{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "d7c20b37", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:09:22.181719Z", "iopub.status.busy": "2025-03-25T07:09:22.181618Z", "iopub.status.idle": "2025-03-25T07:09:22.344878Z", "shell.execute_reply": "2025-03-25T07:09:22.344565Z" } }, "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 = \"Intellectual_Disability\"\n", "cohort = \"GSE158385\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Intellectual_Disability\"\n", "in_cohort_dir = \"../../input/GEO/Intellectual_Disability/GSE158385\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Intellectual_Disability/GSE158385.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Intellectual_Disability/gene_data/GSE158385.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Intellectual_Disability/clinical_data/GSE158385.csv\"\n", "json_path = \"../../output/preprocess/Intellectual_Disability/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "b66bda59", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "1531d515", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:09:22.346308Z", "iopub.status.busy": "2025-03-25T07:09:22.346167Z", "iopub.status.idle": "2025-03-25T07:09:22.399218Z", "shell.execute_reply": "2025-03-25T07:09:22.398923Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Apigenin as a Candidate Prenatal Treatment for Trisomy 21: Effects in Human Amniocytes and the Ts1Cje Mouse Model\"\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: ['tissue: forebrain'], 1: ['developmental stage: E15'], 2: ['genotype: WT', 'genotype: Ts1Cje'], 3: ['treatment: Powder', 'treatment: Apigenin']}\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": "155ba7cf", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "37d56a9d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:09:22.400266Z", "iopub.status.busy": "2025-03-25T07:09:22.400160Z", "iopub.status.idle": "2025-03-25T07:09:22.407907Z", "shell.execute_reply": "2025-03-25T07:09:22.407650Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of selected clinical data:\n", "{'GSM4798553': [nan], 'GSM4798554': [nan], 'GSM4798555': [nan], 'GSM4798556': [nan], 'GSM4798557': [nan], 'GSM4798558': [nan], 'GSM4798559': [nan], 'GSM4798560': [nan], 'GSM4798561': [nan], 'GSM4798562': [nan], 'GSM4798563': [nan], 'GSM4798564': [nan], 'GSM4798565': [nan], 'GSM4798566': [nan], 'GSM4798567': [nan], 'GSM4798568': [nan], 'GSM4798569': [nan], 'GSM4798570': [nan], 'GSM4798571': [nan], 'GSM4798572': [nan]}\n", "Clinical data saved to ../../output/preprocess/Intellectual_Disability/clinical_data/GSE158385.csv\n" ] } ], "source": [ "import pandas as pd\n", "from typing import Optional, Callable\n", "import os\n", "import json\n", "\n", "# Check if gene expression data is available\n", "# From the background information, this appears to be related to trisomy 21 and gene expression\n", "is_gene_available = True\n", "\n", "# Define the row indices for trait, age, and gender\n", "# trait_row: Karyotype information (row 2) can be used to determine intellectual disability (trisomy 21)\n", "trait_row = 2 # karyotype information\n", "age_row = None # No age information available\n", "gender_row = None # Gender can be inferred from karyotype, but it's not a separate variable for analysis\n", "\n", "# Conversion functions\n", "def convert_trait(value: str) -> Optional[int]:\n", " \"\"\"Convert karyotype information to binary trait value (1 for T21, 0 for normal)\"\"\"\n", " if not value or \":\" not in value:\n", " return None\n", " value = value.split(\":\", 1)[1].strip()\n", " if \"T21\" in value: # Trisomy 21 indicates intellectual disability\n", " return 1\n", " elif \"2N\" in value: # Normal karyotype\n", " return 0\n", " return None\n", "\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"Convert age value to float\"\"\"\n", " # Not used but defined for completeness\n", " if not value or \":\" not in value:\n", " return None\n", " value = value.split(\":\", 1)[1].strip()\n", " try:\n", " return float(value)\n", " except:\n", " return None\n", "\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n", " # Not used but defined for completeness\n", " if not value or \":\" not in value:\n", " return None\n", " value = value.split(\":\", 1)[1].strip()\n", " if \"female\" in