{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "4e450c76", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:30.636495Z", "iopub.status.busy": "2025-03-25T05:40:30.636385Z", "iopub.status.idle": "2025-03-25T05:40:30.820647Z", "shell.execute_reply": "2025-03-25T05:40:30.820280Z" } }, "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 = \"Height\"\n", "cohort = \"GSE152073\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Height\"\n", "in_cohort_dir = \"../../input/GEO/Height/GSE152073\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Height/GSE152073.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Height/gene_data/GSE152073.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Height/clinical_data/GSE152073.csv\"\n", "json_path = \"../../output/preprocess/Height/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "0af36208", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "bf794968", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:30.822158Z", "iopub.status.busy": "2025-03-25T05:40:30.821992Z", "iopub.status.idle": "2025-03-25T05:40:31.061888Z", "shell.execute_reply": "2025-03-25T05:40:31.061516Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene expression data from Brazilian SPAH study\"\n", "!Series_summary\t\"This study is part of previous epidemiologic project, including a population-based survey (Sao Paulo Ageing & Health study (SPAH Study). The data from this study was collected between 2015 to 2016 and involved elderly women (ages ≥65 yeas) living in the Butanta district, Sao Paulo. The purpose of the study was identification of association between transcriptome and the osteo metabolism diseases phenotype, like osteoporosis, vertebral fracture and coronary calcification.\"\n", "!Series_summary\t\"Peripheral blood cells suffer alterations in the gene expression pattern in response to perturbations caused by calcium metabolism diseases. The purpose of this study is to identify possible molecular markers associated with osteoporosis, vertebral fractures and coronary calcification in elderly women from community from Brazilian SPAH study. Vertebral fractures were the most common clinical manifestation of osteoporosis and coronary calcifications were associated with high morbimortality.\"\n", "!Series_overall_design\t\"Fasting blood samples were withdrawn from community elderly women with osteo metabolism diseases. RNA was extracted from peripheral total blood, and hybridized into Affymetrix microarrays.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: female'], 1: ['age (years): 76', 'age (years): 77', 'age (years): 75', 'age (years): 80', 'age (years): 82', 'age (years): 83', 'age (years): 78', 'age (years): 74', 'age (years): 81', 'age (years): 91', 'age (years): 79', 'age (years): 88', 'age (years): 87', 'age (years): 86', 'age (years): 70', 'age (years): 85', 'age (years): 73', 'age (years): 84'], 2: [nan, 'height (cm): 153']}\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": "01172cd7", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "d58501e4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:31.063220Z", "iopub.status.busy": "2025-03-25T05:40:31.063091Z", "iopub.status.idle": "2025-03-25T05:40:31.068581Z", "shell.execute_reply": "2025-03-25T05:40:31.068269Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Skipping clinical feature extraction as trait data is unavailable.\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains gene expression data from microarrays\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# For height (our trait variable)\n", "# Looking at sample characteristics dictionary, height is in key 2\n", "# However, there appears to be only one sample with height data - not sufficient for analysis\n", "trait_row = None # Setting to None since effective data is unavailable\n", "\n", "# For age\n", "# Age is available in key 1\n", "age_row = 1 \n", "\n", "# For gender\n", "# Looking at the dictionary, all samples are female (key 0)\n", "# Since there's only one value, gender is constant and not useful for our analysis\n", "gender_row = None \n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"Convert height values to continuous numeric format.