{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "94b0aa78", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:02.882204Z", "iopub.status.busy": "2025-03-25T03:57:02.882109Z", "iopub.status.idle": "2025-03-25T03:57:03.047580Z", "shell.execute_reply": "2025-03-25T03:57:03.047239Z" } }, "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 = \"GSE53441\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Sickle_Cell_Anemia\"\n", "in_cohort_dir = \"../../input/GEO/Sickle_Cell_Anemia/GSE53441\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Sickle_Cell_Anemia/GSE53441.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Sickle_Cell_Anemia/gene_data/GSE53441.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Sickle_Cell_Anemia/clinical_data/GSE53441.csv\"\n", "json_path = \"../../output/preprocess/Sickle_Cell_Anemia/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "1dc6a2cf", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "f5a6b83a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:03.049034Z", "iopub.status.busy": "2025-03-25T03:57:03.048891Z", "iopub.status.idle": "2025-03-25T03:57:03.126236Z", "shell.execute_reply": "2025-03-25T03:57:03.125935Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Associations of inflammation, iron and early death in sickle cell disease\"\n", "!Series_summary\t\"Expression profiling using a defined set of iron regulated genes identifies co-regulation of genes and pathways related to inflammatory cytokines\"\n", "!Series_summary\t\"In this dataset, we include the whole genome expression data obtained from PBMCs from steady state HbSSs with Sickle Cell Disease and healthy HbAAs . Unsupervised two-way hierarchical clustering using a predefined iron regulated gene list identified 3 groups of subjects with high, low and intermediate expression of iron activated genes.In analysis of these three iron regulated gene clusters, we found 98 genes which were consistently and significantly differentially expressed.\"\n", "!Series_overall_design\t\"Samples from 24 subjects with SCA and 9 healthy subjects were analysed. With approval from the institutional review board, RNA was isolated from PBMCs and plasma was stored for analysis of baseline laboratory parameters. All RNA was hybridized on Affymetrix’ (Santa Clara, CA) Human Genome U133 Plus 2.0 Amicroarray in the same batch. Signal intensity values were achieved by well-accepted processing procedures including background correction, quantile normalization and median polish summarization. comparison of the resulting clustering groups were used to find differentially expressed genes with the filter of a change greater than 40% between the clustering groups and 10% false discovery rate (FDR). Functional analysis was performed on Ingenuity Pathway Analysis (IPA) System.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['diagnosis: sickle cell anemia (SCA)', 'diagnosis: normal'], 1: ['tissue: PBMC']}\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": "15787a54", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "e685a195", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:03.127461Z", "iopub.status.busy": "2025-03-25T03:57:03.127360Z", "iopub.status.idle": "2025-03-25T03:57:03.135059Z", "shell.execute_reply": "2025-03-25T03:57:03.134771Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of selected clinical features:\n", "GSM1293592: [1.0]\n", "GSM1293593: [1.0]\n", "GSM1293594: [1.0]\n", "GSM1293595: [1.0]\n", "GSM1293596: [1.0]\n", "GSM1293597: [1.0]\n", "GSM1293598: [1.0]\n", "GSM1293599: [1.0]\n", "GSM1293600: [1.0]\n", "GSM1293601: [1.0]\n", "GSM1293602: [1.0]\n", "GSM1293603: [1.0]\n", "GSM1293604: [1.0]\n", "GSM1293605: [1.0]\n", "GSM1293606: [1.0]\n", "GSM1293607: [1.0]\n", "GSM1293608: [1.0]\n", "GSM1293609: [1.0]\n", "GSM1293610: [1.0]\n", "GSM1293611: [1.0]\n", "GSM1293612: [1.0]\n", "GSM1293613: [1.0]\n", "GSM1293614: [1.0]\n", "GSM1293615: [1.0]\n", "GSM1293616: [0.0]\n", "GSM1293617: [0.0]\n", "GSM1293618: [0.0]\n", "GSM1293619: [0.0]\n", "GSM1293620: [0.0]\n", "GSM1293621: [0.0]\n", "GSM1293622: [0.0]\n", "GSM1293623: [0.0]\n", "GSM1293624: [0.0]\n", "GSM1293625: [0.0]\n", "Clinical data saved to ../../output/preprocess/Sickle_Cell_Anemia/clinical_data/GSE53441.csv\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import json\n", "from typing import Callable, Dict, Any, Optional\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains gene expression data from Affymetrix Human Genome microarray\n", "is_gene_available = True\n", "\n", "# 2.1 Data Availability\n", "# From the sample characteristics dictionary, we can identify relevant rows:\n", "# trait_row = 0 (diagnosis: sickle cell anemia (SCA) or diagnosis: normal)\n", "# There's no age data available\n", "# There's no gender data available\n", "trait_row = 0\n", "age_row = None\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"Convert trait value to binary (0 for normal, 1 for