{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0e5f2abf", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:04.711942Z", "iopub.status.busy": "2025-03-25T05:14:04.711438Z", "iopub.status.idle": "2025-03-25T05:14:04.885534Z", "shell.execute_reply": "2025-03-25T05:14:04.885179Z" } }, "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 = \"Esophageal_Cancer\"\n", "cohort = \"GSE77790\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Esophageal_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Esophageal_Cancer/GSE77790\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Esophageal_Cancer/GSE77790.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Esophageal_Cancer/gene_data/GSE77790.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Esophageal_Cancer/clinical_data/GSE77790.csv\"\n", "json_path = \"../../output/preprocess/Esophageal_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "8810f510", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "18729a2a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:04.887218Z", "iopub.status.busy": "2025-03-25T05:14:04.887042Z", "iopub.status.idle": "2025-03-25T05:14:05.070065Z", "shell.execute_reply": "2025-03-25T05:14:05.069711Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Differentially expressed genes after miRNA or siRNA transfection in human cancer cell lines II\"\n", "!Series_summary\t\"To identify differentially expressed genes by anti cancer treatments (microRNAs or siRNAs) in human cancer, several cell lines (pancreatic cancer, esophageal cancer, bladder cancer, prostate cancer, renal cell carcinoma and lung squamous cell carcinoma) were subjected to Agilent whole genome microarrays.\"\n", "!Series_overall_design\t\"Human cell lines (Panc-1, sw1990, TE8, TE9, A549, MRC-5, BOY, T24, PC3, C4-2, 786-O, A-498 and EBC-1) were treated with miRNAs (miR-375, miR-29a, miR-26a, miR-145-5p, miR-145-3p, miR-218, miR-320a), siRNAs (si-MMP11, si-LAMP1, si-LOXL2, si-PLOD2, si-UHRF1, and si-FOXM1).\"\n", "Sample Characteristics Dictionary:\n", "{0: ['cell line: EBC-1', 'cell line: C4-2', 'cell line: PC3', 'cell line: A-498', 'cell line: 786-O', 'cell line: BOY', 'cell line: T24', 'cell line: A549', 'cell line: MRC-5', 'cell line: Panc-1', 'cell line: sw1990', 'cell line: TE8', 'cell line: TE9'], 1: ['cell type: lung squamous cell carcinoma', 'cell type: prostate cancer', 'cell type: bladder cancer', 'cell type: renal cell carcinoma', 'cell type: lung fibroblast', 'cell type: pancreatic cancer', 'cell type: esophageal cancer'], 2: ['transfection: no transfection']}\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": "53d60589", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "f56bf704", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:05.071289Z", "iopub.status.busy": "2025-03-25T05:14:05.071168Z", "iopub.status.idle": "2025-03-25T05:14:05.078318Z", "shell.execute_reply": "2025-03-25T05:14:05.078014Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Data Preview:\n", "{0: [0.0]}\n", "Clinical data saved to ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE77790.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Analysis\n", "# Based on the background information, this dataset appears to be gene expression data\n", "# from microarray analysis, which is suitable for our study.\n", "is_gene_available = True\n", "\n", "# 2. Clinical Features Analysis\n", "# 2.1 Data Availability\n", "# For trait (esophageal cancer), we can use the cell type information (row 1)\n", "# For age and gender, there's no information in the sample characteristics\n", "trait_row = 1\n", "age_row = None # No age data available\n", "gender_row = None # No gender data available\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"Convert cell type to binary trait (esophageal cancer or not)\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " \n", " # Check if it's esophageal cancer\n", " if 'esophageal cancer' in value:\n", " return 1\n", " else:\n", " return 0\n", "\n", "def convert_age(value):\n", " # Not used, as age data is not available\n", " return None\n", "\n", "def convert_gender(value):\n", " # Not used, as gender data is not