{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "56b10ce3", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:11:35.071914Z", "iopub.status.busy": "2025-03-25T07:11:35.071808Z", "iopub.status.idle": "2025-03-25T07:11:35.235244Z", "shell.execute_reply": "2025-03-25T07:11:35.234882Z" } }, "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 = \"GSE98697\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Intellectual_Disability\"\n", "in_cohort_dir = \"../../input/GEO/Intellectual_Disability/GSE98697\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Intellectual_Disability/GSE98697.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Intellectual_Disability/gene_data/GSE98697.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Intellectual_Disability/clinical_data/GSE98697.csv\"\n", "json_path = \"../../output/preprocess/Intellectual_Disability/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "913e2593", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "3aa7bec2", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:11:35.236688Z", "iopub.status.busy": "2025-03-25T07:11:35.236550Z", "iopub.status.idle": "2025-03-25T07:11:35.440720Z", "shell.execute_reply": "2025-03-25T07:11:35.440364Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Coding and noncoding gene expression of 48 pediatric AML samples\"\n", "!Series_summary\t\"Long non-coding RNAs (lncRNAs) and miRNAs have emerged as crucial regulators of gene expression and cell fate decisions. Here we present an integrated analysis of the ncRNA-transcriptome of purified human hematopoietic stem cells (HSCs) and their differentiated progenies, including granulocytes, monocytes, T-cells, NK-cells, B-cells, megakaryocytes and erythroid precursors, which we correlated with the ncRNA expression profile of 48 pediatric AML samples to establish a core lncRNA stem cell signature in AML.Linear (PCA) and nonlinear (t-SNE) dimensionality reduction of 46 pediatric AML samples including Down syndrome AMKL, core-binding factor AMLs (inv[16] or t[8;21]) and MLL-rearranged leukemias mapped most samples to a space between HSCs and differentiated cells together with the myeloid progenitors. A subset of AML-samples mapped closely to healthy HSCs, including most of the DS-AMKLs and MLL-AMLs. Following the incorporation of acute myeloid leukemia (AML) samples into the landscape, we further uncover prognostically relevant ncRNA stem cell signatures shared between AML blasts and healthy hematopoietic stem cells.\"\n", "!Series_overall_design\t\"AML blasts from 48 pediatric AML samples were FACS-purified and total RNA was subjected to Microarray Analysis on the Arraystar Human LncRNA microarray V2.0 (Agilent-033010) platform\"\n", "Sample Characteristics Dictionary:\n", "{0: ['cell type: AML blasts'], 1: ['facs sorting gate: CD117- CD34+/- CD41+', 'facs sorting gate: CD117- CD34- CD41+', 'facs sorting gate: CD117+ CD34+ CD41- AND CD117- CD34- CD41+', 'facs sorting gate: CD117- CD34+ CD41+', 'facs sorting gate: CD117high+ CD33+ CD34-', 'facs sorting gate: CD117- CD33+ CD34-', 'facs sorting gate: CD117+/ - CD33+ CD34+', 'facs sorting gate: CD117+ CD33+ CD34+', 'facs sorting gate: CD117+ CD33+ CD34+/-', 'facs sorting gate: CD117+ CD33- CD34+', 'facs sorting gate: CD117- CD33- CD34+', 'facs sorting gate: CD117- CD33+ CD34+', 'facs sorting gate: CD117+ CD33+/- CD34+', 'facs sorting gate: CD117+ CD33- CD34-', 'facs sorting gate: CD117+ CD33- CD34+/-', 'facs sorting gate: CD117+/- CD33+ CD34-', 'facs sorting gate: CD117+ CD33- CD34-', 'facs sorting gate: CD117+ CD33+ CD34-', 'facs sorting gate: CD117+ CD33+ CD34-', 'facs sorting gate: CD117+7- CD33+ CD34-'], 2: ['aml subtype: Down-syndrome acute megakaryoblastic leukemia', 'aml subtype: acute megakaryoblastic leukemia', 'aml subtype: AML inv(16) M4eo', 'aml subtype: AML t(15;17) acute promyelocytic leukemia', 'aml subtype: AML t(8;21) M2', 'aml subtype: AML t(10;11) M5']}\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": "0820f588", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "93b6dfea", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:11:35.442207Z", "iopub.status.busy": "2025-03-25T07:11:35.442086Z", "iopub.status.idle": "2025-03-25T07:11:35.464643Z", "shell.execute_reply": "2025-03-25T07:11:35.464375Z" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from typing import Dict, Any, Callable, Optional\n", "import os\n", "import json\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains microarray data of coding and noncoding gene expression\n", "# The \"Series_overall_design\" mentions \"Microarray Analysis on the Arraystar Human LncRNA microarray\" platform\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "\n", "# For trait: Looking at the sample characteristics, row 2 contains AML subtypes\n", "# This is not intellectual disability data, but AML (Acute Myeloid Leukemia) data\n", "# Therefore, the intellectual disability trait is not available in this dataset\n", "trait_row = None # No intellectual disability data\n", "\n", "# For age: There's no specific age mentioned in the sample characteristics\n", "age_row = None\n", "\n", "# For gender: There's no gender information in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "# Define conversion functions even though they might not be used in this dataset\n", "\n", "def convert_trait(value: str) -> Optional[int]:\n", " \"\"\"Convert trait value to binary (0 or 1).