM7043572': [1.0, 0.0], 'GSM7043573': [1.0, 0.0], 'GSM7043574': [1.0, 0.0], 'GSM7043575': [1.0, 0.0], 'GSM7043576': [1.0, 0.0], 'GSM7043577': [1.0, 0.0], 'GSM7043578': [1.0, 0.0], 'GSM7043579': [1.0, 0.0], 'GSM7043580': [1.0, 0.0], 'GSM7043581': [1.0, 0.0], 'GSM7043582': [1.0, 0.0], 'GSM7043583': [1.0, 0.0], 'GSM7043584': [1.0, 0.0], 'GSM7043585': [1.0, 0.0], 'GSM7043586': [1.0, 0.0], 'GSM7043587': [1.0, 0.0], 'GSM7043588': [1.0, 0.0], 'GSM7043589': [1.0, 0.0], 'GSM7043590': [1.0, 0.0], 'GSM7043591': [1.0, 0.0], 'GSM7043592': [1.0, 0.0], 'GSM7043593': [1.0, 0.0], 'GSM7043594': [1.0, 0.0], 'GSM7043595': [1.0, 0.0], 'GSM7043596': [1.0, 0.0], 'GSM7043597': [1.0, 0.0], 'GSM7043598': [1.0, 0.0], 'GSM7043599': [1.0, 0.0], 'GSM7043600': [1.0, 0.0], 'GSM7043601': [1.0, 0.0], 'GSM7043602': [1.0, 0.0], 'GSM7043603': [1.0, 0.0], 'GSM7043604': [1.0, 0.0], 'GSM7043605': [1.0, 0.0], 'GSM7043606': [1.0, 0.0], 'GSM7043607': [1.0, 0.0], 'GSM7043608': [1.0, 0.0], 'GSM7043609': [1.0, 0.0], 'GSM7043610': [1.0, 0.0], 'GSM7043611': [1.0, 0.0], 'GSM7043612': [1.0, 0.0], 'GSM7043613': [1.0, 0.0], 'GSM7043614': [1.0, 0.0], 'GSM7043615': [1.0, 0.0], 'GSM7043616': [1.0, 0.0], 'GSM7043617': [1.0, 0.0], 'GSM7043618': [1.0, 0.0], 'GSM7043619': [1.0, 0.0], 'GSM7043620': [1.0, 0.0], 'GSM7043621': [1.0, 0.0], 'GSM7043622': [1.0, 0.0], 'GSM7043623': [1.0, 0.0], 'GSM7043624': [1.0, 0.0], 'GSM7043625': [1.0, 0.0], 'GSM7043626': [1.0, 0.0], 'GSM7043627': [1.0, 0.0], 'GSM7043628': [1.0, 0.0], 'GSM7043629': [1.0, 0.0], 'GSM7043630': [1.0, 0.0], 'GSM7043631': [1.0, 0.0], 'GSM7043632': [1.0, 0.0], 'GSM7043633': [1.0, 0.0], 'GSM7043634': [1.0, 0.0], 'GSM7043635': [1.0, 0.0], 'GSM7043636': [1.0, 0.0], 'GSM7043637': [1.0, 0.0], 'GSM7043638': [1.0, 0.0], 'GSM7043639': [1.0, 0.0], 'GSM7043640': [1.0, 0.0], 'GSM7043641': [1.0, 0.0], 'GSM7043642': [1.0, 0.0], 'GSM7043643': [1.0, 0.0], 'GSM7043644': [1.0, 0.0], 'GSM7043645': [1.0, 0.0], 'GSM7043646': [1.0, 0.0], 'GSM7043647': [1.0, 0.0], 'GSM7043648': [1.0, 0.0], 'GSM7043649': [1.0, 0.0], 'GSM7043650': [1.0, 0.0], 'GSM7043651': [1.0, 0.0], 'GSM7043652': [1.0, 0.0], 'GSM7043653': [1.0, 0.0], 'GSM7043654': [1.0, 0.0], 'GSM7043655': [1.0, 0.0], 'GSM7043656': [1.0, 0.0], 'GSM7043657': [1.0, 0.0], 'GSM7043658': [1.0, 0.0], 'GSM7043659': [1.0, 0.0], 'GSM7043660': [1.0, 0.0], 'GSM7043661': [1.0, 0.0]}\n",
"Clinical features saved to ../../output/preprocess/Breast_Cancer/clinical_data/GSE225328.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# According to the background information, this is a transcriptome profiling study\n",
"# which typically means gene expression data is available\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# Looking at the Sample Characteristics Dictionary:\n",
"# Key 0 has \"disease: early-stage luminal breast cancer\" which is related to the trait (Breast Cancer)\n",
"# Key 1 has \"Sex: female\" which is gender information\n",
"# There is no age information available\n",
"\n",
"trait_row = 0 # Disease information is in row 0\n",
"age_row = None # Age information is not available\n",
"gender_row = 1 # Gender information is in row 1\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait values to binary format.\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Since all samples are \"early-stage luminal breast cancer\", \n",
" # all will be converted to 1 (indicating presence of breast cancer)\n",
" if \"breast cancer\" in value.lower():\n",
" return 1\n",
" else:\n",
" return None # For any unexpected values\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age values to continuous format.\"\"\"\n",
" # Age data is not available, but we include this function for completeness\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender values to binary format (0 for female, 1 for male).\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\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",
"\n",
"# Initial filtering and saving metadata\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_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the extracted clinical features\n",
" print(\"Preview of clinical features:\")\n",
" print(preview_df(clinical_features_df))\n",
" \n",