available\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", "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", "# Only execute if trait_row is not None\n", "if trait_row is not None:\n", " # Create a DataFrame from the sample characteristics dictionary provided in the previous output\n", " sample_characteristics_dict = {\n", " 0: ['cell line: EBC-1', 'cell line: C4-2', 'cell line: PC3', 'cell line: A-498', \n", " 'cell line: 786-O', 'cell line: BOY', 'cell line: T24', 'cell line: A549', \n", " 'cell line: MRC-5', 'cell line: Panc-1', 'cell line: sw1990', 'cell line: TE8', \n", " 'cell line: TE9'],\n", " 1: ['cell type: lung squamous cell carcinoma', 'cell type: prostate cancer', \n", " 'cell type: bladder cancer', 'cell type: renal cell carcinoma', \n", " 'cell type: lung fibroblast', 'cell type: pancreatic cancer', \n", " 'cell type: esophageal cancer'],\n", " 2: ['transfection: no transfection']\n", " }\n", " \n", " import pandas as pd\n", " # Creating the clinical_data DataFrame from the dictionary\n", " # We need to transpose the data to get samples as rows and features as columns\n", " clinical_data = pd.DataFrame({col: values for col, values in enumerate(list(zip(*[values for values in sample_characteristics_dict.values()])))})\n", " \n", " # Extract clinical 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 data\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical Data Preview:\")\n", " print(preview)\n", " \n", " # Save clinical features 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", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "0d658b99", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "80cef9dc", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:05.079492Z", "iopub.status.busy": "2025-03-25T05:14:05.079380Z", "iopub.status.idle": "2025-03-25T05:14:05.372105Z", "shell.execute_reply": "2025-03-25T05:14:05.371720Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 81\n", "Header line: \"ID_REF\"\t\"GSM2059404\"\t\"GSM2059405\"\t\"GSM2059406\"\t\"GSM2059407\"\t\"GSM2059408\"\t\"GSM2059409\"\t\"GSM2059410\"\t\"GSM2059411\"\t\"GSM2059412\"\t\"GSM2059413\"\t\"GSM2059414\"\t\"GSM2059415\"\t\"GSM2059416\"\t\"GSM2059417\"\t\"GSM2059418\"\t\"GSM2059419\"\t\"GSM2059420\"\t\"GSM2059421\"\t\"GSM2059422\"\t\"GSM2059423\"\t\"GSM2059424\"\t\"GSM2059425\"\t\"GSM2059426\"\t\"GSM2059427\"\t\"GSM2059428\"\t\"GSM2059429\"\t\"GSM2059430\"\t\"GSM2059431\"\t\"GSM2059432\"\t\"GSM2059433\"\t\"GSM2059434\"\t\"GSM2059435\"\n", "First data line: 1\t-1.492678368e-001\t9.385965497e-002\t-8.941784384e-002\t-1.349943700e-002\t-1.599001264e-002\t-8.062755446e-002\t-5.685066626e-002\t3.483449753e-002\t1.110190735e-002\t-1.109288193e-002\t-3.863425129e-002\t-4.031110222e-002\t3.436493922e-002\t6.242996551e-002\t-3.869467488e-002\t-2.818536224e-004\t-6.648348866e-002\t-7.110430995e-002\t-1.601804138e-003\t-6.578105194e-002\t-9.610465045e-004\t3.293553993e-002\t5.540124407e-002\t-7.305230142e-002\t-1.253722506e-002\t-6.620679603e-003\t-7.651308691e-002\t-5.726181154e-002\t-2.069165415e-002\t9.842492290e-003\t4.916461191e-002\t3.215693397e-002\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "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" ] } ], "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. First, let's examine the structure of the matrix file to understand its format\n", "import gzip\n", "\n", "# Peek at the first few lines of the file to understand its structure\n", "with gzip.open(matrix_file, 'rt') as file:\n", " # Read first 100 lines to find the header structure\n", " for i, line in enumerate(file):\n", " if '!series_matrix_table_begin' in line:\n", " print(f\"Found data marker at line {i}\")\n", " # Read the next line which should be the header\n", " header_line = next(file)\n", " print(f\"Header line: {header_line.strip()}\")\n", " # And the first data line\n", " first_data_line = next(file)\n", " print(f\"First data line: {first_data_line.strip()}\")\n", " break\n", " if i > 100: # Limit search to first 100 lines\n", " print(\"Matrix table marker not found in first 100 lines\")\n", " break\n", "\n", "# 3. Now try to get the genetic data with better error