\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # Since this dataset doesn't have intellectual disability data, \n", " # we would return None for all values\n", " return None\n", "\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"Convert age value to continuous (float).\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \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", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip().lower()\n", " \n", " if \"female\" in value or \"f\" == value:\n", " return 0\n", " elif \"male\" in value or \"m\" == value:\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Check if trait data is available (trait_row is not None)\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort information for 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" ] }, { "cell_type": "markdown", "id": "83993654", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "afec298d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:11:35.465990Z", "iopub.status.busy": "2025-03-25T07:11:35.465887Z", "iopub.status.idle": "2025-03-25T07:11:35.737548Z", "shell.execute_reply": "2025-03-25T07:11:35.737163Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting gene data from matrix file:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully extracted gene data with 62738 rows\n", "First 20 gene IDs:\n", "Index(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',\n", " '14', '15', '16', '17', '18', '19', '20'],\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": "495ec315", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "57f7ce43", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:11:35.739413Z", "iopub.status.busy": "2025-03-25T07:11:35.739290Z", "iopub.status.idle": "2025-03-25T07:11:35.741248Z", "shell.execute_reply": "2025-03-25T07:11:35.740957Z" } }, "outputs": [], "source": [ "# Examining the gene identifiers from the previous output\n", "# The identifiers appear to be simple numeric values (1, 2, 3, etc.)\n", "# These are not standard human gene symbols which typically look like:\n", "# BRCA1, TP53, APOE, etc.\n", "\n", "# These numeric identifiers will need to be mapped to standard gene symbols\n", "# for meaningful analysis and interpretation\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "35f15361", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "ead402c5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:11:35.742906Z", "iopub.status.busy": "2025-03-25T07:11:35.742797Z", "iopub.status.idle": "2025-03-25T07:11:39.272775Z", "shell.execute_reply": "2025-03-25T07:11:39.272383Z" } }, "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 3074210 rows\n", "\n", "Gene annotation preview (first few rows):\n", "{'ID': ['1', '2', '3', '4', '5'], 'NAME': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'ASHG19A3A019218', 'ASHG19A3A052390'], 'ORF': [nan, nan, nan, 'DOPEY2', 'SCAMP2'], 'FINAL_SYMBOL': [nan, nan, nan, 'DOPEY2', 'SCAMP2'], 'FINAL_SOURCE': [nan, nan, nan, 'Gencode.V23', 'Gencode.V23'], 'FINAL_TYPE': [nan, nan, nan, 'coding', 'coding'], 'Ensembl.Symbol': [nan, nan, nan, 'DOPEY2', 'SCAMP2'], 'Ensembl.Gene': [nan, nan, nan, 'ENSG00000142197.12', 'ENSG00000140497.16'], 'Ensembl.Type': [nan, nan, nan, 'protein_coding', 'protein_coding'], 'chrom': [nan, nan, nan, 'chr21', 'chr15'], 'txEnd': [nan, nan, nan, 37666571.0, 75165670.0], 'txStart': [nan, nan, nan, 37536838.0, 75137196.0], 'SPOT_ID': ['CONTROL', 'CONTROL', 'CONTROL', 'ASHG19A3A019218', 'ASHG19A3A052390']}\n", "\n", "Column names in gene annotation data:\n", "['ID', 'NAME', 'ORF', 'FINAL_SYMBOL', 'FINAL_SOURCE', 'FINAL_TYPE', 'Ensembl.Symbol', 'Ensembl.Gene', 'Ensembl.Type', 'chrom', 'txEnd', 'txStart', 'SPOT_ID']\n", "\n", "The dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\n", "Example SPOT_ID format: CONTROL\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": "7fe3e245", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "24174b5d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:11:39.274584Z", "iopub.status.busy": "2025-03-25T07:11:39.274433Z", "iopub.status.idle": "2025-03-25T07:11:40.330217Z", "shell.execute_reply": "2025-03-25T07:11:40.329818Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating gene mapping from ID to gene symbols...\n", "Created mapping with 61332 entries\n", "Sample of mapping data:\n", " ID Gene\n", "3 4 DOPEY2\n", "4 5 SCAMP2\n", "5 6 uc.387\n", "6 7 lincRNA-SPATS2L\n", "7 8 VIL1\n", "\n", "Converting probe measurements to gene expression data...