{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "dfc0a692", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:20:53.806659Z", "iopub.status.busy": "2025-03-25T06:20:53.806538Z", "iopub.status.idle": "2025-03-25T06:20:53.968115Z", "shell.execute_reply": "2025-03-25T06:20:53.967769Z" } }, "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 = \"Adrenocortical_Cancer\"\n", "cohort = \"GSE143383\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Adrenocortical_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Adrenocortical_Cancer/GSE143383\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Adrenocortical_Cancer/GSE143383.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Adrenocortical_Cancer/gene_data/GSE143383.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Adrenocortical_Cancer/clinical_data/GSE143383.csv\"\n", "json_path = \"../../output/preprocess/Adrenocortical_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "ec7d1466", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "4e3449e1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:20:53.969496Z", "iopub.status.busy": "2025-03-25T06:20:53.969362Z", "iopub.status.idle": "2025-03-25T06:20:54.123590Z", "shell.execute_reply": "2025-03-25T06:20:54.123264Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene expression analysis of metastatic adrenocortical tumors\"\n", "!Series_summary\t\"Background: Adrenocortical carcinoma (ACC) is a rare, often-aggressive neoplasm of the adrenal cortex, with a 14.5-month median overall survival. We asked whether tumors from patients with advanced or metastatic ACC would offer clues as to putative genes that might have critical roles in disease progression or in more aggressive disease biology. Methods: We conducted comprehensive genomic and expression analyses of 43 ACCs. Results: Copy number gains and losses matched that previously reported. We identified a median mutation rate of 3.38 per megabase (Mb), somewhat higher than in a previous study possibly related to the more advanced disease. The mutational signature was characterized by a predominance of C>T, C>A and T>C transitions. As in previously reports, only cancer genes TP53 (26%) and beta-catenin (CTNNB1, 14%) were mutated in more than 10% of samples. The TCGA-identified putative cancer genes MEN1 and PRKAR1A were found in low frequency – 4.7% and 2.3%, respectively. Most of the mutations were in genes not implicated in the etiology or maintenance of cancer. Specifically, amongst the 38 genes that were mutated in more than 9% of samples, only four were represented in Tier 1 of the 576 COSMIC Cancer Gene Census (CCGC). Thus, 82% of genes found to have mutations likely have no role in the etiology or biology of ACC; while the role of the other 18%, if any, remains to be proven. Finally, the transcript length for the 38 most frequently mutated genes in ACC is statistically longer than the average of all coding genes, raising the question of whether transcript length in part determined mutation probability. Conclusions: We conclude that the mutational and expression profiles of advanced and metastatic tumors is very similar to those from newly diagnosed patients –with very little in the way of genomic aberration to explain it. Our data and that in the previous analyses finds the rate of mutations in ACCs lower than that in other cancers and suggests an epigenetic basis for the disease should be the focus of future studies.\"\n", "!Series_summary\t\"The Affymetrix PrimeView platform was used for the gene expression profiling.\"\n", "!Series_overall_design\t\"Tumor samples embedded in OCT were sectioned, stained with hematoxylin and eosin and reviewed by a pathologist. RNA was extracted from 50-100 mg of tumor using the Qiagen miRNeasy Kit and then used for cDNA array analyses.