handling\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(gene_data.index[:20])\n", "except KeyError as e:\n", " print(f\"KeyError: {e}\")\n", " \n", " # Alternative approach: manually extract the data\n", " print(\"\\nTrying alternative approach to read the gene data:\")\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Find the start of the data\n", " for line in file:\n", " if '!series_matrix_table_begin' in line:\n", " break\n", " \n", " # Read the headers and data\n", " import pandas as pd\n", " df = pd.read_csv(file, sep='\\t', index_col=0)\n", " print(f\"Column names: {df.columns[:5]}\")\n", " print(f\"First 20 row IDs: {df.index[:20]}\")\n", " gene_data = df\n" ] }, { "cell_type": "markdown", "id": "47b4d448", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "c4ac7618", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:05.373458Z", "iopub.status.busy": "2025-03-25T05:14:05.373328Z", "iopub.status.idle": "2025-03-25T05:14:05.375249Z", "shell.execute_reply": "2025-03-25T05:14:05.374960Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers in the gene expression data\n", "# The identifiers are numerical (1, 2, 3, etc.) which are not standard human gene symbols\n", "# These appear to be probe IDs that need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "a849f4fb", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "f55c9aa7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:05.376402Z", "iopub.status.busy": "2025-03-25T05:14:05.376289Z", "iopub.status.idle": "2025-03-25T05:14:05.984312Z", "shell.execute_reply": "2025-03-25T05:14:05.983897Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Examining SOFT file structure:\n", "Line 0: ^DATABASE = GeoMiame\n", "Line 1: !Database_name = Gene Expression Omnibus (GEO)\n", "Line 2: !Database_institute = NCBI NLM NIH\n", "Line 3: !Database_web_link = http://www.ncbi.nlm.nih.gov/geo\n", "Line 4: !Database_email = geo@ncbi.nlm.nih.gov\n", "Line 5: ^SERIES = GSE77790\n", "Line 6: !Series_title = Differentially expressed genes after miRNA or siRNA transfection in human cancer cell lines II\n", "Line 7: !Series_geo_accession = GSE77790\n", "Line 8: !Series_status = Public on Apr 13 2016\n", "Line 9: !Series_submission_date = Feb 10 2016\n", "Line 10: !Series_last_update_date = Oct 07 2019\n", "Line 11: !Series_pubmed_id = 27633630\n", "Line 12: !Series_pubmed_id = 27862697\n", "Line 13: !Series_pubmed_id = 27072587\n", "Line 14: !Series_pubmed_id = 27779648\n", "Line 15: !Series_pubmed_id = 27765924\n", "Line 16: !Series_pubmed_id = 29050264\n", "Line 17: !Series_summary = To identify differentially expressed genes by anti cancer treatments (microRNAs or siRNAs) in human cancer, several cell lines (pancreatic cancer, esophageal cancer, bladder cancer, prostate cancer, renal cell carcinoma and lung squamous cell carcinoma) were subjected to Agilent whole genome microarrays.\n", "Line 18: !Series_overall_design = Human cell lines (Panc-1, sw1990, TE8, TE9, A549, MRC-5, BOY, T24, PC3, C4-2, 786-O, A-498 and EBC-1) were treated with miRNAs (miR-375, miR-29a, miR-26a, miR-145-5p, miR-145-3p, miR-218, miR-320a), siRNAs (si-MMP11, si-LAMP1, si-LOXL2, si-PLOD2, si-UHRF1, and si-FOXM1).\n", "Line 19: !Series_type = Expression profiling by array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': [1, 2, 3, 4, 5], 'COL': [192, 192, 192, 192, 192], 'ROW': [328, 326, 324, 322, 320], 'NAME': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872'], 'SPOT_ID': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, nan, 'NM_001105533'], 'GB_ACC': [nan, nan, nan, nan, 'NM_001105533'], 'LOCUSLINK_ID': [nan, nan, nan, nan, 79974.0], 'GENE_SYMBOL': [nan, nan, nan, nan, 'CPED1'], 'GENE_NAME': [nan, nan, nan, nan, 'cadherin-like and PC-esterase domain containing 1'], 'UNIGENE_ID': [nan, nan, nan, nan, 'Hs.189652'], 'ENSEMBL_ID': [nan, nan, nan, nan, nan], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': [nan, nan, nan, nan, 'ref|NM_001105533|gb|AK025639|gb|BC030538|tc|THC2601673'], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, 'unmapped', 'chr7:120901888-120901947'], 'CYTOBAND': [nan, nan, nan, nan, 'hs|7q31.31'], 'DESCRIPTION': [nan, nan, nan, nan, 'Homo