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{
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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 = \"Sarcoma\"\n",
"cohort = \"GSE133228\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Sarcoma\"\n",
"in_cohort_dir = \"../../input/GEO/Sarcoma/GSE133228\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Sarcoma/GSE133228.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Sarcoma/gene_data/GSE133228.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Sarcoma/clinical_data/GSE133228.csv\"\n",
"json_path = \"../../output/preprocess/Sarcoma/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "315e4635",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files in the directory:\n",
"['GSE133228-GPL16311_series_matrix.txt.gz', 'GSE133228_family.soft.gz']\n",
"SOFT file: ../../input/GEO/Sarcoma/GSE133228/GSE133228_family.soft.gz\n",
"Matrix file: ../../input/GEO/Sarcoma/GSE133228/GSE133228-GPL16311_series_matrix.txt.gz\n",
"Background Information:\n",
"!Series_title\t\"STAG2 promotes CTCF-anchored loop extrusion and cis-promoter and -enhancer interactions\"\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: ['gender: Male', 'gender: Female'], 1: ['age: 3', 'age: 11', 'age: 4', 'age: 25', 'age: 13', 'age: 15', 'age: 19', 'age: 8', 'age: 20', 'age: 24', 'age: 16', 'age: 14', 'age: 5', 'age: 37', 'age: 26', 'age: 10', 'age: 35', 'age: 23', 'age: 17', 'age: 12', 'age: 9', 'age: 0', 'age: 36', 'age: 27', 'age: 1', 'age: 18', 'age: 29', 'age: 6', 'age: 28', 'age: 31'], 2: ['tumor type: primary tumor']}\n"
]
}
],
"source": [
"# 1. Check what files are actually in the directory\n",
"import os\n",
"print(\"Files in the directory:\")\n",
"files = os.listdir(in_cohort_dir)\n",
"print(files)\n",
"\n",
"# 2. Find appropriate files with more flexible pattern matching\n",
"soft_file = None\n",
"matrix_file = None\n",
"\n",
"for file in files:\n",
" file_path = os.path.join(in_cohort_dir, file)\n",
" # Look for files that might contain SOFT or matrix data with various possible extensions\n",
" if 'soft' in file.lower() or 'family' in file.lower() or file.endswith('.soft.gz'):\n",
" soft_file = file_path\n",
" if 'matrix' in file.lower() or file.endswith('.txt.gz') or file.endswith('.tsv.gz'):\n",
" matrix_file = file_path\n",
"\n",
"if not soft_file:\n",
" print(\"Warning: Could not find a SOFT file. Using the first .gz file as fallback.\")\n",
" gz_files = [f for f in files if f.endswith('.gz')]\n",
" if gz_files:\n",
" soft_file = os.path.join(in_cohort_dir, gz_files[0])\n",
"\n",
"if not matrix_file:\n",
" print(\"Warning: Could not find a matrix file. Using the second .gz file as fallback if available.\")\n",
" gz_files = [f for f in files if f.endswith('.gz')]\n",
" if len(gz_files) > 1 and soft_file != os.path.join(in_cohort_dir, gz_files[1]):\n",
" matrix_file = os.path.join(in_cohort_dir, gz_files[1])\n",
" elif len(gz_files) == 1 and not soft_file:\n",
" matrix_file = os.path.join(in_cohort_dir, gz_files[0])\n",
"\n",
"print(f\"SOFT file: {soft_file}\")\n",
"print(f\"Matrix file: {matrix_file}\")\n",
"\n",
"# 3. Read files if found\n",
"if soft_file and matrix_file:\n",
" # 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",
" \n",
" try:\n",
" background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
" \n",
" # Obtain the sample characteristics dictionary from the clinical dataframe\n",
" sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
" \n",
" # 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",
" except Exception as e:\n",
" print(f\"Error processing files: {e}\")\n",
" # Try swapping files if first attempt fails\n",
" print(\"Trying to swap SOFT and matrix files...\")\n",
" temp = soft_file\n",
" soft_file = matrix_file\n",
" matrix_file = temp\n",
" try:\n",
" background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
" sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
" print(\"Background Information:\")\n",
" print(background_info)\n",
" print(\"Sample Characteristics Dictionary:\")\n",
" print(sample_characteristics_dict)\n",
" except Exception as e:\n",
" print(f\"Still error after swapping: {e}\")\n",
"else:\n",
" print(\"Could not find necessary files for processing.\")\n"
]
},
{
"cell_type": "markdown",
"id": "e944276f",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c1a26138",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trait data is not available. Skipping clinical feature extraction.\n"
]
}
],
"source": [
"import pandas as pd\n",
"import os\n",
"import re\n",
"import numpy as np\n",
"\n",
"# Load clinical data\n",