value.lower() or \"f\" == value.lower():\n", " return 0\n", " elif \"male\" in value.lower() or \"m\" == value.lower():\n", " return 1\n", " return None\n", "\n", "# Check trait availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Save metadata using the validation function\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", "# Extract clinical features if trait data is available\n", "# Note: We'll assume the clinical_data is already available as a variable\n", "# from a previous step, rather than loading from a file\n", "if trait_row is not None and 'clinical_data' in locals():\n", " try:\n", " # Use the geo_select_clinical_features function to extract features\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 selected clinical data\n", " print(\"Preview of selected clinical data:\")\n", " print(preview_df(selected_clinical_df))\n", " \n", " # Create the output directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " \n", " # Save the clinical data to a CSV file\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " except Exception as e:\n", " print(f\"Error processing clinical data: {e}\")\n", "else:\n", " if trait_row is not None:\n", " print(\"Clinical data not available in memory. Skipping clinical feature extraction.\")\n", " else:\n", " print(\"No trait data available. Skipping clinical feature extraction.\")\n" ] }, { "cell_type": "markdown", "id": "d3282f6e", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "1b96ef2d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:09:22.408904Z", "iopub.status.busy": "2025-03-25T07:09:22.408802Z", "iopub.status.idle": "2025-03-25T07:09:22.466551Z", "shell.execute_reply": "2025-03-25T07:09:22.466248Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting gene data from matrix file:\n", "Successfully extracted gene data with 21225 rows\n", "First 20 gene IDs:\n", "Index(['100008567_at', '100009600_at', '100009609_at', '100009614_at',\n", " '100012_at', '100017_at', '100019_at', '100033459_at', '100034251_at',\n", " '100034748_at', '100036520_at', '100036521_at', '100036523_at',\n", " '100036537_at', '100036768_at', '100037258_at', '100037260_at',\n", " '100037262_at', '100037278_at', '100037396_at'],\n", " dtype='object', name='ID')\n", "\n", "Gene expression data available: True\n" ] } ], "source": [ "# 1. Get the file paths for the SOFT file and matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Extract gene expression data from the matrix file\n", "try:\n", " print(\"Extracting gene data from matrix file:\")\n", " gene_data = get_genetic_data(matrix_file)\n", " if gene_data.empty:\n", " print(\"Extracted gene expression data is empty\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n", " print(\"First 20 gene IDs:\")\n", " print(gene_data.index[:20])\n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n", " print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n", " is_gene_available = False\n", "\n", "print(f\"\\nGene expression data available: {is_gene_available}\")\n" ] }, { "cell_type": "markdown", "id": "12237d86", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "43ca195f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:09:22.467721Z", "iopub.status.busy": "2025-03-25T07:09:22.467614Z", "iopub.status.idle": "2025-03-25T07:09:22.469358Z", "shell.execute_reply": "2025-03-25T07:09:22.469095Z" } }, "outputs": [], "source": [ "# Based on my biomedical knowledge, these identifiers (TC01000001.hg.1, etc.) are not standard human gene symbols\n", "# They appear to be Affymetrix transcript cluster IDs from a human gene array\n", "# Standard human gene symbols would be like BRCA1, TP53, etc.\n", "# These IDs need to be mapped to standard gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "cfdf62a1", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "407315bf", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:09:22.470375Z", "iopub.status.busy": "2025-03-25T07:09:22.470276Z", "iopub.status.idle": "2025-03-25T07:09:25.433365Z", "shell.execute_reply": "2025-03-25T07:09:25.432988Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting gene annotation data from SOFT file...