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully mapped probes to 37220 genes\n", "First few genes in the mapped data:\n", "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A3GALT2',\n", " 'A4GALT', 'A4GNT', 'AA155639'],\n", " dtype='object', name='Gene')\n", "\n", "Normalizing gene symbols...\n", "After normalization: 24360 genes\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved processed gene data to ../../output/preprocess/Intellectual_Disability/gene_data/GSE98697.csv\n" ] } ], "source": [ "# 1. Identify which columns to use for mapping\n", "# Based on the annotation preview, we need to map:\n", "# - 'ID' column from gene_annotation (numeric identifiers like '1', '2', '3') to the gene expression data index\n", "# - 'FINAL_SYMBOL' column contains gene symbols (like 'DOPEY2', 'SCAMP2')\n", "\n", "print(\"Creating gene mapping from ID to gene symbols...\")\n", "\n", "# 2. Create gene mapping dataframe\n", "mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='FINAL_SYMBOL')\n", "\n", "# Print some information about the mapping\n", "print(f\"Created mapping with {len(mapping_data)} entries\")\n", "print(\"Sample of mapping data:\")\n", "print(mapping_data.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n", "print(\"\\nConverting probe measurements to gene expression data...\")\n", "try:\n", " gene_data = apply_gene_mapping(gene_data, mapping_data)\n", " print(f\"Successfully mapped probes to {len(gene_data)} genes\")\n", " print(\"First few genes in the mapped data:\")\n", " print(gene_data.index[:10])\n", " \n", " # Normalize gene symbols to ensure consistency\n", " print(\"\\nNormalizing gene symbols...\")\n", " gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"After normalization: {len(gene_data)} genes\")\n", " \n", " # Save the processed 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\"Saved processed gene data to {out_gene_data_file}\")\n", " \n", "except Exception as e:\n", " print(f\"Error in gene mapping: {e}\")\n" ] }, { "cell_type": "markdown", "id": "362ea7da", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "2f788ec6", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:11:40.331996Z", "iopub.status.busy": "2025-03-25T07:11:40.331882Z", "iopub.status.idle": "2025-03-25T07:11:41.025195Z", "shell.execute_reply": "2025-03-25T07:11:41.024844Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalizing gene symbols in the expression data...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Intellectual_Disability/gene_data/GSE98697.csv\n", "Normalized gene data shape: (24360, 48)\n", "Cannot link data: either gene data or clinical data is unavailable.\n", "Abnormality detected in the cohort: GSE98697. Preprocessing failed.\n", "Dataset usability: False\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "print(\"Normalizing gene symbols in the expression data...\")\n", "try:\n", " # If previous steps have already loaded gene_data\n", " normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", " \n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", " \n", " # Save normalized gene data\n", " normalized_gene_data.to_csv(out_gene_data_file)\n", " print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", " print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n", "except Exception as e:\n", " print(f\"Error normalizing gene data: {e}\")\n", " is_gene_available = False\n", "\n", "# 2. Link clinical and genetic data\n", "if is_gene_available and 'clinical_df' in locals():\n", " print(\"\\nLinking clinical and genetic data...\")\n", " try:\n", " # Transpose normalized gene data to match clinical data format\n", " gene_data_t = normalized_gene_data.T\n", " \n", " # Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data_t)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " \n", " # 3. Handle missing values systematically\n", " print(\"\\nHandling missing values...\")\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 features are biased\n", " print(\"\\nChecking for bias in features...\")\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # 5. Final validation and save metadata\n", " print(\"\\nPerforming final validation...\")\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=\"\"\n", " )\n", " \n", " # 6. Save the 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.\")\n", " \n", " except Exception as e:\n", " print(f\"Error in data linking or processing: {e}\")\n", " is_usable = False\n", "else:\n", " print(\"Cannot link data: either gene data or clinical data is unavailable.\")\n", " # Create a minimal dataframe for validation purposes\n", " import pandas as pd\n", " linked_data = pd.DataFrame({trait: [None]})\n", " \n", " # Perform final validation with appropriate flags\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=True, # Not relevant since data isn't usable\n", " df=linked_data,\n", " note=\"Failed to link gene and clinical data.\"\n", " )\n", " print(f\"Dataset usability: {is_usable}\")" ] } ], "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 }