sapiens cadherin-like and PC-esterase domain containing 1 (CPED1), transcript variant 2, mRNA [NM_001105533]'], 'GO_ID': [nan, nan, nan, nan, 'GO:0005783(endoplasmic reticulum)'], 'SEQUENCE': [nan, nan, nan, 'AATACATGTTTTGGTAAACACTCGGTCAGAGCACCCTCTTTCTGTGGAATCAGACTGGCA', 'GCTTATCTCACCTAATACAGGGACTATGCAACCAAGAAACTGGAAATAAAAACAAAGATA'], 'SPOT_ID.1': [nan, nan, nan, nan, nan]}\n" ] } ], "source": [ "# 1. Let's first examine the structure of the SOFT file before trying to parse it\n", "import gzip\n", "\n", "# Look at the first few lines of the SOFT file to understand its structure\n", "print(\"Examining SOFT file structure:\")\n", "try:\n", " with gzip.open(soft_file, 'rt') as file:\n", " # Read first 20 lines to understand the file structure\n", " for i, line in enumerate(file):\n", " if i < 20:\n", " print(f\"Line {i}: {line.strip()}\")\n", " else:\n", " break\n", "except Exception as e:\n", " print(f\"Error reading SOFT file: {e}\")\n", "\n", "# 2. Now let's try a more robust approach to extract the gene annotation\n", "# Instead of using the library function which failed, we'll implement a custom approach\n", "try:\n", " # First, look for the platform section which contains gene annotation\n", " platform_data = []\n", " with gzip.open(soft_file, 'rt') as file:\n", " in_platform_section = False\n", " for line in file:\n", " if line.startswith('^PLATFORM'):\n", " in_platform_section = True\n", " continue\n", " if in_platform_section and line.startswith('!platform_table_begin'):\n", " # Next line should be the header\n", " header = next(file).strip()\n", " platform_data.append(header)\n", " # Read until the end of the platform table\n", " for table_line in file:\n", " if table_line.startswith('!platform_table_end'):\n", " break\n", " platform_data.append(table_line.strip())\n", " break\n", " \n", " # If we found platform data, convert it to a DataFrame\n", " if platform_data:\n", " import pandas as pd\n", " import io\n", " platform_text = '\\n'.join(platform_data)\n", " gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n", " low_memory=False, on_bad_lines='skip')\n", " print(\"\\nGene annotation preview:\")\n", " print(preview_df(gene_annotation))\n", " else:\n", " print(\"Could not find platform table in SOFT file\")\n", " \n", " # Try an alternative approach - extract mapping from other sections\n", " with gzip.open(soft_file, 'rt') as file:\n", " for line in file:\n", " if 'ANNOTATION information' in line or 'annotation information' in line:\n", " print(f\"Found annotation information: {line.strip()}\")\n", " if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n", " print(f\"Platform title: {line.strip()}\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing gene annotation: {e}\")\n" ] }, { "cell_type": "markdown", "id": "11b70f6c", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "a3239666", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:05.985608Z", "iopub.status.busy": "2025-03-25T05:14:05.985480Z", "iopub.status.idle": "2025-03-25T05:14:06.140052Z", "shell.execute_reply": "2025-03-25T05:14:06.139650Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping preview:\n", "{'ID': ['5', '6', '7', '8', '12'], 'Gene': ['CPED1', 'BCOR', 'CHAC2', 'IFI30', 'GPR146']}\n", "\n", "Gene expression data after mapping:\n", "Number of genes: 29222\n", "Number of samples: 32\n", "Sample of first few genes:\n", " GSM2059404 GSM2059405 GSM2059406\n", "Gene \n", "A1BG -0.026845 0.292602 -0.127231\n", "A1BG-AS1 0.000000 0.000000 0.000000\n", "A1CF -0.003664 0.000000 0.000000\n", "A1CF-2 0.000000 0.000000 0.000000\n", "A1CF-3 0.086574 0.000000 0.000000\n" ] } ], "source": [ "# 1. Determine which columns contain the identifiers and gene symbols\n", "# From previous output, we can see:\n", "# - 'ID' column contains numeric identifiers matching our gene expression data\n", "# - 'GENE_SYMBOL' column contains the gene symbols we need\n", "\n", "# Create a mapping dataframe with the identifier and gene symbol columns\n", "gene_mapping = get_gene_mapping(\n", " annotation=gene_annotation,\n", " prob_col='ID',\n", " gene_col='GENE_SYMBOL'\n", ")\n", "\n", "# Preview the mapping\n", "print(\"Gene mapping