"clinical_data = pd.read_csv(os.path.join(in_cohort_dir, \"GSE133228-GPL16311_series_matrix.txt.gz\"), \n",
" sep='\\t', comment='!', skiprows=0)\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the matrix filename \"GSE133228-GPL16311_series_matrix.txt.gz\", this likely contains gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# For trait (Sarcoma)\n",
"# From sample characteristics dict, key 2 has 'tumor type: primary tumor', but it's a constant value\n",
"# As per instructions, constant features are useless in associative studies\n",
"trait_row = None\n",
"\n",
"# For age\n",
"# Age is available under key 1 in the sample characteristics dictionary\n",
"age_row = 1\n",
"\n",
"# For gender\n",
"# Gender is available under key 0 in the sample characteristics dictionary\n",
"gender_row = 0\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"# For trait (keeping this function in case it's needed later)\n",
"def convert_trait(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" # Handle if value is already numeric\n",
" if isinstance(value, (int, float)):\n",
" return 1 if value == 1 else 0\n",
" \n",
" # For string values, extract after colon if present\n",
" if ':' in str(value):\n",
" value = str(value).split(':', 1)[1].strip()\n",
" \n",
" if 'primary tumor' in str(value).lower():\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
"# For age\n",
"def convert_age(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" # Handle if value is already numeric\n",
" if isinstance(value, (int, float)):\n",
" return float(value)\n",
" \n",
" # Extract value after colon if present\n",
" if ':' in str(value):\n",
" value = str(value).split(':', 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"# For gender\n",
"def convert_gender(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" # Handle if value is already numeric\n",
" if isinstance(value, (int, float)):\n",
" return 1 if value == 1 else 0 if value == 0 else None\n",
" \n",
" # Extract value after colon if present\n",
" if ':' in str(value):\n",
" value = str(value).split(':', 1)[1].strip().lower()\n",
" else:\n",
" value = str(value).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 if trait data is available (trait_row is not None)\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Initial filtering\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 this step\n",
"if trait_row is not 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(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save to CSV\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
"else:\n",
" print(\"Trait data is not available. Skipping clinical feature extraction.\")\n"
]
},
{
"cell_type": "markdown",
"id": "694ba24a",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\n",
"Found potential subseries references:\n",
"!Series_relation = SuperSeries of: GSE132966\n",
"!Series_relation = SuperSeries of: GSE133154\n",
"!Series_relation = SuperSeries of: GSE133227\n",
"!Series_relation = SuperSeries of: GSE142162\n",
"!Series_relation = SuperSeries of: GSE156649\n",
"!Series_relation = SuperSeries of: GSE156650\n",
"!Series_relation = SuperSeries of: GSE156653\n",
"!Series_relation = SuperSeries of: GSE171948\n",
"\n",
"Gene data extraction result:\n",
"Number of rows: 19070\n",
"First 20 gene/probe identifiers:\n",
"Index(['100009676_at', '10000_at', '10001_at', '10002_at', '10003_at',\n",
" '100048912_at', '100049716_at', '10004_at', '10005_at', '10006_at',\n",
" '10007_at', '10008_at', '100093630_at', '10009_at', '1000_at',\n",
" '100101467_at', '100101938_at', '10010_at', '100113407_at', '10011_at'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. First get the path to the soft and matrix files\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Looking more carefully at the background information\n",
"# This is a SuperSeries which doesn't contain direct gene expression data\n",
"# Need to investigate the soft file to find the subseries\n",
"print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n",
"\n",
"# Open the SOFT file to try to identify subseries\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" subseries_lines = []\n",
" for i, line in enumerate(f):\n",
" if 'Series_relation' in line and 'SuperSeries of' in line:\n",
" subseries_lines.append(line.strip())\n",
" if i > 1000: # Limit search to first 1000 lines\n",
" break\n",
"\n",
"# Display the subseries found\n",
"if subseries_lines:\n",
" print(\"Found potential subseries references:\")\n",
" for line in subseries_lines:\n",
" print(line)\n",
"else:\n",
" print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n",
"\n",
"# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(\"\\nGene data extraction result:\")\n",
