{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "f64b214a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:17:51.949571Z", "iopub.status.busy": "2025-03-25T07:17:51.949343Z", "iopub.status.idle": "2025-03-25T07:17:52.117761Z", "shell.execute_reply": "2025-03-25T07:17:52.117414Z" } }, "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 = \"Kidney_Clear_Cell_Carcinoma\"\n", "cohort = \"GSE127136\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Kidney_Clear_Cell_Carcinoma\"\n", "in_cohort_dir = \"../../input/GEO/Kidney_Clear_Cell_Carcinoma/GSE127136\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Kidney_Clear_Cell_Carcinoma/GSE127136.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Kidney_Clear_Cell_Carcinoma/gene_data/GSE127136.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE127136.csv\"\n", "json_path = \"../../output/preprocess/Kidney_Clear_Cell_Carcinoma/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "677661c0", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "ce2c2bef", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:17:52.119226Z", "iopub.status.busy": "2025-03-25T07:17:52.119085Z", "iopub.status.idle": "2025-03-25T07:17:52.253208Z", "shell.execute_reply": "2025-03-25T07:17:52.252814Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Single-cell RNA-seq profiling reveals novel insights in immune-complex deposition and epithelium transition in IgA nephropathy\"\n", "!Series_summary\t\"IgA nephropathy represents the most prevalent chronic nephrosis worldwide. However, pathogenesis about IgA deposition and end-stage renal failure is still not well defined. Using single-cell RNA-seq, we identified the mesangial membrane receptor for IgA, which collaborates with increased extracellular matrix proteins and protease inhibitor to facilitate IgA deposition. Meanwhile, cell-cell interaction analysis revealed increased communications between mesangium and other cell types, uncovering how morbidity inside glomerulus spreads to whole kidney, which results in the genetic changes of kidney resident immune cells. Prominent interaction decreasing in intercalated cells leads to the discovery of a transitional cell type, which exhibited significant EMT and fibrosis features. Our work comprehensively characterized the pathological mesangial signatures, highlighting the step-by-step pathogenic process of IgA nephropathy from mesangium to epithelium.\"\n", "!Series_overall_design\t\"In this study, we collected single cells from 13 IgAN patients’ renal biopsies and normal renal cells from 6 kidney cancer patients’ paracancerous tissues. As glomerulus are difficult to digest, we separately dissociated the glomerulus and the rest renal tissues. We applied CD326+ and CD14+ MACS to capture epithelium and macrophages, to cover the entire renal cell types, negative selected cells from MACS were also collected. Meanwhile, we isolated monocytes from 5 of the 13 IgAN patients and another 5 normal persons’ peripheral blood using CD14+ MACS\"\n", "!Series_overall_design\t\"\"\n", "!Series_overall_design\t\"**Submitter declares that the raw data have been deposited in the Genome Sequence Archive for Human (https://bigd.big.ac.cn/gsa-human/) under submission number PRJCA003506.**\"\n", "Sample Characteristics Dictionary:\n", "{0: ['patients: IgAN_01', 'patients: IgAN_06', 'patients: IgAN_07', 'patients: IgAN_09', 'patients: IgAN_10', 'patients: IgAN_11', 'patients: IgAN_12', 'patients: IgAN_15', 'patients: IgAN_16', 'patients: IgAN_17', 'patients: IgAN_18', 'patients: IgAN_19', 'patients: IgAN_20', 'patients: NM_01', 'patients: NM_02', 'patients: NM_03', 'patients: NM_07', 'patients: NM_08', 'patients: NM_09', 'patients: PBM_IgAN_10', 'patients: PBM_IgAN_12', 'patients: PBM_IgAN_17', 'patients: PBM_IgAN_19', 'patients: PBM_IgAN_20', 'patients: PBM_NM_01', 'patients: PBM_NM_02', 'patients: PBM_NM_03', 'patients: PBM_NM_04', 'patients: PBM_NM_05'], 1: ['disease state: IgAN', 'disease state: kidney cancer', 'disease state: normal'], 2: ['tissue: renal biopsies', 'tissue: paracancerous tissues', 'cell type: monocytes']}\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": "57508e84", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "929a759f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:17:52.254845Z", "iopub.status.busy": "2025-03-25T07:17:52.254697Z", "iopub.status.idle": "2025-03-25T07:17:52.366580Z", "shell.execute_reply": "2025-03-25T07:17:52.366249Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of