\"\"\"\n", " if pd.isna(value):\n", " return None\n", " # Extract numeric value after colon\n", " try:\n", " # Extract the height value in cm\n", " if \"height (cm):\" in value:\n", " height_value = float(value.split(':')[1].strip())\n", " return height_value\n", " return None\n", " except:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age values to continuous numeric format.\"\"\"\n", " if pd.isna(value):\n", " return None\n", " try:\n", " # Extract the age in years\n", " if \"age (years):\" in value:\n", " age_value = float(value.split(':')[1].strip())\n", " return age_value\n", " return None\n", " except:\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"\n", " Convert gender values to binary format (0 for female, 1 for male).\n", " Not used in this dataset since gender is constant (all female).\n", " \"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " value_lower = value.lower()\n", " if \"female\" in value_lower:\n", " return 0\n", " elif \"male\" in value_lower:\n", " return 1\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "if trait_row is not None:\n", " # We've already loaded clinical_data in a previous step\n", " clinical_selected = 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 processed clinical data\n", " preview = preview_df(clinical_selected)\n", " print(\"Preview of processed clinical data:\")\n", " print(preview)\n", " \n", " # Save the processed clinical data\n", " clinical_selected.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "else:\n", " print(f\"Skipping clinical feature extraction as trait data is unavailable.\")\n" ] }, { "cell_type": "markdown", "id": "07b672d6", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "d452be84", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:31.069810Z", "iopub.status.busy": "2025-03-25T05:40:31.069692Z", "iopub.status.idle": "2025-03-25T05:40:31.828320Z", "shell.execute_reply": "2025-03-25T05:40:31.827966Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix table marker not found in first 100 lines\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "KeyError: \"Only a column name can be used for the key in a dtype mappings argument. 'ID' not found in columns.\"\n", "\n", "Trying alternative approach to read the gene data:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column names: Index(['GSM4602151', 'GSM4602152', 'GSM4602153', 'GSM4602154', 'GSM4602155'], dtype='object')\n", "First 20 row IDs: Index(['TC01000005.hg.1', 'TC01000006.hg.1', 'TC01000007.hg.1',\n", " 'TC01000008.hg.1', 'TC01000009.hg.1', 'TC01000010.hg.1',\n", " 'TC01000011.hg.1', 'TC01000012.hg.1', 'TC01000013.hg.1',\n", " 'TC01000014.hg.1', 'TC01000015.hg.1', 'TC01000016.hg.1',\n", " 'TC01000017.hg.1', 'TC01000018.hg.1', 'TC01000019.hg.1',\n", " 'TC01000020.hg.1', 'TC01000021.hg.1', 'TC01000022.hg.1',\n", " 'TC01000023.hg.1', 'TC01000024.hg.1'],\n", " 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", "# 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": "b49887df", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "c954c50b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:31.829671Z", "iopub.status.busy": "2025-03-25T05:40:31.829549Z", "iopub.status.idle": "2025-03-25T05:40:31.831487Z", "shell.execute_reply": "2025-03-25T05:40:31.831191Z" } }, "outputs": [], "source": [ "# Based on the identifiers shown in gene expression data (TC01000005.hg.1, TC01000006.hg.1, etc.), \n", "# these appear to be Affymetrix Transcriptome Analysis Console (TAC) probeset IDs, not standard human gene symbols.\n", "# These identifiers will need to be mapped to human gene symbols for meaningful analysis.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "160037f6", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "c7dc6e37", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:31.832665Z", "iopub.status.busy": "2025-03-25T05:40:31.832552Z", "iopub.status.idle": "2025-03-25T05:40:34.066884Z", "shell.execute_reply": "2025-03-25T05:40:34.066497Z" } }, "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 = GSE152073\n", "Line 6: !Series_title = Gene expression data from Brazilian SPAH study\n", "Line 7: !Series_geo_accession = GSE152073\n", "Line 8: !Series_status = Public on Jun 10 2020\n", "Line 9: !Series_submission_date = Jun 09 2020\n", "Line 10: !Series_last_update_date = Sep 29 2021\n", "Line 11: !Series_pubmed_id = 32602654\n", "Line 12: !Series_pubmed_id = 34493690\n", "Line 13: !Series_summary = This study is part of previous epidemiologic project, including a population-based survey (Sao Paulo Ageing & Health study (SPAH Study). The data from this study was collected between 2015 to 2016 and involved elderly women (ages ≥65 yeas) living in the Butanta district, Sao Paulo. The purpose of the study was identification of association between transcriptome and the osteo metabolism diseases phenotype, like osteoporosis, vertebral fracture and coronary calcification.