" print(\"Number of rows:\", len(gene_data))\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",
" print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n"
]
},
{
"cell_type": "markdown",
"id": "9700f5dc",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
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"outputs": [],
"source": [
"# Analyze the gene identifiers\n",
"# The format \"XXX_at\" where XXX is a numerical ID suggests these are probe identifiers\n",
"# from a microarray platform (likely Affymetrix), not standard human gene symbols.\n",
"# These need to be mapped to official gene symbols for biological interpretation.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "52093038",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['1_at', '10_at', '100_at', '1000_at', '10000_at'], 'SPOT_ID': ['1', '10', '100', '1000', '10000'], 'Description': ['alpha-1-B glycoprotein', 'N-acetyltransferase 2 (arylamine N-acetyltransferase)', 'adenosine deaminase', 'cadherin 2, type 1, N-cadherin (neuronal)', 'v-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma)']}\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": "ddf97a15",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "676792eb",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping info:\n",
"Total number of probe-gene mappings: 19037\n",
"Sample mappings (first 5 rows):\n",
" ID Gene\n",
"0 1_at alpha-1-B glycoprotein\n",
"1 10_at N-acetyltransferase 2 (arylamine N-acetyltrans...\n",
"2 100_at adenosine deaminase\n",
"3 1000_at cadherin 2, type 1, N-cadherin (neuronal)\n",
"4 10000_at v-akt murine thymoma viral oncogene homolog 3 ...\n",
"\n",
"After mapping:\n",
"Number of unique genes: 2034\n",
"First 5 gene symbols:\n",
"Index(['A-', 'A-2', 'A-52', 'A-I', 'A-II'], dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. Identify which columns in the gene annotation dataframe contain the identifiers and symbols\n",
"# From the preview, we can see:\n",
"# - 'ID' column contains identifiers like '1_at', matching the gene expression data format\n",
"# - 'Description' column contains gene names/descriptions\n",
"\n",
"# 2. Extract the gene mapping dataframe with probe IDs and gene symbols\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Description')\n",
"\n",
"# Print info about the mapping\n",
"print(\"Gene mapping info:\")\n",
"print(\"Total number of probe-gene mappings:\", len(gene_mapping))\n",
"print(\"Sample mappings (first 5 rows):\")\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(expression_df=gene_data, mapping_df=gene_mapping)\n",
"\n",
"# Print some statistics about the gene data after mapping\n",
"print(\"\\nAfter mapping:\")\n",
"print(\"Number of unique genes:\", len(gene_data))\n",
"print(\"First 5 gene symbols:\")\n",
"print(gene_data.index[:5])\n"
]
},
{
"cell_type": "markdown",
"id": "f43cf360",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1f5b831d",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original gene expression data shape: (19070, 79)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created mapping with 19037 entries\n",
"Processing batch 1/20\n",
"Processing batch 2/20\n",
"Processing batch 3/20\n",
"Processing batch 4/20\n",
"Processing batch 5/20\n",
"Processing batch 6/20\n",
"Processing batch 7/20\n",
"Processing batch 8/20\n",
"Processing batch 9/20\n",
"Processing batch 10/20\n",
"Processing batch 11/20\n",
"Processing batch 12/20\n",
"Processing batch 13/20\n",
"Processing batch 14/20\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing batch 15/20\n",
"Processing batch 16/20\n",
"Processing batch 17/20\n",
"Processing batch 18/20\n",
"Processing batch 19/20\n",
"Processing batch 20/20\n",
"After mapping: (4280, 79)\n",
"After normalization: (1171, 79)\n",
"Gene expression data saved to ../../output/preprocess/Sarcoma/gene_data/GSE133228.csv\n",
"Sample IDs from gene data: 79 samples\n",
"Clinical data shape: (1, 79)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data saved to ../../output/preprocess/Sarcoma/clinical_data/GSE133228.csv\n",
"Selecting top 5000 genes with highest variance...\n",
"Subset gene data shape: (1171, 79)\n",
"Shape of linked data: (79, 1172)\n",
"Handling missing values...\n",
"Shape of linked data after handling missing values: (79, 1172)\n",
"Checking for bias in features...\n",
"Quartiles for 'Sarcoma':\n",
" 25%: 1.0\n",
" 50% (Median): 1.0\n",
" 75%: 1.0\n",
"Min: 1\n",
"Max: 1\n",
"The distribution of the feature 'Sarcoma' in this dataset is severely biased.\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/media/techt/DATA/GenoAgent/tools/preprocess.py:455: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