SCA)\"\"\"\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\n", " if 'sickle cell anemia' in value.lower() or 'sca' in value.lower():\n", " return 1\n", " elif 'normal' in value.lower():\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age value to continuous\"\"\"\n", " # Not used since age data is not available\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n", " # Not used since gender data is not available\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Conduct 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", "if trait_row is not None:\n", " # Assuming clinical_data is already available from previous step\n", " try:\n", " # Extract clinical features\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data, # Use existing variable\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", " for key, values in preview.items():\n", " print(f\"{key}: {values}\")\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 the clinical data\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " except NameError:\n", " print(\"Warning: clinical_data variable is not available. Cannot extract clinical features.\")\n", " except Exception as e:\n", " print(f\"Error during clinical feature extraction: {str(e)}\")\n" ] }, { "cell_type": "markdown", "id": "ebbe1bbf", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "a0270301", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:03.136049Z", "iopub.status.busy": "2025-03-25T03:57:03.135942Z", "iopub.status.idle": "2025-03-25T03:57:03.244091Z", "shell.execute_reply": "2025-03-25T03:57:03.243716Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 61\n", "Header line: \"ID_REF\"\t\"GSM1293592\"\t\"GSM1293593\"\t\"GSM1293594\"\t\"GSM1293595\"\t\"GSM1293596\"\t\"GSM1293597\"\t\"GSM1293598\"\t\"GSM1293599\"\t\"GSM1293600\"\t\"GSM1293601\"\t\"GSM1293602\"\t\"GSM1293603\"\t\"GSM1293604\"\t\"GSM1293605\"\t\"GSM1293606\"\t\"GSM1293607\"\t\"GSM1293608\"\t\"GSM1293609\"\t\"GSM1293610\"\t\"GSM1293611\"\t\"GSM1293612\"\t\"GSM1293613\"\t\"GSM1293614\"\t\"GSM1293615\"\t\"GSM1293616\"\t\"GSM1293617\"\t\"GSM1293618\"\t\"GSM1293619\"\t\"GSM1293620\"\t\"GSM1293621\"\t\"GSM1293622\"\t\"GSM1293623\"\t\"GSM1293624\"\t\"GSM1293625\"\n", "First data line: \"1007_s_at\"\t6.26\t6.62\t6.36\t6.44\t6.67\t6.48\t6.36\t6.82\t6.35\t6.44\t6.4\t6.27\t6.02\t6.55\t6.71\t6.81\t6.95\t7.21\t6.33\t6.62\t6.6\t6.28\t6.51\t6.91\t6.27\t6.11\t6.47\t6.28\t6.47\t6.35\t6.59\t6.35\t6.5\t5.94\n", "Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n", " '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n", " '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n", " '1552263_at', '1552264_a_at', '1552266_at'],\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": "e2eeac3f", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "4ae73c1d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:03.245464Z", "iopub.status.busy": "2025-03-25T03:57:03.245357Z", "iopub.status.idle": "2025-03-25T03:57:03.247181Z", "shell.execute_reply": "2025-03-25T03:57:03.246908Z" } }, "outputs": [], "source": [ "# Examining the identifiers in the gene expression data\n", "# The identifiers appear to be Affymetrix probe IDs (e.g., '1007_s_at', '1053_at', etc.)\n", "# These are not standard human gene symbols but rather microarray probe identifiers\n", "# They need to be mapped to standard gene symbols for biological interpretation\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "37f90c80", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "826fee24", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:03.248409Z", "iopub.status.busy": "2025-03-25T03:57:03.248310Z", "iopub.status.idle": "2025-03-25T03:57:04.104481Z", "shell.execute_reply": "2025-03-25T03:57:04.104115Z" } }, "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 = GSE53441\n", "Line 6: !Series_title = Associations of inflammation, iron and early death in sickle cell disease\n", "Line 7: !Series_geo_accession = GSE53441\n", "Line 8: !Series_status = Public on Dec 31 2014\n", "Line 9: !Series_submission_date = Dec 18 2013\n", "Line 10: !Series_last_update_date = Mar 25 2019\n", "Line 11: !Series_pubmed_id = 25378535\n", "Line 12: !Series_summary = Expression profiling using a defined set of iron regulated genes identifies co-regulation of genes and pathways related to inflammatory cytokines\n", "Line 13: !Series_summary = In this dataset, we include the whole genome expression data obtained from PBMCs from steady state HbSSs with Sickle Cell Disease and healthy HbAAs . Unsupervised two-way hierarchical clustering using a predefined iron regulated gene list identified 3 groups of subjects with high, low and intermediate expression of iron activated genes.In analysis of these three iron regulated gene clusters, we found 98 genes which were consistently and significantly differentially expressed.