\n", "Line 14: !Series_summary = Peripheral blood cells suffer alterations in the gene expression pattern in response to perturbations caused by calcium metabolism diseases. The purpose of this study is to identify possible molecular markers associated with osteoporosis, vertebral fractures and coronary calcification in elderly women from community from Brazilian SPAH study. Vertebral fractures were the most common clinical manifestation of osteoporosis and coronary calcifications were associated with high morbimortality.\n", "Line 15: !Series_overall_design = Fasting blood samples were withdrawn from community elderly women with osteo metabolism diseases. RNA was extracted from peripheral total blood, and hybridized into Affymetrix microarrays.\n", "Line 16: !Series_type = Expression profiling by array\n", "Line 17: !Series_contributor = L,H,Jales Neto\n", "Line 18: !Series_contributor = Z,,Wicik\n", "Line 19: !Series_contributor = G,H,Torres\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': ['TC01000001.hg.1', 'TC01000002.hg.1', 'TC01000003.hg.1', 'TC01000004.hg.1', 'TC01000005.hg.1'], 'probeset_id': ['TC01000001.hg.1', 'TC01000002.hg.1', 'TC01000003.hg.1', 'TC01000004.hg.1', 'TC01000005.hg.1'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+', '+', '+'], 'start': ['11869', '29554', '69091', '160446', '317811'], 'stop': ['14409', '31109', '70008', '161525', '328581'], 'total_probes': [49, 60, 30, 30, 191], 'gene_assignment': ['NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000456328 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // 9p24.3 // 100287596 /// ENST00000456328 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102', 'ENST00000408384 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000408384 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000408384 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000408384 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// ENST00000469289 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000469289 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000469289 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000469289 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// ENST00000473358 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000473358 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000473358 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000473358 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// OTTHUMT00000002841 // OTTHUMG00000000959 // NULL // --- // --- /// OTTHUMT00000002841 // RP11-34P13.3 // NULL // --- // --- /// OTTHUMT00000002840 // OTTHUMG00000000959 // NULL // --- // --- /// OTTHUMT00000002840 // RP11-34P13.3 // NULL // --- // ---', 'NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// OTTHUMT00000003223 // OR4F5 // NULL // --- // ---', 'OTTHUMT00000007169 // OTTHUMG00000002525 // NULL // --- // --- /// OTTHUMT00000007169 // RP11-34P13.9 // NULL // --- // ---', 'NR_028322 // LOC100132287 // uncharacterized LOC100132287 // 1p36.33 // 100132287 /// NR_028327 // LOC100133331 // uncharacterized LOC100133331 // 1p36.33 // 100133331 /// ENST00000425496 // LOC101060495 // uncharacterized LOC101060495 // --- // 101060495 /// ENST00000425496 // LOC101060494 // uncharacterized LOC101060494 // --- // 101060494 /// ENST00000425496 // LOC101059936 // uncharacterized LOC101059936 // --- // 101059936 /// ENST00000425496 // LOC100996502 // uncharacterized LOC100996502 // --- // 100996502 /// ENST00000425496 // LOC100996328 // uncharacterized LOC100996328 // --- // 100996328 /// ENST00000425496 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// NR_028325 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// OTTHUMT00000346878 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346878 // RP4-669L17.10 // NULL // --- // --- /// OTTHUMT00000346879 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346879 // RP4-669L17.10 // NULL // --- // --- /// OTTHUMT00000346880 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346880 // RP4-669L17.10 // NULL // --- // --- /// OTTHUMT00000346881 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346881 // RP4-669L17.10 // NULL // --- // ---'], 'mrna_assignment': ['NR_046018 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 (DDX11L1), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000456328 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aaa.3 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc010nxq.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc010nxr.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0', 'ENST00000408384 // ENSEMBL // ncrna:miRNA chromosome:GRCh37:1:30366:30503:1 gene:ENSG00000221311 gene_biotype:miRNA transcript_biotype:miRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000469289 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:30267:31109:1 gene:ENSG00000243485 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000473358 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:29554:31097:1 gene:ENSG00000243485 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000002841 // Havana transcript // cdna:all chromosome:VEGA52:1:30267:31109:1 Gene:OTTHUMG00000000959 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000002840 // Havana transcript // cdna:all chromosome:VEGA52:1:29554:31097:1 Gene:OTTHUMG00000000959 // chr1 // 100 // 100 // 0 // --- // 0', 'NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // cdna:known chromosome:GRCh37:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aal.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000003223 // Havana transcript // cdna:all chromosome:VEGA52:1:69091:70008:1 Gene:OTTHUMG00000001094 // chr1 // 100 // 100 // 0 // --- // 0', 'ENST00000496488 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:160446:161525:1 gene:ENSG00000241599 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000007169 // Havana transcript // cdna:all chromosome:VEGA52:1:160446:161525:1 Gene:OTTHUMG00000002525 // chr1 // 100 // 100 // 0 // --- // 0', 'NR_028322 // RefSeq // Homo sapiens uncharacterized LOC100132287 (LOC100132287), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// NR_028327 // RefSeq // Homo sapiens uncharacterized LOC100133331 (LOC100133331), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000425496 // ENSEMBL // ensembl:lincRNA chromosome:GRCh37:1:324756:328453:1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000426316 // ENSEMBL // [retired] cdna:known chromosome:GRCh37:1:317811:328455:1 gene:ENSG00000240876 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 100 // 0 // --- // 0 /// NR_028325 // RefSeq // Homo sapiens uncharacterized LOC100132062 (LOC100132062), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// uc009vjk.2 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc021oeh.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc021oei.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346906 // Havana transcript // [retired] cdna:all chromosome:VEGA50:1:317811:328455:1 Gene:OTTHUMG00000156972 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346878 // Havana transcript // cdna:all chromosome:VEGA52:1:320162:321056:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346879 // Havana transcript // cdna:all chromosome:VEGA52:1:320162:324461:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346880 // Havana transcript // cdna:all chromosome:VEGA52:1:317720:324873:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346881 // Havana transcript // cdna:all chromosome:VEGA52:1:322672:324955:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0'], 'swissprot': ['NR_046018 // B7ZGX0 /// NR_046018 // B7ZGX2 /// NR_046018 // B7ZGX7 /// NR_046018 // B7ZGX8 /// ENST00000456328 // B7ZGX0 /// ENST00000456328 // B7ZGX2 /// ENST00000456328 // B7ZGX3 /// ENST00000456328 // B7ZGX7 /// ENST00000456328 // B7ZGX8 /// ENST00000456328 // Q6ZU42', '---', 'NM_001005484 // Q8NH21 /// ENST00000335137 // Q8NH21', '---', 'NR_028325 // B4DYM5 /// NR_028325 // B4E0H4 /// NR_028325 // B4E3X0 /// NR_028325 // B4E3X2 /// NR_028325 // Q6ZQS4'], 'unigene': ['NR_046018 // Hs.714157 // testis| normal| adult /// ENST00000456328 // Hs.719844 // brain| testis| normal /// ENST00000456328 // Hs.714157 // testis| normal| adult /// ENST00000456328 // Hs.618434 // testis| normal', 'ENST00000469289 // Hs.622486 // eye| normal| adult /// ENST00000469289 // Hs.729632 // testis| normal /// ENST00000469289 // Hs.742718 // testis /// ENST00000473358 // Hs.622486 // eye| normal| adult /// ENST00000473358 // Hs.729632 // testis| normal /// ENST00000473358 // Hs.742718 // testis', 'NM_001005484 // Hs.554500 // --- /// ENST00000335137 // Hs.554500 // ---', '---', 'NR_028322 // Hs.446409 // adrenal gland| blood| bone| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| lymph node| mouth| pharynx| placenta| prostate| skin| testis| thymus| thyroid| uterus| bladder carcinoma| chondrosarcoma| colorectal tumor| germ cell tumor| head and neck tumor| kidney tumor| leukemia| lung tumor| normal| primitive neuroectodermal tumor of the CNS| uterine tumor|embryoid body| blastocyst| fetus| neonate| adult /// NR_028327 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000425496 // Hs.744556 // mammary gland| normal| adult /// ENST00000425496 // Hs.660700 // eye| placenta| testis| normal| adult /// ENST00000425496 // Hs.518952 // blood| brain| intestine| lung| mammary gland| mouth| muscle| pharynx| placenta| prostate| spleen| testis| thymus| thyroid| trachea| breast (mammary gland) tumor| colorectal tumor| head and neck tumor| leukemia| lung tumor| normal| prostate cancer| fetus| adult /// ENST00000425496 // Hs.742131 // testis| normal| adult /// ENST00000425496 // Hs.636102 // uterus| uterine tumor /// ENST00000425496 // Hs.646112 // brain| intestine| larynx| lung| mouth| prostate| testis| thyroid| colorectal tumor| head and neck tumor| lung tumor| normal| prostate cancer| adult /// ENST00000425496 // Hs.647795 // brain| lung| lung tumor| adult /// ENST00000425496 // Hs.684307 // --- /// ENST00000425496 // Hs.720881 // testis| normal /// ENST00000425496 // Hs.729353 // brain| lung| placenta| testis| trachea| lung tumor| normal| fetus| adult /// ENST00000425496 // Hs.735014 // ovary| ovarian tumor /// NR_028325 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| kidney tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult'], 'category': ['main', 'main', 'main', 'main', 'main'], 'locus type': ['Coding', 'Coding', 'Coding', 'Coding', 'Coding'], 'notes': ['---', '---', '---', '---', '2 retired transcript(s) from ENSEMBL, Havana transcript'], 'SPOT_ID': ['chr1(+):11869-14409', 'chr1(+):29554-31109', 'chr1(+):69091-70008', 'chr1(+):160446-161525', 'chr1(+):317811-328581']}\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": "3fe4cdb2", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "5496c79f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:34.068220Z", "iopub.status.busy": "2025-03-25T05:40:34.068101Z", "iopub.status.idle": "2025-03-25T05:40:34.689347Z", "shell.execute_reply": "2025-03-25T05:40:34.688989Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Preview of gene data after mapping:\n", "(49166, 90)\n", "Index(['A-', 'A-I', 'A-II', 'A-IV', 'A-V', 'A0', 'A1', 'A1-', 'A10', 'A11'], dtype='object', name='Gene')\n" ] } ], "source": [ "# 1. Looking at the gene identifiers and annotation data:\n", "# - Gene expression data has probe IDs like 'TC01000005.hg.1' in its index\n", "# - Gene annotation dataframe has the same identifiers in the 'ID' column\n", "# - The gene symbols are in the 'gene_assignment' column, which contains gene information\n", "\n", "# 2. Extract the gene mapping data\n", "# The 'ID' column contains the probe identifiers\n", "# The 'gene_assignment' column contains gene information including gene symbols\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# Preview the mapped gene data\n", "print(\"\\nPreview of gene data after mapping:\")\n", "print(gene_data.shape)\n", "print(gene_data.index[:10]) # Print first 10 gene symbols\n" ] }, { "cell_type": "markdown", "id": "ed77384c", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "d2bc3926", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:40:34.691076Z", "iopub.status.busy": "2025-03-25T05:40:34.690953Z", "iopub.status.idle": "2025-03-25T05:40:35.145189Z", "shell.execute_reply": "2025-03-25T05:40:35.144848Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (46362, 90)\n", "Gene data shape after normalization: (0, 90)\n", "Normalized gene data saved to ../../output/preprocess/Height/gene_data/GSE152073.csv\n", "Clinical data saved to ../../output/preprocess/Height/clinical_data/GSE152073.csv\n", "Linked data shape: (90, 2)\n", "Abnormality detected in the cohort: GSE152073. Preprocessing failed.