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: M', 'gender: F', 'gender: unknown']}\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": "fd4ea54f", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "847ab34d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:20:54.124713Z", "iopub.status.busy": "2025-03-25T06:20:54.124610Z", "iopub.status.idle": "2025-03-25T06:20:54.144562Z", "shell.execute_reply": "2025-03-25T06:20:54.144253Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Error processing clinical data: [Errno 2] No such file or directory: '../../input/GEO/Adrenocortical_Cancer/GSE143383/clinical_data.csv'\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import json\n", "from typing import Callable, Dict, Any, Optional\n", "\n", "# 1. Gene Expression Data Availability\n", "# This dataset is about gene expression profiling using Affymetrix PrimeView platform\n", "# and the series summary mentions gene expression analysis\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "\n", "# 2.1 Data Availability\n", "# Looking at the sample characteristics dictionary, we only have gender information\n", "# There's no direct key for adrenocortical cancer trait or age\n", "trait_row = None\n", "age_row = None\n", "gender_row = 0 # Gender is available at key 0\n", "\n", "# 2.2 Data Type Conversion\n", "# Since trait_row is None, we don't need to define convert_trait\n", "# But we'll create it with a placeholder function to maintain code structure\n", "def convert_trait(value):\n", " return None\n", "\n", "# Since age_row is None, we don't need to define convert_age\n", "# But we'll create it with a placeholder function to maintain code structure\n", "def convert_age(value):\n", " return None\n", "\n", "def convert_gender(value):\n", " # Extract value after colon\n", " if ':' in value:\n", " gender = value.split(':', 1)[1].strip().lower()\n", " \n", " if gender == 'f':\n", " return 0 # Female\n", " elif gender == 'm':\n", " return 1 # Male\n", " else:\n", " return None # Unknown or other\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Trait data availability is determined by whether trait_row is None\n", "is_trait_available = trait_row is not None\n", "\n", "# Save the initial filtering 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'll skip this substep\n", "# However, we can still extract and save gender data if clinical_data is available\n", "try:\n", " clinical_data = pd.read_csv(os.path.join(in_cohort_dir, \"clinical_data.csv\"))\n", " \n", " if gender_row is not None:\n", " # We only have gender data\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=0, # Using a placeholder since trait data isn't available\n", " convert_trait=convert_trait, # Using a placeholder function\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the selected clinical data\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of selected clinical data:\")\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, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " else:\n", " print(\"No clinical features are available to extract.\")\n", "except Exception as e:\n", " print(f\"Error processing clinical data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "7b0d2cd0", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "ca3cf239", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:20:54.145674Z", "iopub.status.busy": "2025-03-25T06:20:54.145575Z", "iopub.status.idle": "2025-03-25T06:20:54.381975Z", "shell.execute_reply": "2025-03-25T06:20:54.381542Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 20 gene/probe identifiers:\n", "Index(['11715100_at', '11715101_s_at', '11715102_x_at', '11715103_x_at',\n", " '11715104_s_at', '11715105_at', '11715106_x_at', '11715107_s_at',\n", " '11715108_x_at', '11715109_at', '11715110_at', '11715111_s_at',\n", " '11715112_at', '11715113_x_at', '11715114_x_at', '11715115_s_at',\n", " '11715116_s_at', '11715117_x_at', '11715118_s_at', '11715119_s_at'],\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": "34416ff4", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "41f8d973", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:20:54.383477Z", "iopub.status.busy": "2025-03-25T06:20:54.383360Z", "iopub.status.idle": "2025-03-25T06:20:54.385238Z", "shell.execute_reply": "2025-03-25T06:20:54.384946Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers, these appear to be probe IDs from an Affymetrix microarray\n", "# (format like \"11715100_at\", \"11715101_s_at\")\n", "# These are not standard human gene symbols (like BRCA1, TP53) and will need to be mapped\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "98f22dff", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "acccd3c9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:20:54.386395Z", "iopub.status.busy": "2025-03-25T06:20:54.386291Z", "iopub.status.idle": "2025-03-25T06:20:59.879849Z", "shell.execute_reply": "2025-03-25T06:20:59.879481Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['11715100_at', '11715101_s_at', '11715102_x_at', '11715103_x_at', '11715104_s_at'], 