preview:\")\n", "print(preview_df(gene_mapping))\n", "\n", "# 2. Apply the gene mapping to convert probe-level expression to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# Preview the gene expression data after mapping\n", "print(\"\\nGene expression data after mapping:\")\n", "print(f\"Number of genes: {len(gene_data)}\")\n", "print(f\"Number of samples: {len(gene_data.columns)}\")\n", "print(\"Sample of first few genes:\")\n", "print(gene_data.head(5).iloc[:, :3]) # Show first 5 genes, first 3 samples\n" ] }, { "cell_type": "markdown", "id": "a5bba099", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "94d5a16a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:14:06.141435Z", "iopub.status.busy": "2025-03-25T05:14:06.141317Z", "iopub.status.idle": "2025-03-25T05:14:13.254506Z", "shell.execute_reply": "2025-03-25T05:14:13.254161Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (20778, 32)\n", "First few genes with their expression values after normalization:\n", " GSM2059404 GSM2059405 GSM2059406 GSM2059407 GSM2059408 \\\n", "Gene \n", "A1BG -0.026845 0.292602 -0.127231 -0.125141 -0.076007 \n", "A1BG-AS1 0.000000 0.000000 0.000000 0.251289 0.082415 \n", "A1CF -0.003664 0.000000 0.000000 0.000000 0.000000 \n", "A2M 0.103392 0.000000 -0.070657 0.483920 -0.159715 \n", "A2M-AS1 -0.022907 -0.019728 -0.104097 -0.203670 -0.555398 \n", "\n", " GSM2059409 GSM2059410 GSM2059411 GSM2059412 GSM2059413 ... \\\n", "Gene ... \n", "A1BG -0.009207 -0.082495 0.189666 -0.237983 -0.006939 ... \n", "A1BG-AS1 0.065617 0.000000 0.000000 -0.050387 -0.103464 ... \n", "A1CF 0.000000 -0.243397 0.000000 0.000000 0.000000 ... \n", "A2M 0.173058 -0.342849 0.435977 0.523215 0.318503 ... \n", "A2M-AS1 -0.335948 0.189627 0.046185 -0.490978 0.251301 ... \n", "\n", " GSM2059426 GSM2059427 GSM2059428 GSM2059429 GSM2059430 \\\n", "Gene \n", "A1BG -0.058951 -0.083353 -0.207919 -0.198724 -0.385907 \n", "A1BG-AS1 0.120403 0.007354 -0.098147 -0.032209 -0.106467 \n", "A1CF 0.000000 0.000000 0.000000 0.000000 -0.146621 \n", "A2M 0.000000 0.000000 0.000000 0.000000 0.009201 \n", "A2M-AS1 0.344190 -0.187586 0.010471 -0.310572 -0.194673 \n", "\n", " GSM2059431 GSM2059432 GSM2059433 GSM2059434 GSM2059435 \n", "Gene \n", "A1BG -0.275737 0.065018 0.106474 -0.202607 0.198972 \n", "A1BG-AS1 0.029031 -0.058106 0.063948 0.053285 0.147985 \n", "A1CF 0.000000 0.372980 -0.050298 -0.307278 -0.127532 \n", "A2M 0.157753 0.614686 0.000000 -0.160533 -0.044805 \n", "A2M-AS1 -0.230303 -0.125125 0.013175 0.209994 -0.157355 \n", "\n", "[5 rows x 32 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Esophageal_Cancer/gene_data/GSE77790.csv\n", "Raw clinical data shape: (3, 33)\n", "Clinical features:\n", " GSM2059404 GSM2059405 GSM2059406 GSM2059407 GSM2059408 \\\n", "Esophageal_Cancer 0.0 0.0 0.0 0.0 0.0 \n", "\n", " GSM2059409 GSM2059410 GSM2059411 GSM2059412 GSM2059413 \\\n", "Esophageal_Cancer 0.0 0.0 0.0 0.0 0.0 \n", "\n", " ... GSM2059426 GSM2059427 GSM2059428 GSM2059429 \\\n", "Esophageal_Cancer ... 0.0 0.0 0.0 0.0 \n", "\n", " GSM2059430 GSM2059431 GSM2059432 GSM2059433 GSM2059434 \\\n", "Esophageal_Cancer 1.0 1.0 0.0 0.0 0.0 \n", "\n", " GSM2059435 \n", "Esophageal_Cancer 0.0 \n", "\n", "[1 rows x 32 columns]\n", "Clinical features saved to ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE77790.csv\n", "Linked data shape: (32, 20779)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Esophageal_Cancer A1BG A1BG-AS1 A1CF A2M\n", "GSM2059404 0.0 -0.026845 0.000000 -0.003664 0.103392\n", "GSM2059405 0.0 0.292602 0.000000 0.000000 0.000000\n", "GSM2059406 0.0 -0.127231 0.000000 0.000000 -0.070657\n", "GSM2059407 0.0 -0.125141 0.251289 0.000000 0.483920\n", "GSM2059408 0.0 -0.076007 0.082415 0.000000 -0.159715\n", "Missing values before handling:\n", " Trait (Esophageal_Cancer) missing: 0 out of 32\n", " Genes with >20% missing: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Samples with >5% missing genes: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (32, 20779)\n", "For the feature 'Esophageal_Cancer', the least common label is '1.0' with 2 occurrences. This represents 6.25% of the dataset.