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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 = \"Alopecia\"\n",
"cohort = \"GSE18876\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Alopecia\"\n",
"in_cohort_dir = \"../../input/GEO/Alopecia/GSE18876\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Alopecia/GSE18876.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Alopecia/gene_data/GSE18876.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Alopecia/clinical_data/GSE18876.csv\"\n",
"json_path = \"../../output/preprocess/Alopecia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "7ec98190",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"execution_count": 2,
"id": "21f44c67",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Transcriptional Profile of Aging in Healthy Human Skin\"\n",
"!Series_summary\t\"Gene expression changes were assessed from the non sun-exposed skin of the lower back of 98 healthy males aged 19-86. We show that contrary to previous thought, genome wide transcriptional activity does not display an exclusively linear correlation with ageing, but rather, in human skin, undergoes a period of significant transient change between 30 and 45 years of age. The identified transient transcriptional changes suggest a period of heightened metabolic activity and cellular damage mediated primarily through the actions of TP53 (tumour protein 53) and TNF (tumour necrosis factor). We also identified a subgroup of the population characterised by increased expression of a large group of hair follicle genes that correlates strongly with a younger age of onset and increasing severity of androgenetic alopecia.\"\n",
"!Series_overall_design\t\"Skin was collected from the lower back at the level of the belt, aproximately 5cm lateral to midline from healthy males, (defined as; non-smoking, no hospital admissions in the previous 5 years, no significant medical conditions or medications). Each sample was individually hybridised to an exon 1.0 ST array.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['age: 19', 'age: 20', 'age: 21', 'age: 22', 'age: 23', 'age: 24', 'age: 25', 'age: 26', 'age: 27', 'age: 30', 'age: 31', 'age: 33', 'age: 34', 'age: 36', 'age: 38', 'age: 39', 'age: 41', 'age: 42', 'age: 43', 'age: 44', 'age: 45', 'age: 47', 'age: 49', 'age: 50', 'age: 51', 'age: 52', 'age: 53', 'age: 54', 'age: 55', 'age: 57'], 1: ['tissue: skin']}\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": "1d63f3b0",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8f27f0ea",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# 1. Gene Expression Data Availability \n",
"# Based on the background information, this dataset contains transcriptional profiles from skin samples\n",
"# hybridized to exon arrays, which indicates gene expression data is available\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# Age is available in row 0\n",
"age_row = 0\n",
"\n",
"# Gender is not explicitly mentioned, but the background information states \"healthy males\" only,\n",
"# so all subjects are male (constant). Therefore gender data is not useful for our analysis.\n",
"gender_row = None\n",
"\n",
"# For trait (Alopecia), there's no direct mention in the sample characteristics,\n",
"# but the background information mentions a \"subgroup of the population characterised by... androgenetic alopecia\"\n",
"# However, we don't have this information in the sample characteristics dictionary\n",
"trait_row = None \n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_age(age_str):\n",
" \"\"\"Convert age string to numeric value.\"\"\"\n",
" try:\n",
" # Extract the number after the colon and space\n",
" if ':' in age_str:\n",
" age_val = age_str.split(': ')[1].strip()\n",
" return float(age_val)\n",
" else:\n",
" return None\n",
" except:\n",
" return None\n",
"\n",
"def convert_trait(trait_str):\n",
" \"\"\"\n",
" Convert trait string to binary value.\n",
" This function is defined but won't be used since trait_row is None.\n",
" \"\"\"\n",
" return None\n",
"\n",
"def convert_gender(gender_str):\n",
" \"\"\"\n",
" Convert gender string to binary value.\n",
" This function is defined but won't be used since gender_row is None.\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",
"# Initial filtering and save the information\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",
"# Since trait_row is None, we skip the clinical feature extraction step entirely\n",
"if trait_row is not None:\n",
" # This block won't execute in this case since trait_row is None\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 dataframe\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Clinical data preview:\")\n",
" print(preview)\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 saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "1f019122",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "be9c3b27",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 20 gene/probe identifiers:\n",
"Index(['2315554', '2315633', '2315674', '2315739', '2315894', '2315918',\n",
" '2315951', '2316218', '2316245', '2316379', '2316558', '2316605',\n",
" '2316746', '2316905', '2316953', '2317246', '2317317', '2317434',\n",
" '2317472', '2317512'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. First get the file paths again to access the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data from the matrix_file\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n",
"print(\"First 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "66e2b791",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9ebeea21",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# Examine the gene identifiers in the given index\n",
"# The identifiers appear to be numerical, which suggests they are not human gene symbols\n",
"# Human gene symbols typically follow specific naming conventions (e.g., BRCA1, TP53)\n",
"# These look like probe IDs that would need mapping to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "5bc10530",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "eed54af9",