\n", "Dataset usability: False\n", "Dataset does not contain Height data and cannot be used for association studies.\n" ] } ], "source": [ "import numpy as np\n", "import os\n", "import gzip\n", "\n", "# 1. Extract gene expression data using the alternative approach that worked in Step 3\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", " gene_data = pd.read_csv(file, sep='\\t', index_col=0)\n", "\n", "# Check if we have gene data before proceeding\n", "if gene_data.empty:\n", " print(\"No gene expression data found in the matrix file.\")\n", " is_gene_available = False\n", "else:\n", " is_gene_available = True\n", " print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", "\n", " # Normalize gene symbols using the NCBI Gene database information\n", " try:\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 to the output file\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", " except Exception as e:\n", " print(f\"Error normalizing gene data: {e}\")\n", " is_gene_available = False\n", " normalized_gene_data = gene_data # Use original data if normalization fails\n", "\n", "# 2. Link clinical and genetic data\n", "# In Step 2, we determined that Height data is not available in this dataset (trait_row = None)\n", "# Create a minimal clinical dataframe with the trait column (containing NaNs)\n", "if is_gene_available:\n", " sample_ids = gene_data.columns\n", " minimal_clinical_df = pd.DataFrame(index=sample_ids)\n", " minimal_clinical_df[trait] = np.nan # Add the trait column with NaN values\n", "\n", " # If we have age and gender data from Step 2, add those columns\n", " if age_row is not None:\n", " minimal_clinical_df['Age'] = get_feature_data(clinical_data, age_row, 'Age', convert_age).iloc[0]\n", "\n", " if gender_row is not None:\n", " minimal_clinical_df['Gender'] = get_feature_data(clinical_data, gender_row, 'Gender', convert_gender).iloc[0]\n", "\n", " minimal_clinical_df.index.name = 'Sample'\n", "\n", " # Save this minimal clinical data for reference\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " minimal_clinical_df.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", " # Create a linked dataset \n", " if is_gene_available and normalized_gene_data is not None:\n", " linked_data = pd.concat([minimal_clinical_df, normalized_gene_data.T], axis=1)\n", " linked_data.index.name = 'Sample'\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " else:\n", " linked_data = minimal_clinical_df\n", " print(\"No gene data to link with clinical data.\")\n", "else:\n", " # Create a minimal dataframe with just the trait for the validation step\n", " linked_data = pd.DataFrame({trait: [np.nan]})\n", " print(\"No gene data available, creating minimal dataframe for validation.\")\n", "\n", "# 4 & 5. Validate and save cohort information\n", "# Since trait_row was None in Step 2, we know Height data is not available\n", "is_trait_available = False # Height data is not available\n", "\n", "note = \"Dataset contains gene expression data but no Height measurements. This dataset is not usable for studying Height associations.\"\n", "\n", "# For datasets without trait data, we set is_biased to False\n", "# This indicates the dataset is not usable due to missing trait data, not due to bias\n", "is_biased = False\n", "\n", "# Final validation\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=is_gene_available, \n", " is_trait_available=is_trait_available, \n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=note\n", ")\n", "\n", "# 6. Since there is no trait data, the dataset is not usable for our association study\n", "# So we should not save it to out_data_file\n", "print(f\"Dataset usability: {is_usable}\")\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Dataset does not contain Height data and cannot be used for association studies.\")" ] } ], "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 }