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully extracted gene annotation data with 1647953 rows\n", "\n", "Gene annotation preview (first few rows):\n", "{'ID': ['100008567_at', '100009600_at', '100009609_at', '100009614_at', '100012_at'], 'ENTREZ_GENE_ID': ['100008567', '100009600', '100009609', '100009614', '100012'], 'Description': ['predicted gene 14964', 'zinc finger, GATA-like protein 1', 'vomeronasal 2, receptor 65', 'keratin associated protein LOC100009614', 'oogenesin 3']}\n", "\n", "Column names in gene annotation data:\n", "['ID', 'ENTREZ_GENE_ID', 'Description']\n" ] } ], "source": [ "# 1. Extract gene annotation data from the SOFT file\n", "print(\"Extracting gene annotation data from SOFT file...\")\n", "try:\n", " # Use the library function to extract gene annotation\n", " gene_annotation = get_gene_annotation(soft_file)\n", " print(f\"Successfully extracted gene annotation data with {len(gene_annotation.index)} rows\")\n", " \n", " # Preview the annotation DataFrame\n", " print(\"\\nGene annotation preview (first few rows):\")\n", " print(preview_df(gene_annotation))\n", " \n", " # Show column names to help identify which columns we need for mapping\n", " print(\"\\nColumn names in gene annotation data:\")\n", " print(gene_annotation.columns.tolist())\n", " \n", " # Check for relevant mapping columns\n", " if 'GB_ACC' in gene_annotation.columns:\n", " print(\"\\nThe dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\")\n", " # Count non-null values in GB_ACC column\n", " non_null_count = gene_annotation['GB_ACC'].count()\n", " print(f\"Number of rows with GenBank accessions: {non_null_count} out of {len(gene_annotation)}\")\n", " \n", " if 'SPOT_ID' in gene_annotation.columns:\n", " print(\"\\nThe dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\")\n", " print(\"Example SPOT_ID format:\", gene_annotation['SPOT_ID'].iloc[0])\n", " \n", "except Exception as e:\n", " print(f\"Error processing gene annotation data: {e}\")\n", " is_gene_available = False\n" ] }, { "cell_type": "markdown", "id": "5c593ed3", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "a5b2974c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:09:25.434765Z", "iopub.status.busy": "2025-03-25T07:09:25.434638Z", "iopub.status.idle": "2025-03-25T07:09:26.413409Z", "shell.execute_reply": "2025-03-25T07:09:26.413080Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Analyzing gene identifiers for mapping...\n", "\n", "Creating gene mapping dataframe...\n", "Created mapping dataframe with 1647953 rows\n", "Sample mapping entries:\n", " ID Gene\n", "0 100008567_at 100008567\n", "1 100009600_at 100009600\n", "2 100009609_at 100009609\n", "3 100009614_at 100009614\n", "4 100012_at 100012\n", "\n", "Applying gene mapping to expression data...\n", "Overlap between expression data and mapping: 21225 probes out of 21225\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully mapped to 0 genes\n", "First few gene symbols:\n", "Index([], dtype='object', name='Gene')\n", "\n", "Normalizing gene symbols...\n", "After normalization: 0 genes\n", "Gene expression data saved to ../../output/preprocess/Intellectual_Disability/gene_data/GSE158385.csv\n" ] } ], "source": [ "# 1. Examine the gene identifiers to determine mapping\n", "print(\"Analyzing gene identifiers for mapping...\")\n", "\n", "# From the previous output, we have gene annotation with ID, ENTREZ_GENE_ID, and Description\n", "# We'll use the ENTREZ_GENE_ID for mapping since it contains gene identifiers\n", "\n", "# Create mapping dataframe using ID and ENTREZ_GENE_ID\n", "print(\"\\nCreating gene mapping dataframe...\")\n", "mapping_df = pd.DataFrame({\n", " 'ID': gene_annotation['ID'],\n", " 'Gene': gene_annotation['ENTREZ_GENE_ID']\n", "})\n", "\n", "# Keep only rows with valid gene mappings\n", "mapping_df = mapping_df.dropna(subset=['Gene'])\n", "mapping_df = mapping_df[mapping_df['Gene'] != '---'] # Remove any placeholder values\n", "print(f\"Created mapping dataframe with {len(mapping_df)} rows\")\n", "print(\"Sample mapping entries:\")\n", "print(mapping_df.head())\n", "\n", "# 2. Apply gene mapping to convert probe-level measurements to gene expression data\n", "try:\n", " print(\"\\nApplying gene mapping to expression data...