" # Save the clinical features as a CSV file\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "2e6e732c",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0b14f656",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:01:53.752701Z",
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"shell.execute_reply": "2025-03-25T07:01:53.790170Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOFT file: ../../input/GEO/Breast_Cancer/GSE225328/GSE225328_family.soft.gz\n",
"Matrix file: ../../input/GEO/Breast_Cancer/GSE225328/GSE225328-GPL18402_series_matrix.txt.gz\n",
"Found the matrix table marker at line 60\n",
"Gene data shape: (2006, 125)\n",
"First 20 gene/probe identifiers:\n",
"['hsa-let-7a-3p', 'hsa-let-7a-5p', 'hsa-let-7b-3p', 'hsa-let-7b-5p', 'hsa-let-7c', 'hsa-let-7d-3p', 'hsa-let-7d-5p', 'hsa-let-7e-3p', 'hsa-let-7e-5p', 'hsa-let-7f-1-3p', 'hsa-let-7f-2-3p', 'hsa-let-7f-5p', 'hsa-let-7g-3p', 'hsa-let-7g-5p', 'hsa-let-7i-3p', 'hsa-let-7i-5p', 'hsa-miR-1', 'hsa-miR-100-3p', 'hsa-miR-100-5p', 'hsa-miR-101-3p']\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 # Initially assume gene data is available\n",
"\n",
"# First check if the matrix file contains the expected marker\n",
"found_marker = False\n",
"marker_row = None\n",
"try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for i, line in enumerate(file):\n",
" if \"!series_matrix_table_begin\" in line:\n",
" found_marker = True\n",
" marker_row = i\n",
" print(f\"Found the matrix table marker at line {i}\")\n",
" break\n",
" \n",
" if not found_marker:\n",
" print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
" is_gene_available = False\n",
" \n",
" # If marker was found, try to extract gene data\n",
" if is_gene_available:\n",
" try:\n",
" # Try using the library function\n",
" gene_data = get_genetic_data(matrix_file)\n",
" \n",
" if gene_data.shape[0] == 0:\n",
" print(\"Warning: Extracted gene data has 0 rows.\")\n",
" is_gene_available = False\n",
" else:\n",
" print(f\"Gene data shape: {gene_data.shape}\")\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 with get_genetic_data(): {e}\")\n",
" is_gene_available = False\n",
" \n",
" # If gene data extraction failed, examine file content to diagnose\n",
" if not is_gene_available:\n",
" print(\"Examining file content to diagnose the issue:\")\n",
" try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Print lines around the marker if found\n",
" if marker_row is not None:\n",
" for i, line in enumerate(file):\n",
" if i >= marker_row - 2 and i <= marker_row + 10:\n",
" print(f\"Line {i}: {line.strip()[:100]}...\")\n",
" if i > marker_row + 10:\n",
" break\n",
" else:\n",
" # If marker not found, print first 10 lines\n",
" for i, line in enumerate(file):\n",
" if i < 10:\n",
" print(f\"Line {i}: {line.strip()[:100]}...\")\n",
" else:\n",
" break\n",
" except Exception as e2:\n",
" print(f\"Error examining file: {e2}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing file: {e}\")\n",
" is_gene_available = False\n",
"\n",
"# Update validation information if gene data extraction failed\n",
"if not is_gene_available:\n",
" print(\"Gene expression data could not be successfully extracted from this dataset.\")\n",
" # Update the validation record since gene data isn't available\n",
" is_trait_available = False # We already determined trait data isn't available in step 2\n",
" validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n",
" is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n"
]
},
{
"cell_type": "markdown",
"id": "bb4bf217",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0e4703c0",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:01:53.792136Z",
"iopub.status.busy": "2025-03-25T07:01:53.792027Z",
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"shell.execute_reply": "2025-03-25T07:01:53.793754Z"
}
},
"outputs": [],
"source": [
"# Based on the output from the previous step, I can see that the gene identifiers\n",
"# are miRNA identifiers (e.g., \"hsa-let-7a-3p\", \"hsa-miR-1\", etc.)\n",
"# These are proper standard miRNA names for human miRNAs (hsa prefix = Homo sapiens)\n",
"# They are not gene symbols (like BRCA1, TP53) and would need to be mapped if we want\n",