\n", "Line 14: !Series_overall_design = Samples from 24 subjects with SCA and 9 healthy subjects were analysed. With approval from the institutional review board, RNA was isolated from PBMCs and plasma was stored for analysis of baseline laboratory parameters. All RNA was hybridized on Affymetrix’ (Santa Clara, CA) Human Genome U133 Plus 2.0 Amicroarray in the same batch. Signal intensity values were achieved by well-accepted processing procedures including background correction, quantile normalization and median polish summarization. comparison of the resulting clustering groups were used to find differentially expressed genes with the filter of a change greater than 40% between the clustering groups and 10% false discovery rate (FDR). Functional analysis was performed on Ingenuity Pathway Analysis (IPA) System.\n", "Line 15: !Series_type = Expression profiling by array\n", "Line 16: !Series_contributor = Eduard,J,van Beers\n", "Line 17: !Series_contributor = Yanqin,,Yang\n", "Line 18: !Series_contributor = Nalini,,Raghavachari\n", "Line 19: !Series_contributor = Gregory,J,Kato\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\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": "b53e591c", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "40ae415c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:04.106024Z", "iopub.status.busy": "2025-03-25T03:57:04.105874Z", "iopub.status.idle": "2025-03-25T03:57:04.547899Z", "shell.execute_reply": "2025-03-25T03:57:04.547523Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mapping dataframe shape: (45782, 2)\n", "Sample of mapping data:\n", " ID Gene\n", "0 1007_s_at DDR1 /// MIR4640\n", "1 1053_at RFC2\n", "2 117_at HSPA6\n", "3 121_at PAX8\n", "4 1255_g_at GUCA1A\n", "Gene expression data shape after mapping: (21278, 34)\n", "First few gene symbols after mapping:\n", "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n", " 'A4GALT', 'A4GNT', 'AA06'],\n", " dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Sickle_Cell_Anemia/gene_data/GSE53441.csv\n" ] } ], "source": [ "# 1. Identify the relevant columns from the gene annotation data\n", "# From previous output, we can see that:\n", "# - 'ID' column contains the probe identifiers (e.g., '1007_s_at')\n", "# - 'Gene Symbol' column contains the gene symbols (e.g., 'DDR1 /// MIR4640')\n", "\n", "# Get the mapping dataframe from the annotation data\n", "prob_col = 'ID' # Column with probe identifiers\n", "gene_col = 'Gene Symbol' # Column with gene symbols\n", "\n", "# 2. Create the mapping dataframe\n", "mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n", "print(\"Sample of mapping data:\")\n", "print(mapping_df.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n", "print(\"First few gene symbols after mapping:\")\n", "print(gene_data.index[:10])\n", "\n", "# 4. Save the gene expression data to 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": "a7c30763", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "47c264a7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:57:04.549383Z", "iopub.status.busy": "2025-03-25T03:57:04.549274Z", "iopub.status.idle": "2025-03-25T03:57:04.934377Z", "shell.execute_reply": "2025-03-25T03:57:04.934016Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (21278, 34)\n", "Gene data shape after normalization: (19845, 34)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Sickle_Cell_Anemia/gene_data/GSE53441.csv\n", "Loaded clinical features with shape: (1, 34)\n", "Linked data shape: (34, 19846)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " 0 A1BG A1BG-AS1 A1CF A2M\n", "GSM1293592 1.0 6.79 4.73 8.22 8.01\n", "GSM1293593 1.0 6.20 4.52 8.20 7.70\n", "GSM1293594 1.0 6.58 4.65 8.43 7.94\n", "GSM1293595 1.0 6.35 4.62 7.87 7.27\n", "GSM1293596 1.0 6.24 4.21 8.07 8.18\n", "Missing values before handling:\n", "Error processing data: 'Sickle_Cell_Anemia'\n", "Abnormality detected in the cohort: GSE53441. Preprocessing failed.\n", "Data was determined to be unusable and was not saved\n" ] } ], "source": [ "# 1. Normalize gene symbols in the obtained gene expression data\n", "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", "\n", "# Save the normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. Load and prepare clinical features\n", "try:\n", " # Load the clinical features created in a previous step\n", " clinical_features = pd.read_csv(out_clinical_data_file)\n", " print(f\"Loaded clinical features with shape: {clinical_features.shape}\")\n", " \n", " # Rename the column to the trait name if it's a numeric column\n", " if '0' in clinical_features.columns:\n", " clinical_features = clinical_features.rename(columns={'0': trait})\n", " \n", " # 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", " # 3. 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", " # 4. 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", " # 5. 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 includes sickle cell anemia patients and healthy controls, processed from Affymetrix microarray data.\"\n", " )\n", " \n", " # 6. 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 }