selected clinical features:\n", "{0: [nan], 1: [1.0], 2: [nan]}\n", "Clinical data saved to ../../output/preprocess/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE127136.csv\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import numpy as np\n", "from typing import Optional, Dict, Any, Callable\n", "\n", "# 1. Determine gene expression data availability\n", "# This is a single-cell RNA-seq dataset, which contains gene expression data\n", "is_gene_available = True\n", "\n", "# 2. Determine variable availability and data type conversion\n", "# 2.1 Data Availability\n", "# From the sample characteristics dictionary, we need to analyze what's available\n", "\n", "# For trait (Kidney Clear Cell Carcinoma vs normal control):\n", "# Looking at key 1: 'disease state: IgAN', 'disease state: kidney cancer', 'disease state: normal'\n", "trait_row = 1 # Disease state information is in row 1\n", "\n", "# For age: There's no age information 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", "def convert_trait(value):\n", " \"\"\"Convert trait values to binary format (0 for normal/control, 1 for disease)\"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary (1 for kidney cancer, 0 for normal)\n", " if 'kidney cancer' in value.lower():\n", " return 1 # Kidney Cancer (Clear Cell Carcinoma)\n", " elif 'normal' in value.lower():\n", " return 0 # Normal control\n", " elif 'igan' in value.lower():\n", " return None # Not considering IgAN samples\n", " else:\n", " return None # Unknown value\n", "\n", "# Age conversion function (not used but defined for completeness)\n", "def convert_age(value):\n", " \"\"\"Convert age values to continuous format\"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \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", "# Gender conversion function (not used but defined for completeness)\n", "def convert_gender(value):\n", " \"\"\"Convert gender values to binary format (0 for female, 1 for male)\"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " value = value.lower()\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 - initial filtering\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 (only if trait_row is not None)\n", "if trait_row is not None:\n", " # Create a DataFrame from the sample characteristics dictionary\n", " clinical_data = pd.DataFrame()\n", " for key, values in {0: ['patients: IgAN_01', 'patients: IgAN_06', 'patients: IgAN_07', 'patients: IgAN_09', \n", " 'patients: IgAN_10', 'patients: IgAN_11', 'patients: IgAN_12', 'patients: IgAN_15', \n", " 'patients: IgAN_16', 'patients: IgAN_17', 'patients: IgAN_18', 'patients: IgAN_19', \n", " 'patients: IgAN_20', 'patients: NM_01', 'patients: NM_02', 'patients: NM_03', \n", " 'patients: NM_07', 'patients: NM_08', 'patients: NM_09', 'patients: PBM_IgAN_10', \n", " 'patients: PBM_IgAN_12', 'patients: PBM_IgAN_17', 'patients: PBM_IgAN_19', \n", " 'patients: PBM_IgAN_20', 'patients: PBM_NM_01', 'patients: PBM_NM_02', \n", " 'patients: PBM_NM_03', 'patients: PBM_NM_04', 'patients: PBM_NM_05'], \n", " 1: ['disease state: IgAN', 'disease state: kidney cancer', 'disease state: normal'], \n", " 2: ['tissue: renal biopsies', 'tissue: paracancerous tissues', 'cell type: monocytes']}.items():\n", " clinical_data[key] = pd.Series(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 dataframe\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of selected clinical features:\")\n", " print(preview)\n", " \n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " \n", " # Save to CSV\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": "07c4959f", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "eb90d5f8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:17:52.367884Z", "iopub.status.busy": "2025-03-25T07:17:52.367774Z", "iopub.status.idle": "2025-03-25T07:17:53.008476Z", "shell.execute_reply": "2025-03-25T07:17:53.008034Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Examining matrix file structure...\n", "Line 0: !Series_title\t\"Single-cell RNA-seq profiling reveals novel insights in immune-complex deposition and epithelium transition in IgA nephropathy\"\n", "Line 1: !Series_geo_accession\t\"GSE127136\"\n", "Line 2: !Series_status\t\"Public on Jul 06 2021\"\n", "Line 3: !Series_submission_date\t\"Feb 25 2019\"\n", "Line 4: !Series_last_update_date\t\"Jul 06 2021\"\n", "Line 5: !Series_summary\t\"IgA nephropathy represents the most prevalent chronic nephrosis worldwide. However, pathogenesis about IgA deposition and end-stage renal failure is still not well defined. Using single-cell RNA-seq, we identified the mesangial membrane receptor for IgA, which collaborates with increased extracellular matrix proteins and protease inhibitor to facilitate IgA deposition. Meanwhile, cell-cell interaction analysis revealed increased communications between mesangium and other cell types, uncovering how morbidity inside glomerulus spreads to whole kidney, which results in the genetic changes of kidney resident immune cells. Prominent interaction decreasing in intercalated cells leads to the discovery of a transitional cell type, which exhibited significant EMT and fibrosis features. Our work comprehensively characterized the pathological mesangial signatures, highlighting the step-by-step pathogenic process of IgA nephropathy from mesangium to epithelium.\"\n", "Line 6: !Series_overall_design\t\"In this study, we collected single cells from 13 IgAN patients’ renal biopsies and normal renal cells from 6 kidney cancer patients’ paracancerous tissues. As glomerulus are difficult to digest, we separately dissociated the glomerulus and the rest renal tissues. We applied CD326+ and CD14+ MACS to capture epithelium and macrophages, to cover the entire renal cell types, negative selected cells from MACS were also collected. Meanwhile, we isolated monocytes from 5 of the 13 IgAN patients and another 5 normal persons’ peripheral blood using CD14+ MACS\"\n", "Line 7: !Series_overall_design\t\"\"\n", "Line 8: !Series_overall_design\t\"**Submitter declares that the raw data have been deposited in the Genome Sequence Archive for Human (https://bigd.big.ac.cn/gsa-human/) under submission number PRJCA003506.**\"\n", "Line 9: !Series_type\t\"Expression profiling by high throughput sequencing\"\n", "Found table marker at line 67\n", "First few lines after marker:\n", 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"!series_matrix_table_end\n", "\n", "\n", "\n", "Total lines examined: 68\n", "\n", "Attempting to extract gene data from matrix file...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Extracted gene expression data is empty\n", "\n", "Gene expression data available: False\n", "\n", "Trying alternative approach to read gene expression data...\n", "Successfully extracted gene data with alternative method: (1, 3620)\n", "First 20 gene IDs:\n", "Index(['!series_matrix_table_end'], dtype='object', name='ID_REF')\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", "# Add diagnostic code to check file content and structure\n", "print(\"Examining matrix file structure...\")\n", "with gzip.open(matrix_file, 'rt') as file:\n", " table_marker_found = False\n", " lines_read = 0\n", " for i, line in enumerate(file):\n", " lines_read += 1\n", " if '!series_matrix_table_begin' in line:\n", " table_marker_found = True\n", " print(f\"Found table marker at line {i}\")\n", " # Read a few lines after the marker to check data structure\n", " next_lines = [next(file, \"\").strip() for _ in range(5)]\n", " print(\"First few lines after marker:\")\n", " for next_line in next_lines:\n", " print(next_line)\n", " break\n", " if i < 10: # Print first few lines to see file structure\n", " print(f\"Line {i}: {line.strip()}\")\n", " if i > 100: # Don't read the entire file\n", " break\n", " \n", " if not table_marker_found:\n", " print(\"Table marker '!series_matrix_table_begin' not found in first 100 lines\")\n", " print(f\"Total lines examined: {lines_read}\")\n", "\n", "# 2. Try extracting gene expression data from the matrix file again with better diagnostics\n", "try:\n", " print(\"\\nAttempting to extract 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: {str(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", "\n", "# If data extraction failed, try an alternative approach using pandas directly\n", "if not is_gene_available:\n", " print(\"\\nTrying alternative approach to read gene expression data...\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Skip lines until we find the marker\n", " for line in file:\n", " if '!series_matrix_table_begin' in line:\n", " break\n", " \n", " # Try to read the data directly with pandas\n", " gene_data = pd.read_csv(file, sep='\\t', index_col=0)\n", " \n", " if not gene_data.empty:\n", " print(f\"Successfully extracted gene data with alternative method: {gene_data.shape}\")\n", " print(\"First 20 gene IDs:\")\n", " print(gene_data.index[:20])\n", " is_gene_available = True\n", " else:\n", " print(\"Alternative extraction method also produced empty data\")\n", " except Exception as e:\n", " print(f\"Alternative extraction failed: {str(e)}\")\n" ] }, { "cell_type": "markdown", "id": "1c013d59", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "b87dbb98", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:17:53.009888Z", "iopub.status.busy": "2025-03-25T07:17:53.009773Z", "iopub.status.idle": "2025-03-25T07:17:53.052253Z", "shell.execute_reply": "2025-03-25T07:17:53.051879Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample gene identifiers: []\n", "No gene identifiers found in the matrix file.\n", "Available files in the dataset: ['GSE127136_family.soft.gz', 'GSE127136_series_matrix.txt.gz']\n", "Based on dataset description (single-cell RNA-seq), gene mapping is likely required.\n", "\n", "requires_gene_mapping = True\n" ] } ], "source": [ "# Examine gene identifiers to determine if mapping is required\n", "import pandas as pd\n", "import os\n", "import gzip\n", "\n", "# Read the matrix data to identify gene identifiers\n", "matrix_file = os.path.join(in_cohort_dir, \"GSE127136_series_matrix.txt.gz\")\n", "\n", "# First, let's try to properly access the file structure and identify gene identifiers\n", "try:\n", " # First attempt - try to read with automatic header detection\n", " with gzip.open(matrix_file, 'rt') as f:\n", " # Skip the first lines until we find the data table start\n", " line = \"\"\n", " skip_rows = 0\n", " while not line.startswith(\"!series_matrix_table_begin\"):\n", " line = f.readline()\n", " skip_rows += 1\n", " \n", " # Read the column headers line\n", " headers = f.readline().strip().split('\\t')\n", " \n", " # Read a few sample rows of data\n", " sample_genes = []\n", " for i in range(5):\n", " line = f.readline().strip()\n", " if line and not line.startswith(\"!series_matrix_table_end\"):\n", " fields = line.split('\\t')\n", " if len(fields) > 0:\n", " sample_genes.append(fields[0])\n", " \n", " print(f\"Sample gene identifiers: {sample_genes[:5]}\")\n", " \n", " # Check if we have common gene identifier formats\n", " has_ensembl = any(id.startswith('ENSG') for id in sample_genes)\n", " has_entrez = all(id.isdigit() for id in sample_genes)\n", " has_symbols = not has_ensembl and not has_entrez\n", " \n", " if sample_genes:\n", " print(f\"Gene ID examples: {sample_genes[:5]}\")\n", " print(f\"Has Ensembl IDs: {has_ensembl}\")\n", " print(f\"Has Entrez IDs: {has_entrez}\")\n", " print(f\"Likely has gene symbols: {has_symbols}\")\n", " else:\n", " print(\"No gene identifiers found in the matrix file.\")\n", " \n", " # Check supplementary files for expression data\n", " supplementary_files = [f for f in os.listdir(in_cohort_dir) if os.path.isfile(os.path.join(in_cohort_dir, f))]\n", " print(f\"Available files in the dataset: {supplementary_files}\")\n", " \n", " # This is single-cell RNA-seq data, which often requires mapping\n", " print(\"Based on dataset description (single-cell RNA-seq), gene mapping is likely required.\")\n", " requires_gene_mapping = True\n", " \n", "except Exception as e:\n", " print(f\"Error examining gene identifiers: {e}\")\n", " print(\"Based on the inability to extract gene IDs and the fact this is single-cell RNA-seq data, assuming mapping is required.\")\n", " requires_gene_mapping = True\n", " \n", "# If we haven't set requires_gene_mapping yet, determine it based on our analysis\n", "if 'requires_gene_mapping' not in locals():\n", " if has_symbols:\n", " requires_gene_mapping = False\n", " print(\"The dataset appears to already contain human gene symbols.\")\n", " else:\n", " requires_gene_mapping = True\n", " print(\"The dataset contains non-symbol identifiers that will need to be mapped.\")\n", "\n", "print(f\"\\nrequires_gene_mapping = {requires_gene_mapping}\")\n" ] }, { "cell_type": "markdown", "id": "53201762", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "dd31e0a4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:17:53.053578Z", "iopub.status.busy": "2025-03-25T07:17:53.053463Z", "iopub.status.idle": "2025-03-25T07:17:53.449099Z", "shell.execute_reply": "2025-03-25T07:17:53.448747Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Examining SOFT file structure...\n", "First few lines of SOFT file:\n", "^DATABASE = GeoMiame\n", "!Database_name = Gene Expression Omnibus (GEO)\n", "!Database_institute = NCBI NLM NIH\n", "!Database_web_link = http://www.ncbi.nlm.nih.gov/geo\n", "!Database_email = geo@ncbi.nlm.nih.gov\n", "File contains platform annotation markers: False\n", "File contains sample annotation markers: False\n", "File contains series annotation markers: True\n", "\n", "Checking for other files that might contain gene mapping information...\n", "Available files in the dataset: ['GSE127136_family.soft.gz', 'GSE127136_series_matrix.txt.gz']\n", "Found potential expression files: ['GSE127136_series_matrix.txt.gz']\n", "\n", "Trying extraction with standard prefixes...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Standard extraction approach failed: No columns to parse from file\n", "\n", "Trying direct file reading for annotation data...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Could not find platform sections in the SOFT file\n", "\n", "Failed to extract gene annotation data from the SOFT file.\n", "This is likely because:\n", "1. This is a single-cell RNA-seq dataset with custom annotations\n", "2. Gene identifiers are already in a usable format or not provided in the standard way\n", "3. Gene mapping information might be stored in a different file format\n", "\n", "Creating minimal gene annotation structure for downstream processing...\n", "Created placeholder gene annotation data for compatibility\n" ] } ], "source": [ "# 1. Extract gene annotation data from the SOFT file - with better error handling and inspection\n", "import traceback\n", "import io\n", "import re\n", "\n", "print(\"Examining SOFT file structure...\")\n", "try:\n", " # First, inspect the SOFT file structure directly\n", " with gzip.open(soft_file, 'rt') as f:\n", " # Read first few lines to understand the file structure\n", " first_lines = [f.readline().strip() for _ in range(20)]\n", " print(\"First few lines of SOFT file:\")\n", " for line in first_lines[:5]: # Print just a few to avoid overwhelming output\n", " print(line)\n", " \n", " # Check if any lines have typical annotation markers\n", " has_platform_annotation = any(line.startswith('^PLATFORM') for line in first_lines)\n", " has_sample_annotation = any(line.startswith('^SAMPLE') for line in first_lines)\n", " has_series_annotation = any(line.startswith('^SERIES') for line in first_lines)\n", " \n", " print(f\"File contains platform annotation markers: {has_platform_annotation}\")\n", " print(f\"File contains sample annotation markers: {has_sample_annotation}\")\n", " print(f\"File contains series annotation markers: {has_series_annotation}\")\n", " \n", " # Now try different approaches to extract gene annotation\n", " annotation_available = False\n", " \n", " # Try to find any supplementary files that might contain gene information\n", " print(\"\\nChecking for other files that might contain gene mapping information...\")\n", " supplementary_files = [f for f in os.listdir(in_cohort_dir) if os.path.isfile(os.path.join(in_cohort_dir, f))]\n", " print(f\"Available files in the dataset: {supplementary_files}\")\n", " \n", " # Look for expression matrix files which might have gene symbols as row names\n", " expression_files = [f for f in supplementary_files if 'expr' in f.lower() or 'matrix' in f.lower()]\n", " if expression_files:\n", " print(f\"Found potential expression files: {expression_files}\")\n", " # We would parse these files here if needed\n", " \n", " # Approach 1: Try with different prefixes for gene annotation\n", " print(\"\\nTrying extraction with standard prefixes...\")\n", " try:\n", " gene_annotation = get_gene_annotation(soft_file, prefixes=['^', '!', '#'])\n", " annotation_available = not gene_annotation.empty\n", " except Exception as e:\n", " print(f\"Standard extraction approach failed: {str(e)}\")\n", " gene_annotation = pd.DataFrame() # Initialize empty DataFrame\n", " \n", " # Approach 2: Try with direct file reading for simple TSV format\n", " if not annotation_available:\n", " print(\"\\nTrying direct file reading for annotation data...\")\n", " try:\n", " with gzip.open(soft_file, 'rt') as f:\n", " content = f.read()\n", " # Look for platform data section which often contains gene annotations\n", " platform_sections = re.findall(r'\\^PLATFORM.*?!\\n', content, re.DOTALL)\n", " \n", " if platform_sections:\n", " print(f\"Found {len(platform_sections)} platform sections in the SOFT file\")\n", " platform_section = platform_sections[0]\n", " # Extract sample table data\n", " table_data = re.search(r'!platform_table_begin\\n(.*?)!platform_table_end', platform_section, re.DOTALL)\n", " \n", " if table_data:\n", " print(\"Found platform table data\")\n", " table_content = table_data.group(1)\n", " gene_annotation = pd.read_csv(io.StringIO(table_content), sep='\\t')\n", " annotation_available = not gene_annotation.empty\n", " if annotation_available:\n", " print(f\"Successfully extracted gene annotation with {len(gene_annotation)} rows\")\n", " else:\n", " print(\"Could not find platform table data in the platform section\")\n", " else:\n", " print(\"Could not find platform sections in the SOFT file\")\n", " except Exception as e:\n", " print(f\"Direct file reading approach failed: {str(e)}\")\n", " \n", " # Check if we have successfully extracted gene annotation\n", " if annotation_available:\n", " print(f\"\\nSuccessfully extracted gene annotation data with {len(gene_annotation)} rows and {len(gene_annotation.columns)} columns\")\n", " \n", " # Preview the annotation DataFrame\n", " print(\"\\nGene annotation preview (first few rows):\")\n", " preview = preview_df(gene_annotation)\n", " print(preview)\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", " else:\n", " print(\"\\nFailed to extract gene annotation data from the SOFT file.\")\n", " print(\"This is likely because:\")\n", " print(\"1. This is a single-cell RNA-seq dataset with custom annotations\")\n", " print(\"2. Gene identifiers are already in a usable format or not provided in the standard way\")\n", " print(\"3. Gene mapping information might be stored in a different file format\")\n", " \n", " # Create a minimal gene annotation dataframe with ID and Gene columns\n", " print(\"\\nCreating minimal gene annotation structure for downstream processing...\")\n", " gene_annotation = pd.DataFrame({'ID': ['placeholder'], 'Gene': ['placeholder']})\n", " print(\"Created placeholder gene annotation data for compatibility\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing gene annotation data: {str(e)}\")\n", " print(\"Detailed error information:\", traceback.format_exc())\n", " # Create minimal gene annotation for compatibility\n", " gene_annotation = pd.DataFrame({'ID': ['placeholder'], 'Gene': ['placeholder']})\n", " annotation_available = False\n" ] }, { "cell_type": "markdown", "id": "283787f5", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "805503cd", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:17:53.450491Z", "iopub.status.busy": "2025-03-25T07:17:53.450365Z", "iopub.status.idle": "2025-03-25T07:17:53.496481Z", "shell.execute_reply": "2025-03-25T07:17:53.496104Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Analyzing single-cell RNA-seq dataset - adapting preprocessing approach...\n", "\n", "Attempting to extract gene information from matrix file...\n", "Found 0 potential gene IDs: []\n", "Creating placeholder gene expression data...\n", "\n", "Gene mapping not available for this dataset - using gene IDs directly as symbols\n", "Saved gene data file to: ../../output/preprocess/Kidney_Clear_Cell_Carcinoma/gene_data/GSE127136.csv\n", "\n", "Gene data preview:\n", " Sample1 Sample2\n", "Gene \n", "GENE1 0.0 0.0\n", "GENE2 0.0 0.0\n", "GENE3 0.0 0.0\n", "\n", "Note: This dataset is a special case where standard GEO format processing is not applicable\n", "The single-cell RNA-seq data is stored in a custom format and potentially in an external repository\n", "(Genome Sequence Archive for Human) as mentioned in the dataset description.\n" ] } ], "source": [ "# Based on the dataset analysis, it's clear this is a single-cell RNA-seq dataset\n", "# with a non-standard format. Let's examine the structure and proceed accordingly.\n", "\n", "# The gene annotation extraction has failed as there's no standard platform annotation in the SOFT file.\n", "# The gene_annotation dataframe we created is just a placeholder with 'ID' and 'Gene' columns.\n", "# The gene expression data couldn't be properly extracted from the matrix file.\n", "\n", "# Given these challenges, let's adapt our approach:\n", "\n", "print(\"\\nAnalyzing single-cell RNA-seq dataset - adapting preprocessing approach...\")\n", "\n", "# This dataset appears to be a non-standard GEO submission for single-cell RNA-seq data\n", "# The main data is likely available in a different format or repository\n", "# As noted in the dataset description: \"raw data have been deposited in the Genome Sequence Archive for Human\"\n", "\n", "# Since we don't have proper gene expression data or gene annotations, \n", "# we'll create a minimal valid output to allow the pipeline to continue\n", "\n", "# 1. First, try to salvage any gene identifiers from the matrix file\n", "try:\n", " # Try reading directly from matrix file\n", " print(\"\\nAttempting to extract gene information from matrix file...\")\n", " with gzip.open(matrix_file, 'rt') as f:\n", " # Skip to the data section\n", " for line in f:\n", " if '!series_matrix_table_begin' in line:\n", " break\n", " \n", " # Read header line\n", " header = next(f, \"\").strip().split('\\t')\n", " \n", " # Try to read a few data rows\n", " gene_ids = []\n", " for i in range(10): # Try to get at least a few gene IDs\n", " line = next(f, \"\").strip()\n", " if line and not line.startswith('!series_matrix_table_end'):\n", " fields = line.split('\\t', 1)\n", " if len(fields) > 0:\n", " gene_ids.append(fields[0])\n", " \n", " print(f\"Found {len(gene_ids)} potential gene IDs: {gene_ids[:5]}\")\n", " \n", " # Create a minimal gene_data DataFrame\n", " if gene_ids:\n", " # Create a minimal gene expression DataFrame\n", " print(\"Creating basic gene expression DataFrame from extracted IDs...\")\n", " gene_data = pd.DataFrame(index=gene_ids)\n", " for col in header[1:5]: # Add a few sample columns\n", " gene_data[col] = 0.0 # placeholder values\n", " else:\n", " # If no gene IDs found, create completely placeholder data\n", " print(\"Creating placeholder gene expression data...\")\n", " gene_data = pd.DataFrame(index=['GENE1', 'GENE2', 'GENE3'])\n", " gene_data['Sample1'] = [0.0, 0.0, 0.0]\n", " gene_data['Sample2'] = [0.0, 0.0, 0.0]\n", "except Exception as e:\n", " print(f\"Error creating gene data: {str(e)}\")\n", " # Create minimal gene_data for compatibility\n", " gene_data = pd.DataFrame(index=['GENE1', 'GENE2', 'GENE3'])\n", " gene_data['Sample1'] = [0.0, 0.0, 0.0]\n", " gene_data['Sample2'] = [0.0, 0.0, 0.0]\n", "\n", "# Set the index name to clarify these are gene identifiers\n", "gene_data.index.name = 'Gene'\n", "\n", "# 2. Since we don't have real gene mapping, we'll simulate the process\n", "print(\"\\nGene mapping not available for this dataset - using gene IDs directly as symbols\")\n", "\n", "# Create directory if it doesn't exist\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "\n", "# 3. Save the gene data for compatibility with downstream steps\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"Saved gene data file to: {out_gene_data_file}\")\n", "\n", "print(\"\\nGene data preview:\")\n", "print(gene_data.head())\n", "\n", "print(\"\\nNote: This dataset is a special case where standard GEO format processing is not applicable\")\n", "print(\"The single-cell RNA-seq data is stored in a custom format and potentially in an external repository\")\n", "print(\"(Genome Sequence Archive for Human) as mentioned in the dataset description.\")\n" ] }, { "cell_type": "markdown", "id": "4984232c", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "c705c732", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:17:53.497851Z", "iopub.status.busy": "2025-03-25T07:17:53.497737Z", "iopub.status.idle": "2025-03-25T07:17:53.567526Z", "shell.execute_reply": "2025-03-25T07:17:53.567179Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Normalizing gene symbols...\n", "After normalization: 0 unique gene symbols\n", "Normalized gene expression data saved to ../../output/preprocess/Kidney_Clear_Cell_Carcinoma/gene_data/GSE127136.csv\n", "\n", "Checking trait availability from previous analysis...\n", "Loaded clinical data with shape: (1, 3)\n", "Renamed column '1' to 'Kidney_Clear_Cell_Carcinoma'\n", "\n", "Linking clinical and genetic data...\n", "Cannot link data: clinical or genetic data is missing.\n", "Reason: Gene expression data is empty.\n", "\n", "Performing final validation...\n", "Abnormality detected in the cohort: GSE127136. Preprocessing failed.\n", "Dataset not usable for Kidney_Clear_Cell_Carcinoma association studies. Data not saved.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "print(\"\\nNormalizing gene symbols...\")\n", "try:\n", " # Check if gene_data is empty before normalization\n", " if gene_data.empty:\n", " print(\"Gene data is empty. Skipping normalization.\")\n", " normalized_gene_data = gene_data\n", " else:\n", " normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"After normalization: {len(normalized_gene_data.index)} unique gene symbols\")\n", " \n", " # Save the normalized gene data (even if empty, to maintain consistent workflow)\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 normalizing gene symbols: {e}\")\n", " normalized_gene_data = gene_data # Use original data if normalization fails\n", "\n", "# Use is_trait_available from previous steps, don't recheck trait_row\n", "print(\"\\nChecking trait availability from previous analysis...\")\n", "# Load the clinical data from the previously saved file\n", "try:\n", " clinical_df = pd.read_csv(out_clinical_data_file)\n", " print(f\"Loaded clinical data with shape: {clinical_df.shape}\")\n", " # Rename the column '1' to the trait name if it exists\n", " if '1' in clinical_df.columns:\n", " clinical_df = clinical_df.rename(columns={'1': trait})\n", " print(f\"Renamed column '1' to '{trait}'\")\n", " is_trait_available = trait in clinical_df.columns\n", "except Exception as e:\n", " print(f\"Error loading clinical data: {e}\")\n", " clinical_df = pd.DataFrame()\n", " is_trait_available = False\n", "\n", "# 2. Link clinical and genetic data if available\n", "print(\"\\nLinking clinical and genetic data...\")\n", "try:\n", " if is_trait_available and not normalized_gene_data.empty:\n", " # Print sample IDs from both datasets for debugging\n", " print(\"Clinical columns:\", list(clinical_df.columns))\n", " print(\"First few genetic sample columns:\", list(normalized_gene_data.columns)[:5])\n", " \n", " # Transpose clinical data so samples are rows\n", " if clinical_df.shape[0] == 1: # If it's currently 1 row with samples as columns\n", " clinical_df = clinical_df.T\n", " clinical_df.columns = [trait]\n", " print(f\"Transposed clinical data to shape: {clinical_df.shape}\")\n", " \n", " # Now link clinical and genetic data\n", " linked_data = pd.concat([clinical_df, normalized_gene_data.T], axis=1)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " \n", " # Check if we have at least one sample with trait value\n", " if trait in linked_data.columns:\n", " trait_count = linked_data[trait].count()\n", " print(f\"Number of samples with trait values: {trait_count}\")\n", " \n", " if trait_count > 0:\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", " # Check if we still have samples after missing value handling\n", " if linked_data.shape[0] > 0:\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", " else:\n", " print(\"Error: All samples were removed during missing value handling.\")\n", " is_biased = True\n", " else:\n", " print(\"No samples have valid trait values. Dataset cannot be used.\")\n", " is_biased = True\n", " else:\n", " print(f\"Trait column '{trait}' not found in linked data.\")\n", " is_biased = True\n", " else:\n", " print(\"Cannot link data: clinical or genetic data is missing.\")\n", " if not is_trait_available:\n", " print(f\"Reason: Trait column '{trait}' is not available in clinical data.\")\n", " if normalized_gene_data.empty:\n", " print(\"Reason: Gene expression data is empty.\")\n", " linked_data = pd.DataFrame()\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_biased = True\n", "\n", "# 5. Final quality validation\n", "print(\"\\nPerforming final validation...\")\n", "note = \"\"\n", "if 'linked_data' in locals() and linked_data.empty:\n", " if not normalized_gene_data.empty and not clinical_df.empty:\n", " note = \"Dataset failed in linking phase. Clinical data structure could not be properly matched with gene expression data.\"\n", " elif normalized_gene_data.empty:\n", " note = \"Dataset failed in gene mapping phase. Could not properly map probe IDs to gene symbols.\"\n", " elif clinical_df.empty:\n", " note = \"Dataset failed in clinical data extraction. No valid trait information available.\"\n", "elif 'is_biased' in locals() and is_biased:\n", " note = \"Dataset passed initial processing but contains severely biased trait distribution.\"\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=not normalized_gene_data.empty, # Set based on actual gene data availability\n", " is_trait_available=is_trait_available,\n", " is_biased=is_biased if 'is_biased' in locals() else True,\n", " df=linked_data if 'linked_data' in locals() and not linked_data.empty else pd.DataFrame(),\n", " note=note\n", ")\n", "\n", "# 6. Save linked data if usable\n", "if is_usable and 'linked_data' in locals() and not linked_data.empty:\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 }