'GeneChip Array': ['Human Genome PrimeView Array', 'Human Genome PrimeView Array', 'Human Genome PrimeView Array', 'Human Genome PrimeView Array', 'Human Genome PrimeView Array'], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': [40780.0, 40780.0, 40780.0, 40780.0, 40780.0], 'Sequence Type': ['Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database'], 'Transcript ID(Array Design)': ['g21264570', 'g21264570', 'g21264570', 'g22748780', 'g30039713'], 'Target Description': ['g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g22748780 /TID=g22748780 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g22748780 /REP_ORG=Homo sapiens', 'g30039713 /TID=g30039713 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g30039713 /REP_ORG=Homo sapiens'], 'GB_ACC': [nan, nan, nan, nan, nan], 'GI': [21264570.0, 21264570.0, 21264570.0, 22748780.0, 30039713.0], 'UniGene ID': ['Hs.247813', 'Hs.247813', 'Hs.247813', 'Hs.465643', 'Hs.352515'], 'Genome Version': ['February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)'], 'Alignments': ['chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr19:4639529-5145579 (+) // 48.53 // p13.3', 'chr17:72920369-72929640 (+) // 100.0 // q25.1'], 'Gene Title': ['histone cluster 1, H3g', 'histone cluster 1, H3g', 'histone cluster 1, H3g', 'tumor necrosis factor, alpha-induced protein 8-like 1', 'otopetrin 2'], 'Gene Symbol': ['HIST1H3G', 'HIST1H3G', 'HIST1H3G', 'TNFAIP8L1', 'OTOP2'], 'Chromosomal Location': ['chr6p21.3', 'chr6p21.3', 'chr6p21.3', 'chr19p13.3', 'chr17q25.1'], 'Unigene Cluster Type': ['full length', 'full length', 'full length', 'full length', 'full length'], 'Ensembl': ['ENSG00000248541', 'ENSG00000248541', 'ENSG00000248541', 'ENSG00000185361', 'ENSG00000183034'], 'Entrez Gene': ['8355', '8355', '8355', '126282', '92736'], 'SwissProt': ['P68431', 'P68431', 'P68431', 'Q8WVP5', 'Q7RTS6'], 'OMIM': ['602815', '602815', '602815', '---', '607827'], 'RefSeq Protein ID': ['NP_003525', 'NP_003525', 'NP_003525', 'NP_001161414 /// NP_689575', 'NP_835454'], 'RefSeq Transcript ID': ['NM_003534', 'NM_003534', 'NM_003534', 'NM_001167942 /// NM_152362', 'NM_178160'], 'SPOT_ID': [nan, nan, nan, nan, nan]}\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": "acd6f141", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "34d2c6a4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:20:59.881238Z", "iopub.status.busy": "2025-03-25T06:20:59.881116Z", "iopub.status.idle": "2025-03-25T06:21:00.175537Z", "shell.execute_reply": "2025-03-25T06:21:00.175171Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping dataframe shape: (49372, 2)\n", "First few rows of gene mapping:\n", " ID Gene\n", "0 11715100_at HIST1H3G\n", "1 11715101_s_at HIST1H3G\n", "2 11715102_x_at HIST1H3G\n", "3 11715103_x_at TNFAIP8L1\n", "4 11715104_s_at OTOP2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape after mapping: (19534, 63)\n", "First few gene symbols after mapping:\n", "Index(['A1BG', 'A1CF', 'A2LD1', 'A2M', 'A2ML1', 'A4GALT', 'A4GNT', 'AAA1',\n", " 'AAAS', 'AACS'],\n", " dtype='object', name='Gene')\n", "Preview of gene expression data:\n", " GSM4258059 GSM4258060 GSM4258061 GSM4258062 GSM4258063\n", "Gene \n", "A1BG 4.86208 5.81829 5.69429 5.99362 6.01689\n", "A1CF 8.17809 7.65680 8.57597 9.20387 9.12301\n", "A2LD1 4.19459 4.34588 4.45649 3.94573 4.17089\n", "A2M 11.48740 10.66540 12.94520 11.30090 12.05390\n", "A2ML1 6.95604 6.78831 7.57040 7.79159 7.42280\n" ] } ], "source": [ "# 1. Determine which columns contain probe IDs and gene symbols\n", "# From the annotation preview, we can see:\n", "# - 'ID' column contains the probe identifiers (e.g., '11715100_at')\n", "# - 'Gene Symbol' column contains the gene symbols (e.g., 'HIST1H3G')\n", "\n", "# 2. Create gene mapping DataFrame\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n", "print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\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 expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n", "print(\"First few gene symbols after mapping:\")\n", "print(gene_data.index[:10])\n", "\n", "# Preview the first few rows and columns of the gene expression data\n", "print(\"Preview of gene expression data:\")\n", "first_genes = gene_data.index[:5]\n", "first_samples = gene_data.columns[:5]\n", "print(gene_data.loc[first_genes, first_samples])\n" ] }, { "cell_type": "markdown", "id": "815dd5ce", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "45210003", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:21:00.177383Z", "iopub.status.busy": "2025-03-25T06:21:00.177267Z", "iopub.status.idle": "2025-03-25T06:21:00.914743Z", "shell.execute_reply": "2025-03-25T06:21:00.914425Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalizing gene symbols...