\")\n", " # First, check the overlap between gene expression data IDs and mapping IDs\n", " overlap_count = sum(gene_data.index.isin(mapping_df['ID']))\n", " print(f\"Overlap between expression data and mapping: {overlap_count} probes out of {len(gene_data.index)}\")\n", " \n", " if overlap_count > 0:\n", " # Apply gene mapping to convert probe-level measurements to gene expression data\n", " gene_data = apply_gene_mapping(gene_data, mapping_df)\n", " print(f\"Successfully mapped to {len(gene_data.index)} genes\")\n", " print(\"First few gene symbols:\")\n", " print(gene_data.index[:5])\n", " \n", " # Optional: Normalize gene symbols to standard forms\n", " try:\n", " print(\"\\nNormalizing gene symbols...\")\n", " gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"After normalization: {len(gene_data.index)} genes\")\n", " except Exception as e:\n", " print(f\"Error normalizing gene symbols: {e}\")\n", " # Continue with unnormalized symbols\n", " else:\n", " print(\"No overlap found between expression data IDs and mapping IDs.\")\n", " print(\"Using probe IDs directly as gene proxies.\")\n", " # Rename index to Gene for consistency in downstream processing\n", " gene_data.index.name = 'Gene'\n", " \n", " # 3. Save the gene expression 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\"Gene expression data saved to {out_gene_data_file}\")\n", " \n", "except Exception as e:\n", " print(f\"Error applying gene mapping: {e}\")\n", " is_gene_available = False\n" ] }, { "cell_type": "markdown", "id": "690ec6b7", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "2602f583", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:09:26.414798Z", "iopub.status.busy": "2025-03-25T07:09:26.414690Z", "iopub.status.idle": "2025-03-25T07:09:26.731964Z", "shell.execute_reply": "2025-03-25T07:09:26.731633Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Handling gene data...\n", "No valid gene symbols after mapping. Using original probe data as gene proxies...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data saved to: ../../output/preprocess/Intellectual_Disability/gene_data/GSE158385.csv with 21225 features\n", "\n", "Loading clinical data and linking with genetic data...\n", "Loaded clinical data with shape: (1, 19)\n", "Clinical data columns: Index(['GSM4798554', 'GSM4798555', 'GSM4798556', 'GSM4798557', 'GSM4798558',\n", " 'GSM4798559', 'GSM4798560', 'GSM4798561', 'GSM4798562', 'GSM4798563',\n", " 'GSM4798564', 'GSM4798565', 'GSM4798566', 'GSM4798567', 'GSM4798568',\n", " 'GSM4798569', 'GSM4798570', 'GSM4798571', 'GSM4798572'],\n", " dtype='object')\n", "Clinical data index: Index([nan], dtype='float64', name='GSM4798553')\n", "Updated clinical data index: Index(['Intellectual_Disability'], dtype='object')\n", "First few clinical sample IDs: ['GSM4798554', 'GSM4798555', 'GSM4798556', 'GSM4798557', 'GSM4798558']\n", "First few genetic sample IDs: ['GSM4798553', 'GSM4798554', 'GSM4798555', 'GSM4798556', 'GSM4798557']\n", "Found 19 common samples between clinical and genetic data\n", "Linked data shape: (19, 21226)\n", "Linked data columns: Index(['Intellectual_Disability', '100008567_at', '100009600_at',\n", " '100009609_at', '100009614_at', '100012_at', '100017_at', '100019_at',\n", " '100033459_at', '100034251_at'],\n", " dtype='object')\n", "\n", "Handling missing values...\n", "After handling missing values, data shape: (0, 1)\n", "\n", "Checking for bias in features...\n", "Quartiles for 'Intellectual_Disability':\n", " 25%: nan\n", " 50% (Median): nan\n", " 75%: nan\n", "Min: nan\n", "Max: nan\n", "The distribution of the feature 'Intellectual_Disability' in this dataset is fine.\n", "\n", "\n", "Performing final validation...\n", "Abnormality detected in the cohort: GSE158385. Preprocessing failed.\n", "Dataset not usable for Intellectual_Disability association studies. Data not saved.\n" ] } ], "source": [ "# 1. Use the original gene expression data with probe IDs since normalization gave 0 genes\n", "print(\"\\nHandling gene data...\")\n", "try:\n", " # Load original gene data from previous step if it exists\n", " if 'gene_data' not in locals() or len(gene_data.index) == 0:\n", " print(\"No valid gene symbols after mapping. Using original probe data as gene proxies...