" df[gene_cols] = df[gene_cols].fillna(df[gene_cols].mean())\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset validation failed due to trait bias. Final linked data not saved.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols - let's take a more memory-efficient approach\n",
"# Instead of doing all at once, process in smaller chunks\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Get fresh gene expression data\n",
"gene_data = get_genetic_data(matrix_file)\n",
"print(f\"Original gene expression data shape: {gene_data.shape}\")\n",
"\n",
"# Get the gene annotation again\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Description')\n",
"print(f\"Created mapping with {len(gene_mapping)} entries\")\n",
"\n",
"# Process and map in chunks to reduce memory usage\n",
"batch_size = 1000\n",
"num_batches = (len(gene_data) + batch_size - 1) // batch_size\n",
"result_dfs = []\n",
"\n",
"for i in range(num_batches):\n",
" print(f\"Processing batch {i+1}/{num_batches}\")\n",
" start_idx = i * batch_size\n",
" end_idx = min((i + 1) * batch_size, len(gene_data))\n",
" \n",
" # Get a subset of the expression data\n",
" batch_expr = gene_data.iloc[start_idx:end_idx]\n",
" \n",
" # Process this batch\n",
" batch_mapping = gene_mapping[gene_mapping['ID'].isin(batch_expr.index)]\n",
" if len(batch_mapping) > 0:\n",
" mapped_batch = apply_gene_mapping(batch_expr, batch_mapping)\n",
" result_dfs.append(mapped_batch)\n",
" \n",
" # Clear memory\n",
" del batch_expr\n",
" del batch_mapping\n",
"\n",
"# Combine results\n",
"if result_dfs:\n",
" mapped_gene_data = pd.concat(result_dfs)\n",
" print(f\"After mapping: {mapped_gene_data.shape}\")\n",
" \n",
" # Normalize gene symbols using NCBI database\n",
" try:\n",
" gene_data_normalized = normalize_gene_symbols_in_index(mapped_gene_data)\n",
" print(f\"After normalization: {gene_data_normalized.shape}\")\n",
" except Exception as e:\n",
" print(f\"Error during normalization: {e}\")\n",
" # Fallback to unmapped data\n",
" gene_data_normalized = mapped_gene_data\n",
"else:\n",
" print(\"Mapping failed for all batches, using original data\")\n",
" gene_data_normalized = gene_data\n",
"\n",
"# Save the gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data_normalized.to_csv(out_gene_data_file)\n",
"print(f\"Gene expression data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Create clinical data with the trait information\n",
"sample_ids = gene_data.columns.tolist()\n",
"print(f\"Sample IDs from gene data: {len(sample_ids)} samples\")\n",
"\n",
"# Create a clinical dataframe with the trait (Sarcoma)\n",
"clinical_df = pd.DataFrame(index=[trait], columns=sample_ids)\n",
"clinical_df.loc[trait] = 1 # All samples are sarcoma tumors\n",
"\n",
"print(f\"Clinical data shape: {clinical_df.shape}\")\n",
"\n",
"# Save the clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# 3. Link clinical and genetic data - using smaller version for efficiency\n",
"# Select a subset of genes to reduce memory issues\n",
"print(\"Selecting top 5000 genes with highest variance...\")\n",
"if len(gene_data_normalized) > 5000:\n",
" gene_variance = gene_data_normalized.var(axis=1)\n",
" top_genes = gene_variance.nlargest(5000).index\n",
" gene_data_subset = gene_data_normalized.loc[top_genes]\n",
"else:\n",
" gene_data_subset = gene_data_normalized\n",
"\n",
"print(f\"Subset gene data shape: {gene_data_subset.shape}\")\n",
"linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data_subset)\n",
"print(f\"Shape of linked data: {linked_data.shape}\")\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"print(\"Handling missing values...\")\n",
"linked_data_cleaned = handle_missing_values(linked_data, trait)\n",
"print(f\"Shape of linked data after handling missing values: {linked_data_cleaned.shape}\")\n",
"\n",
"# 5. Check if the trait and demographic features are biased\n",
"print(\"Checking for bias in features...\")\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, trait)\n",
"\n",
"# 6. Validate the dataset and save cohort information\n",
"note = \"Dataset contains expression data for pediatric tumors including rhabdomyosarcoma (sarcoma). All samples are tumor samples, so trait bias is expected and the dataset is not suitable for case-control analysis.\"\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=unbiased_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",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Saved processed linked data to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset validation failed due to trait bias. Final linked data not saved.\")"
]
}
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