\n", "The distribution of the feature 'Esophageal_Cancer' in this dataset is severely biased.\n", "\n", "Data was determined to be unusable or empty and was not saved\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n", "print(\"First few genes with their expression values after normalization:\")\n", "print(normalized_gene_data.head())\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 data saved to {out_gene_data_file}\")\n", "\n", "# 2. Check if trait data is available before proceeding with clinical data extraction\n", "if trait_row is None:\n", " print(\"Trait row is None. Cannot extract trait information from clinical data.\")\n", " # Create an empty dataframe for clinical features\n", " clinical_features = pd.DataFrame()\n", " \n", " # Create an empty dataframe for linked data\n", " linked_data = pd.DataFrame()\n", " \n", " # Validate and save cohort info\n", " 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=False, # Trait data is not available\n", " is_biased=True, # Not applicable but required\n", " df=pd.DataFrame(), # Empty dataframe\n", " note=\"Dataset contains gene expression data but lacks clear trait indicators for Duchenne Muscular Dystrophy status.\"\n", " )\n", " print(\"Data was determined to be unusable due to missing trait indicators and was not saved\")\n", "else:\n", " try:\n", " # Get the file paths for the matrix file to extract clinical data\n", " _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", " \n", " # Get raw clinical data from the matrix file\n", " _, clinical_raw = get_background_and_clinical_data(matrix_file)\n", " \n", " # Verify clinical data structure\n", " print(\"Raw clinical data shape:\", clinical_raw.shape)\n", " \n", " # Extract clinical features using the defined conversion functions\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_raw,\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", " print(\"Clinical features:\")\n", " print(clinical_features)\n", " \n", " # Save clinical features to file\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n", " \n", " # 3. Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " print(\"Linked data preview (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # 4. Handle missing values\n", " print(\"Missing values before handling:\")\n", " print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n", " if 'Age' in linked_data.columns:\n", " print(f\" Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n", " if 'Gender' in linked_data.columns:\n", " print(f\" Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n", " \n", " gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n", " print(f\" Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n", " print(f\" Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n", " \n", " cleaned_data = handle_missing_values(linked_data, trait)\n", " print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n", " \n", " # 5. Evaluate bias in trait and demographic features\n", " is_trait_biased = False\n", " if len(cleaned_data) > 0:\n", " trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n", " is_trait_biased = trait_biased\n", " else:\n", " print(\"No data remains after handling missing values.\")\n", " is_trait_biased = True\n", " \n", " # 6. Final validation and save\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=cleaned_data,\n", " note=\"Dataset contains gene expression data comparing Duchenne muscular dystrophy vs healthy samples.\"\n", " )\n", " \n", " # 7. Save if usable\n", " if is_usable and len(cleaned_data) > 0:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " cleaned_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(\"Data was determined to be unusable or empty and was not saved\")\n", " \n", " except Exception as e:\n", " print(f\"Error processing data: {e}\")\n", " # Handle the error case by still recording cohort info\n", " 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=False, # Mark as not available due to processing issues\n", " is_biased=True, \n", " df=pd.DataFrame(), # Empty dataframe\n", " note=f\"Error processing data: {str(e)}\"\n", " )\n", " print(\"Data was determined to be unusable and was 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 }