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (19326, 63)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene expression data saved to ../../output/preprocess/Adrenocortical_Cancer/gene_data/GSE143383.csv\n", "Creating clinical data with available gender information...\n", "Clinical data shape: (63, 1)\n", "Clinical data saved to ../../output/preprocess/Adrenocortical_Cancer/clinical_data/GSE143383.csv\n", "This dataset doesn't contain trait information for Adrenocortical_Cancer.\n", "Abnormality detected in the cohort: GSE143383. Preprocessing failed.\n", "Dataset usability: False\n", "Dataset is not usable for trait-gene association studies due to missing trait information.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "print(\"Normalizing gene symbols...\")\n", "try:\n", " # Normalize gene symbols using the NCBI Gene database\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\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 expression data saved to {out_gene_data_file}\")\n", "except Exception as e:\n", " print(f\"Error during gene normalization: {e}\")\n", " # If normalization fails, use the original gene data\n", " print(\"Using original 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\"Original gene expression data saved to {out_gene_data_file}\")\n", "\n", "# 2. Create a basic clinical dataframe with gender information\n", "# Since we identified in Step 2 that only gender information is available\n", "print(\"Creating clinical data with available gender information...\")\n", "# First, get the sample identifiers from gene_data columns\n", "sample_ids = gene_data.columns.tolist()\n", "\n", "# Create a DataFrame for gender using the clinical data we collected earlier\n", "gender_row = 0 # As identified in Step 2\n", "gender_data = None\n", "\n", "try:\n", " gender_data = get_feature_data(clinical_data, gender_row, 'Gender', convert_gender)\n", " # Convert to a DataFrame with samples as rows\n", " clinical_df = gender_data.T\n", " print(f\"Clinical data shape: {clinical_df.shape}\")\n", "except Exception as e:\n", " print(f\"Error extracting gender data: {e}\")\n", " # Create an empty DataFrame if gender extraction fails\n", " clinical_df = pd.DataFrame(index=sample_ids)\n", " print(\"Created empty clinical dataframe.\")\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. Since we don't have trait data (trait_row is None as per Step 2), we can't create a proper linked dataset\n", "# We'll mark the dataset as not usable for trait-gene association studies\n", "print(\"This dataset doesn't contain trait information for Adrenocortical_Cancer.\")\n", "\n", "# 4. Validate and save cohort information\n", "note = \"Dataset contains gene expression data from adrenocortical tumors, but lacks a proper control group \" \\\n", " \"and trait classification. Only gender information is available as a clinical feature.\"\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=True, # Set to True since we don't have a trait to analyze\n", " df=clinical_df, # Just pass the clinical data since we don't have a properly linked dataset\n", " note=note\n", ")\n", "\n", "print(f\"Dataset usability: {is_usable}\")\n", "\n", "# 5. Since the dataset isn't usable for trait-gene association studies (no trait data),\n", "# we won't create or save a linked dataset\n", "if is_usable:\n", " # This condition won't be met based on our assessment, but included for completeness\n", " # We would need to transpose gene_data and concatenate with clinical_df\n", " gene_data_t = gene_data.T\n", " linked_data = pd.concat([clinical_df, gene_data_t], axis=1)\n", " \n", " # Handle missing values and check for bias\n", " linked_data = handle_missing_values(linked_data, trait)\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # Save the linked data\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Dataset is not usable for trait-gene association studies due to missing trait information.\")" ] } ], "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 }