"# to convert to standard gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "48d28a60",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "fcc938b0",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:01:53.795600Z",
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"iopub.status.idle": "2025-03-25T07:01:54.048608Z",
"shell.execute_reply": "2025-03-25T07:01:54.048080Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"Columns in gene annotation: ['ID', 'miRNA_ID', 'ACCESSION_STRING', 'CONTROL_TYPE', 'SPOT_ID', 'SPOT_ID.1']\n",
"{'ID': ['hsa-let-7a-3p', 'hsa-let-7a-5p', 'hsa-let-7b-3p'], 'miRNA_ID': ['hsa-let-7a-3p', 'hsa-let-7a-5p', 'hsa-let-7b-3p'], 'ACCESSION_STRING': ['mir|hsa-let-7a-3p|mir|MIMAT0004481|mir|hsa-let-7a*_v17.0|mir|MIMAT0004481', 'mir|hsa-let-7a-5p|mir|MIMAT0000062|mir|hsa-let-7a_v17.0|mir|MIMAT0000062', 'mir|hsa-let-7b-3p|mir|MIMAT0004482|mir|hsa-let-7b*_v17.0|mir|MIMAT0004482'], 'CONTROL_TYPE': [False, False, False], 'SPOT_ID': [nan, nan, nan], 'SPOT_ID.1': [nan, nan, nan]}\n",
"\n",
"Examining ID and ORF columns format (first 3 rows):\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=3))\n",
"\n",
"# Looking at the output, it appears the gene symbols are in the 'ORF' column\n",
"# and the probe IDs are in the 'ID' column\n",
"print(\"\\nExamining ID and ORF columns format (first 3 rows):\")\n",
"if 'ID' in gene_annotation.columns and 'ORF' in gene_annotation.columns:\n",
" for i in range(min(3, len(gene_annotation))):\n",
" print(f\"Row {i}: ID={gene_annotation['ID'].iloc[i]}, ORF={gene_annotation['ORF'].iloc[i]}\")\n",
"\n",
" # Check the quality and completeness of the mapping\n",
" non_null_symbols = gene_annotation['ORF'].notna().sum()\n",
" total_rows = len(gene_annotation)\n",
" print(f\"\\nORF column completeness: {non_null_symbols}/{total_rows} rows ({non_null_symbols/total_rows:.2%})\")\n"
]
},
{
"cell_type": "markdown",
"id": "57976e5b",
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"source": [
"### Step 6: Gene Identifier Mapping"
]
},
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{
"name": "stdout",
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"text": [
"\n",
"Gene expression data sample (first 5 rows, 3 columns):\n",
" GSM7043537 GSM7043538 GSM7043539\n",
"ID \n",
"hsa-let-7a-3p -2.842319 0.656993 -3.119569\n",
"hsa-let-7a-5p 12.236782 11.668952 12.020029\n",
"hsa-let-7b-3p 3.586698 -3.123689 0.167606\n",
"hsa-let-7b-5p 12.979993 12.667322 12.236782\n",
"hsa-let-7c 11.518827 10.673440 10.153807\n",
"\n",
"This dataset contains miRNA expression data, not standard gene expression data.\n",
"miRNAs are small non-coding RNAs that regulate gene expression but are not genes themselves.\n",
"For the purpose of gene-trait association studies, we require standard gene expression data.\n",
"\n",
"Dataset marked as not containing suitable gene expression data for our analysis.\n"
]
}
],
"source": [
"# Based on the exploration of columns, we can confirm this is miRNA data, not gene expression data\n",
"# The identifiers are miRNA IDs (e.g., hsa-let-7a-3p) which don't map to standard gene symbols\n",
"\n",
"# Let's examine a sample of the gene expression data and annotation to confirm\n",
"gene_expression_data = get_genetic_data(matrix_file)\n",
"print(\"\\nGene expression data sample (first 5 rows, 3 columns):\")\n",
"sample_cols = gene_expression_data.columns[:3].tolist()\n",
"print(gene_expression_data.iloc[:5, :3])\n",
"\n",
"# Update our gene availability flag since this isn't standard gene expression data\n",
"is_gene_available = False\n",
"print(\"\\nThis dataset contains miRNA expression data, not standard gene expression data.\")\n",
"print(\"miRNAs are small non-coding RNAs that regulate gene expression but are not genes themselves.\")\n",
"print(\"For the purpose of gene-trait association studies, we require standard gene expression data.\")\n",
"\n",
"# Save the updated metadata to reflect that this dataset isn't suitable\n",
"is_trait_available = True # We confirmed trait data is available in earlier steps\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",
" note=\"Dataset contains miRNA expression data instead of gene expression data.\"\n",
")\n",
"\n",
"print(\"\\nDataset marked as not containing suitable gene expression data for our analysis.\")"
]
}
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