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"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['2315100', '2315106', '2315109', '2315111', '2315113'], 'GB_LIST': ['NR_024005,NR_034090,NR_024004,AK093685', 'DQ786314', nan, nan, 'DQ786265'], 'SPOT_ID': ['chr1:11884-14409', 'chr1:14760-15198', 'chr1:19408-19712', 'chr1:25142-25532', 'chr1:27563-27813'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10'], 'RANGE_STRAND': ['+', '+', '+', '+', '+'], 'RANGE_START': ['11884', '14760', '19408', '25142', '27563'], 'RANGE_STOP': ['14409', '15198', '19712', '25532', '27813'], 'total_probes': ['20', '8', '4', '4', '4'], 'gene_assignment': ['NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771 /// NR_034090 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 9 // 15q26.3 // 100288486 /// NR_024004 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771 /// AK093685 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771', '---', '---', '---', '---'], 'mrna_assignment': ['NR_024005 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 (DDX11L2), transcript variant 2, non-coding RNA. // chr1 // 100 // 80 // 16 // 16 // 0 /// NR_034090 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 9 (DDX11L9), non-coding RNA. // chr1 // 100 // 80 // 16 // 16 // 0 /// NR_024004 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 (DDX11L2), transcript variant 1, non-coding RNA. // chr1 // 100 // 75 // 15 // 15 // 0 /// AK093685 // GenBank // Homo sapiens cDNA FLJ36366 fis, clone THYMU2007824. // chr1 // 94 // 80 // 15 // 16 // 0 /// ENST00000513886 // ENSEMBL // cdna:known chromosome:GRCh37:16:61555:64090:1 gene:ENSG00000233614 // chr1 // 100 // 80 // 16 // 16 // 0 /// ENST00000456328 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000223972 // chr1 // 100 // 80 // 16 // 16 // 0 /// ENST00000518655 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000253101 // chr1 // 100 // 80 // 16 // 16 // 0', 'DQ786314 // GenBank // Homo sapiens clone HLS_IMAGE_811138 mRNA sequence. // chr1 // 100 // 38 // 3 // 3 // 0', '---', '---', 'DQ786265 // GenBank // Homo sapiens clone HLS_IMAGE_298685 mRNA sequence. // chr1 // 100 // 100 // 4 // 4 // 0'], 'category': ['main', 'main', '---', '---', 'main']}\n"
]
}
],
"source": [
"# 1. First get the file paths using geo_get_relevant_filepaths function\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. 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",
"# 3. 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": "0556a9fb",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First few rows of gene mapping:\n",
" ID Gene\n",
"0 2315100 NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-As...\n",
"1 2315106 ---\n",
"2 2315109 ---\n",
"3 2315111 ---\n",
"4 2315113 ---\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"First 20 gene symbols after mapping:\n",
"Index(['A-', 'A-2', 'A-52', 'A-E', 'A-I', 'A-II', 'A-IV', 'A-V', 'A0', 'A1',\n",
" 'A1-', 'A10', 'A11', 'A12', 'A13', 'A14', 'A16', 'A1BG', 'A1BG-AS',\n",
" 'A1CF'],\n",
" dtype='object', name='Gene')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data saved to ../../output/preprocess/Alopecia/gene_data/GSE18876.csv\n"
]
}
],
"source": [
"# 1. Identify the columns for gene identifiers and gene symbols in the gene annotation data\n",
"# From the preview, 'ID' column contains the same numeric identifiers as in gene expression data\n",
"# and 'gene_assignment' contains the gene symbol information\n",
"\n",
"# 2. Get gene mapping dataframe by extracting these columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')\n",
"\n",
"# Check the first few rows of the mapping\n",
"print(\"First few rows of gene mapping:\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene-level expression\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Check the first few gene symbols in the processed data\n",
"print(\"\\nFirst 20 gene symbols after mapping:\")\n",
"print(gene_data.index[:20])\n",
"\n",
"# Save gene data to CSV\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "63562ae8",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9d8aa50b",
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"execution": {
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"shell.execute_reply": "2025-03-25T06:24:43.122259Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalizing gene symbols...\n",
"Gene data shape after normalization: (18418, 98)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Alopecia/gene_data/GSE18876.csv\n",
"No trait data available for clinical feature extraction\n",
"Empty clinical data saved to ../../output/preprocess/Alopecia/clinical_data/GSE18876.csv\n",
"Creating gene data representation without clinical features...\n",
"Linked data shape: (98, 18418)\n",
"Dataset usability: False\n",
"Dataset is not usable for trait-gene association studies due to missing trait data.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"print(\"Normalizing 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",
"\n",
"# Save the normalized gene data to a CSV file\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Since trait_row is None (no trait data available), we'll create an empty clinical dataframe\n",
"print(\"No trait data available for clinical feature extraction\")\n",
"selected_clinical_df = pd.DataFrame()\n",
"\n",
"# Save empty clinical data to a CSV file for consistency\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\"Empty clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Create a linked dataframe with just gene data (no clinical features)\n",
"print(\"Creating gene data representation without clinical features...\")\n",
"linked_data = normalized_gene_data.T # Transpose to get samples as rows\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 3-6. Since trait data is unavailable, we'll use is_final=False in validation\n",
"# We'll skip handling missing values and bias checking since they require trait data\n",
"\n",
"# Update the note to reflect the actual dataset\n",
"note = \"Dataset contains gene expression data from skin samples of healthy males of different ages, as described in the study 'Transcriptional Profile of Aging in Healthy Human Skin'. The study mentions a subgroup with androgenetic alopecia, but this information is not available in the clinical annotations.\"\n",
"\n",
"# Perform validation with is_final=False since we can't evaluate bias without trait data\n",
"is_usable = 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",
"print(f\"Dataset usability: {is_usable}\")\n",
"print(\"Dataset is not usable for trait-gene association studies due to missing trait data.\")"
]
}
],
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