\")\n", " # Get original gene expression data again\n", " gene_data = get_genetic_data(matrix_file)\n", " # Rename index to Gene for consistency\n", " gene_data.index.name = 'Gene'\n", " \n", " # Save the 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\"Gene data saved to: {out_gene_data_file} with {len(gene_data.index)} features\")\n", " is_gene_available = len(gene_data.index) > 0\n", "except Exception as e:\n", " print(f\"Error handling gene data: {e}\")\n", " is_gene_available = False\n", "\n", "# 2. Load the clinical data and link with genetic data\n", "print(\"\\nLoading clinical data and linking with genetic data...\")\n", "try:\n", " # Load the clinical data\n", " clinical_df = pd.read_csv(out_clinical_data_file)\n", " \n", " # If clinical_df doesn't have an index column, set the first column as index\n", " if not clinical_df.index.name and len(clinical_df.columns) > 1:\n", " clinical_df = clinical_df.set_index(clinical_df.columns[0])\n", " \n", " print(f\"Loaded clinical data with shape: {clinical_df.shape}\")\n", " print(f\"Clinical data columns: {clinical_df.columns}\")\n", " print(f\"Clinical data index: {clinical_df.index}\")\n", " \n", " # Set the appropriate name for the trait in clinical data\n", " # Since we're working with one trait row from earlier steps\n", " clinical_df.index = [trait]\n", " print(f\"Updated clinical data index: {clinical_df.index}\")\n", " \n", " # Ensure we have gene data\n", " if is_gene_available and not gene_data.empty:\n", " # Print sample IDs from both datasets for debugging\n", " print(\"First few clinical sample IDs:\", list(clinical_df.columns)[:5])\n", " print(\"First few genetic sample IDs:\", list(gene_data.columns)[:5])\n", " \n", " # Check and align sample IDs if needed\n", " common_samples = set(clinical_df.columns).intersection(set(gene_data.columns))\n", " if len(common_samples) > 0:\n", " print(f\"Found {len(common_samples)} common samples between clinical and genetic data\")\n", " # Keep only common samples\n", " clinical_subset = clinical_df[list(common_samples)]\n", " gene_data_subset = gene_data[list(common_samples)]\n", " \n", " # Link clinical and genetic data\n", " linked_data = pd.concat([clinical_subset, gene_data_subset], axis=0).T\n", " is_trait_available = True\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " print(f\"Linked data columns: {linked_data.columns[:10]}\") # Print first 10 columns\n", " \n", " # 3. Handle missing values systematically\n", " print(\"\\nHandling missing values...\")\n", " try:\n", " # Make sure the trait column exists in the linked data\n", " if trait not in linked_data.columns:\n", " print(f\"Warning: {trait} column not found in linked data. Available columns: {linked_data.columns[:5]}\")\n", " # If the first column is our trait data, rename it\n", " linked_data.rename(columns={linked_data.columns[0]: trait}, inplace=True)\n", " print(f\"Renamed first column to {trait}\")\n", " \n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"After handling missing values, data shape: {linked_data.shape}\")\n", " \n", " # 4. Determine whether the trait and demographic features are biased\n", " print(\"\\nChecking for bias in features...\")\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " except Exception as e:\n", " print(f\"Error handling missing values: {e}\")\n", " linked_data = pd.DataFrame()\n", " is_trait_available = False\n", " is_biased = True\n", " else:\n", " print(\"No common samples found between clinical and genetic data\")\n", " linked_data = pd.DataFrame()\n", " is_trait_available = False\n", " is_biased = True\n", " else:\n", " print(\"No valid gene expression data available\")\n", " linked_data = pd.DataFrame()\n", " is_trait_available = False\n", " is_biased = True\n", " \n", "except Exception as e:\n", " print(f\"Error in linking clinical and genetic data: {e}\")\n", " linked_data = pd.DataFrame()\n", " is_trait_available = False\n", " is_biased = True\n", "\n", "# 5. Final quality validation\n", "print(\"\\nPerforming final validation...\")\n", "note = \"Dataset is about trisomy 21 (Down syndrome) which is associated with intellectual disability\"\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", "# 6. Save linked data if usable\n", "if is_usable:\n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " \n", " # Save linked data\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(f\"Dataset not usable for {trait} association studies. Data not saved.\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }