{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "a9b8852e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:31.196829Z", "iopub.status.busy": "2025-03-25T03:57:31.196662Z", "iopub.status.idle": "2025-03-25T03:57:31.359228Z", "shell.execute_reply": "2025-03-25T03:57:31.358782Z" } }, "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 = \"Sickle_Cell_Anemia\"\n", "cohort = \"GSE84634\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Sickle_Cell_Anemia\"\n", "in_cohort_dir = \"../../input/GEO/Sickle_Cell_Anemia/GSE84634\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Sickle_Cell_Anemia/GSE84634.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Sickle_Cell_Anemia/gene_data/GSE84634.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84634.csv\"\n", "json_path = \"../../output/preprocess/Sickle_Cell_Anemia/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "5cdeb923", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "f39293de", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:31.360720Z", "iopub.status.busy": "2025-03-25T03:57:31.360575Z", "iopub.status.idle": "2025-03-25T03:57:31.439194Z", "shell.execute_reply": "2025-03-25T03:57:31.438751Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene expression of peripheral blood mononuclear cells from adults with sickle cell disease (University of Chicago cohort)\"\n", "!Series_summary\t\"Sickle cell disease is associated with systemic complications, many associated with either severity of disease or increased risk of mortality. We sought to identify a circulating gene expression profile whose predictive capacity spanned the spectrum of these poor outcomes in sickle cell disease.\"\n", "!Series_summary\t\"The Training cohort consisted of patients with SCD who were prospectively recruited from the University of Illinois. The Testing cohort consisted of a combination of patients prospectively seen at two separate institutions including the University of Chicago and Howard University.\"\n", "!Series_overall_design\t\"The gene expression of PBMC from 38 sickle cell disease patients from University of Chicago were analyzed.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: peripheral blood'], 1: ['cell type: mononuclear cells'], 2: ['disease: sickle cell disease']}\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": "650cdcdc", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "0eb82aff", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:31.440565Z", "iopub.status.busy": "2025-03-25T03:57:31.440459Z", "iopub.status.idle": "2025-03-25T03:57:31.447437Z", "shell.execute_reply": "2025-03-25T03:57:31.446985Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of clinical data:\n", "{'characteristics_ch1': [1.0]}\n", "Clinical data saved to ../../output/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84634.csv\n" ] } ], "source": [ "import os\n", "import json\n", "import pandas as pd\n", "from typing import Dict, Any, Callable, Optional\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains gene expression data from PBMCs\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# From sample characteristics, we can see \"disease: sickle cell disease\" corresponds to our trait\n", "trait_row = 2 # \"disease: sickle cell disease\"\n", "age_row = None # Age is not available in the sample characteristics\n", "gender_row = None # Gender is not available in the sample characteristics\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"Convert trait value to binary format (0 = control, 1 = case).\"\"\"\n", " if value is None:\n", " return None\n", " # Extract value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # From the data, everyone has sickle cell disease\n", " # So this is a constant feature with all 1s\n", " if \"sickle cell disease\" in value.lower():\n", " return 1\n", " else:\n", " return None # For any unexpected values\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age value to continuous format.\"\"\"\n", " # Not used as age data is not available\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender value to binary format (0 = female, 1 = male).\"\"\"\n", " # Not used as gender data is not available\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Since trait_row is not None, trait data is available\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort info (initial filtering)\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "# Since trait_row is not None, clinical data is available\n", "if trait_row is not None:\n", " # Assume clinical_data is already loaded from a previous step\n", " clinical_data = pd.DataFrame({\"characteristics_ch1\": [\"disease: sickle cell disease\"] * 38}) # Based on the description of 38 SCD patients\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 clinical data:\")\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": "ffa4f7c2", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "a913ec6d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:31.448783Z", "iopub.status.busy": "2025-03-25T03:57:31.448678Z", "iopub.status.idle": "2025-03-25T03:57:31.548656Z", "shell.execute_reply": "2025-03-25T03:57:31.548144Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 65\n", "Header line: \"ID_REF\"\t\"GSM2243346\"\t\"GSM2243347\"\t\"GSM2243348\"\t\"GSM2243349\"\t\"GSM2243350\"\t\"GSM2243351\"\t\"GSM2243352\"\t\"GSM2243353\"\t\"GSM2243354\"\t\"GSM2243355\"\t\"GSM2243356\"\t\"GSM2243357\"\t\"GSM2243358\"\t\"GSM2243359\"\t\"GSM2243360\"\t\"GSM2243361\"\t\"GSM2243362\"\t\"GSM2243363\"\t\"GSM2243364\"\t\"GSM2243365\"\t\"GSM2243366\"\t\"GSM2243367\"\t\"GSM2243368\"\t\"GSM2243369\"\t\"GSM2243370\"\t\"GSM2243371\"\t\"GSM2243372\"\t\"GSM2243373\"\t\"GSM2243374\"\t\"GSM2243375\"\t\"GSM2243376\"\t\"GSM2243377\"\t\"GSM2243378\"\t\"GSM2243379\"\t\"GSM2243380\"\t\"GSM2243381\"\t\"GSM2243382\"\t\"GSM2243383\"\n", "First data line: 2315554\t6.087148013\t6.505591352\t5.921904717\t6.15941806\t6.138992908\t6.032789074\t6.625662972\t6.089488274\t6.240933297\t6.014541988\t6.426659671\t6.494851638\t6.218330081\t6.189424379\t6.351294575\t6.507638473\t5.986528622\t6.190491525\t6.075310436\t6.304058565\t6.304614819\t6.753211011\t6.097934478\t6.23269389\t6.098436019\t6.311435772\t6.188093249\t5.776282784\t5.939137591\t6.056735194\t6.32142099\t6.341911205\t6.161502475\t6.343962963\t6.54086782\t6.221432023\t6.303210505\t6.090967623\n", "Index(['2315554', '2315633', '2315674', '2315739', '2315894', '2315918',\n", " '2315951', '2316218', '2316245', '2316379', '2316558', '2316605',\n", " '2316746', '2316905', '2316953', '2317246', '2317317', '2317434',\n", " '2317472', '2317512'],\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": "c0e1efb8", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "300b9845", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:31.550125Z", "iopub.status.busy": "2025-03-25T03:57:31.550014Z", "iopub.status.idle": "2025-03-25T03:57:31.552374Z", "shell.execute_reply": "2025-03-25T03:57:31.551956Z" } }, "outputs": [], "source": [ "# Examine the gene identifiers from the previous output\n", "# The identifiers shown (2315554, 2315633, etc.) appear to be numeric IDs, \n", "# not standard human gene symbols which are typically alphanumeric (e.g. BRCA1, TP53)\n", "# These are likely probe IDs from a microarray platform that need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "9d136115", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "a9fab677", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:31.553730Z", "iopub.status.busy": "2025-03-25T03:57:31.553629Z", "iopub.status.idle": "2025-03-25T03:57:32.951176Z", "shell.execute_reply": "2025-03-25T03:57:32.950644Z" } }, "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 = GSE84634\n", "Line 6: !Series_title = Gene expression of peripheral blood mononuclear cells from adults with sickle cell disease (University of Chicago cohort)\n", "Line 7: !Series_geo_accession = GSE84634\n", "Line 8: !Series_status = Public on Sep 08 2017\n", "Line 9: !Series_submission_date = Jul 20 2016\n", "Line 10: !Series_last_update_date = Feb 18 2019\n", "Line 11: !Series_pubmed_id = 28373264\n", "Line 12: !Series_summary = Sickle cell disease is associated with systemic complications, many associated with either severity of disease or increased risk of mortality. We sought to identify a circulating gene expression profile whose predictive capacity spanned the spectrum of these poor outcomes in sickle cell disease.\n", "Line 13: !Series_summary = The Training cohort consisted of patients with SCD who were prospectively recruited from the University of Illinois. The Testing cohort consisted of a combination of patients prospectively seen at two separate institutions including the University of Chicago and Howard University.\n", "Line 14: !Series_overall_design = The gene expression of PBMC from 38 sickle cell disease patients from University of Chicago were analyzed.\n", "Line 15: !Series_type = Expression profiling by array\n", "Line 16: !Series_contributor = Zhengdeng,,Lei\n", "Line 17: !Series_contributor = Ankit,,Desai\n", "Line 18: !Series_contributor = Roberto,,Machado\n", "Line 19: !Series_sample_id = GSM2243346\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': [2315100, 2315106, 2315109, 2315111, 2315113], 'GB_LIST': ['NR_024005,NR_034090,NR_024004,AK093685', 'DQ786314', nan, nan, 'DQ786265'], 'SPOT_ID': ['chr1:11884-14409', 'chr1:14760-15198', 'chr1:19408-19712', 'chr1:25142-25532', 'chr1:27563-27813'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10'], 'RANGE_STRAND': ['+', '+', '+', '+', '+'], 'RANGE_START': ['11884', '14760', '19408', '25142', '27563'], 'RANGE_STOP': ['14409', '15198', '19712', '25532', '27813'], 'total_probes': ['20', '8', '4', '4', '4'], 'gene_assignment': ['NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771 /// NR_034090 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 9 // 15q26.3 // 100288486 /// NR_024004 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771 /// AK093685 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771', '---', '---', '---', '---'], 'mrna_assignment': ['NR_024005 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 (DDX11L2), transcript variant 2, non-coding RNA. // chr1 // 100 // 80 // 16 // 16 // 0 /// NR_034090 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 9 (DDX11L9), non-coding RNA. // chr1 // 100 // 80 // 16 // 16 // 0 /// NR_024004 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 (DDX11L2), transcript variant 1, non-coding RNA. // chr1 // 100 // 75 // 15 // 15 // 0 /// AK093685 // GenBank // Homo sapiens cDNA FLJ36366 fis, clone THYMU2007824. // chr1 // 94 // 80 // 15 // 16 // 0 /// ENST00000513886 // ENSEMBL // cdna:known chromosome:GRCh37:16:61555:64090:1 gene:ENSG00000233614 // chr1 // 100 // 80 // 16 // 16 // 0 /// ENST00000456328 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000223972 // chr1 // 100 // 80 // 16 // 16 // 0 /// ENST00000518655 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000253101 // chr1 // 100 // 80 // 16 // 16 // 0', 'DQ786314 // GenBank // Homo sapiens clone HLS_IMAGE_811138 mRNA sequence. // chr1 // 100 // 38 // 3 // 3 // 0', '---', '---', 'DQ786265 // GenBank // Homo sapiens clone HLS_IMAGE_298685 mRNA sequence. // chr1 // 100 // 100 // 4 // 4 // 0'], 'category': ['main', 'main', '---', '---', 'main']}\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": "476eb3da", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "513f4641", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:32.952691Z", "iopub.status.busy": "2025-03-25T03:57:32.952585Z", "iopub.status.idle": "2025-03-25T03:57:36.495479Z", "shell.execute_reply": "2025-03-25T03:57:36.494946Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape: (22011, 38)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation data shape: (1153375, 12)\n", "Mapping dataframe shape: (316481, 2)\n", "First few rows of mapping dataframe:\n", " ID Gene\n", "0 2315100 NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-As...\n", "1 2315106 ---\n", "2 2315109 ---\n", "3 2315111 ---\n", "4 2315113 ---\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Mapped gene expression data shape: (48895, 38)\n", "First few rows of gene expression data:\n", " GSM2243346 GSM2243347 GSM2243348 GSM2243349 GSM2243350 GSM2243351 \\\n", "Gene \n", "A- 15.509117 15.397530 15.808708 15.439154 15.664632 15.683596 \n", "A-2 2.343726 2.239466 2.320720 2.421121 2.389999 2.265380 \n", "A-52 4.393986 4.424308 4.358994 4.405801 4.404844 4.336857 \n", "A-E 1.381988 1.407230 1.351711 1.373573 1.373060 1.288748 \n", "A-I 4.520968 4.413596 4.748982 4.810093 4.669394 4.478377 \n", "\n", " GSM2243352 GSM2243353 GSM2243354 GSM2243355 ... GSM2243374 \\\n", "Gene ... \n", "A- 14.417419 15.318694 15.801011 15.815661 ... 15.801735 \n", "A-2 2.383855 2.341696 2.324946 2.263749 ... 2.363010 \n", "A-52 4.457693 4.427117 4.329272 4.250242 ... 4.407136 \n", "A-E 1.413379 1.367112 1.313429 1.266908 ... 1.364211 \n", "A-I 4.570401 4.527268 4.713956 4.606424 ... 4.691553 \n", "\n", " GSM2243375 GSM2243376 GSM2243377 GSM2243378 GSM2243379 GSM2243380 \\\n", "Gene \n", "A- 15.851646 15.128587 15.012407 15.457966 15.585657 15.258049 \n", "A-2 2.355550 2.423941 2.246307 2.403882 2.144101 2.256434 \n", "A-52 4.411432 4.457067 4.421105 4.426387 4.514809 4.412663 \n", "A-E 1.363080 1.473665 1.406145 1.441528 1.273675 1.407616 \n", "A-I 4.833840 4.519637 4.564514 4.714724 4.344824 4.619879 \n", "\n", " GSM2243381 GSM2243382 GSM2243383 \n", "Gene \n", "A- 15.716938 15.370655 15.709934 \n", "A-2 2.337742 2.305593 2.359541 \n", "A-52 4.469582 4.386699 4.432339 \n", "A-E 1.399194 1.350216 1.309320 \n", "A-I 4.725567 4.545077 4.431872 \n", "\n", "[5 rows x 38 columns]\n", "After normalization, gene expression data shape: (18418, 38)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Sickle_Cell_Anemia/gene_data/GSE84634.csv\n" ] } ], "source": [ "# 1. Identify the relevant columns for mapping\n", "# From examining the gene annotation data, we can see:\n", "# - 'ID' column contains numeric identifiers matching those in the gene expression data\n", "# - 'gene_assignment' column contains gene symbol information\n", "\n", "# First, obtain the gene expression data if we haven't already\n", "gene_data = get_genetic_data(matrix_file)\n", "print(f\"Gene expression data shape: {gene_data.shape}\")\n", "\n", "# Get gene annotation data from SOFT file\n", "gene_annotation = get_gene_annotation(soft_file)\n", "print(f\"Gene annotation data shape: {gene_annotation.shape}\")\n", "\n", "# 2. Create mapping dataframe with ID and gene symbols\n", "# Extract the 'ID' and 'gene_assignment' columns for mapping\n", "mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')\n", "print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n", "print(f\"First few rows of mapping dataframe:\")\n", "print(mapping_df.head())\n", "\n", "# 3. Apply the mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "print(f\"Mapped gene expression data shape: {gene_data.shape}\")\n", "print(f\"First few rows of gene expression data:\")\n", "print(gene_data.head())\n", "\n", "# Apply gene symbol normalization to handle synonyms\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"After normalization, gene expression data shape: {gene_data.shape}\")\n", "\n", "# Save gene expression data to output file\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"Gene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "185781a1", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "4c52d163", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:36.497130Z", "iopub.status.busy": "2025-03-25T03:57:36.497007Z", "iopub.status.idle": "2025-03-25T03:57:42.341987Z", "shell.execute_reply": "2025-03-25T03:57:42.341361Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (18418, 38)\n", "First few genes with their expression values after normalization:\n", " GSM2243346 GSM2243347 GSM2243348 GSM2243349 GSM2243350 \\\n", "Gene \n", "A1BG 1.629561 1.650178 1.623354 1.674790 1.693946 \n", "A1BG-AS1 1.629561 1.650178 1.623354 1.674790 1.693946 \n", "A1CF 1.021493 1.015058 0.983417 1.029269 0.977699 \n", "A2M 2.297381 2.518467 2.445836 2.369931 2.310326 \n", "A2ML1 1.144764 1.230670 1.232296 1.259902 1.492003 \n", "\n", " GSM2243351 GSM2243352 GSM2243353 GSM2243354 GSM2243355 ... \\\n", "Gene ... \n", "A1BG 1.585265 1.601531 1.629005 1.595809 1.503266 ... \n", "A1BG-AS1 1.585265 1.601531 1.629005 1.595809 1.503266 ... \n", "A1CF 0.943049 1.038536 1.008471 0.984444 0.999792 ... \n", "A2M 2.350215 2.275702 2.481097 2.344658 2.395414 ... \n", "A2ML1 1.157765 1.343480 1.225757 1.160672 1.288853 ... \n", "\n", " GSM2243374 GSM2243375 GSM2243376 GSM2243377 GSM2243378 \\\n", "Gene \n", "A1BG 1.593967 1.657989 1.669062 1.657994 1.656727 \n", "A1BG-AS1 1.593967 1.657989 1.669062 1.657994 1.656727 \n", "A1CF 1.006683 1.044714 1.015970 1.084542 1.058144 \n", "A2M 2.334292 2.218342 2.386534 2.445819 2.368546 \n", "A2ML1 1.212673 1.336245 1.480550 1.279770 1.217620 \n", "\n", " GSM2243379 GSM2243380 GSM2243381 GSM2243382 GSM2243383 \n", "Gene \n", "A1BG 1.507334 1.628379 1.617256 1.673631 1.555988 \n", "A1BG-AS1 1.507334 1.628379 1.617256 1.673631 1.555988 \n", "A1CF 1.015978 0.996713 1.014247 0.910722 0.994150 \n", "A2M 2.437949 2.281662 2.289033 2.366515 2.444647 \n", "A2ML1 1.362562 1.315409 1.179588 1.379166 1.280339 \n", "\n", "[5 rows x 38 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Sickle_Cell_Anemia/gene_data/GSE84634.csv\n", "Raw clinical data shape: (3, 39)\n", "Clinical features:\n", " GSM2243346 GSM2243347 GSM2243348 GSM2243349 \\\n", "Sickle_Cell_Anemia 1.0 1.0 1.0 1.0 \n", "\n", " GSM2243350 GSM2243351 GSM2243352 GSM2243353 \\\n", "Sickle_Cell_Anemia 1.0 1.0 1.0 1.0 \n", "\n", " GSM2243354 GSM2243355 ... GSM2243374 GSM2243375 \\\n", "Sickle_Cell_Anemia 1.0 1.0 ... 1.0 1.0 \n", "\n", " GSM2243376 GSM2243377 GSM2243378 GSM2243379 \\\n", "Sickle_Cell_Anemia 1.0 1.0 1.0 1.0 \n", "\n", " GSM2243380 GSM2243381 GSM2243382 GSM2243383 \n", "Sickle_Cell_Anemia 1.0 1.0 1.0 1.0 \n", "\n", "[1 rows x 38 columns]\n", "Clinical features saved to ../../output/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84634.csv\n", "Linked data shape: (38, 18419)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Sickle_Cell_Anemia A1BG A1BG-AS1 A1CF A2M\n", "GSM2243346 1.0 1.629561 1.629561 1.021493 2.297381\n", "GSM2243347 1.0 1.650178 1.650178 1.015058 2.518467\n", "GSM2243348 1.0 1.623354 1.623354 0.983417 2.445836\n", "GSM2243349 1.0 1.674790 1.674790 1.029269 2.369931\n", "GSM2243350 1.0 1.693946 1.693946 0.977699 2.310326\n", "Missing values before handling:\n", " Trait (Sickle_Cell_Anemia) missing: 0 out of 38\n", " Genes with >20% missing: 0\n", " Samples with >5% missing genes: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (38, 18419)\n", "Quartiles for 'Sickle_Cell_Anemia':\n", " 25%: 1.0\n", " 50% (Median): 1.0\n", " 75%: 1.0\n", "Min: 1.0\n", "Max: 1.0\n", "The distribution of the feature 'Sickle_Cell_Anemia' 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=f\"Dataset contains gene expression data but lacks clear trait indicators for {trait} 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=f\"Dataset contains only {trait} patients with no healthy controls, making it unsuitable for case-control analysis.\"\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 for {trait}: {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 }