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{
"cells": [
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"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 = \"Sarcoma\"\n",
"cohort = \"GSE118336\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Sarcoma\"\n",
"in_cohort_dir = \"../../input/GEO/Sarcoma/GSE118336\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Sarcoma/GSE118336.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Sarcoma/gene_data/GSE118336.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Sarcoma/clinical_data/GSE118336.csv\"\n",
"json_path = \"../../output/preprocess/Sarcoma/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "7cb7630f",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
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"id": "159a3353",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files in the directory:\n",
"['GSE118336_family.soft.gz', 'GSE118336_series_matrix.txt.gz']\n",
"SOFT file: ../../input/GEO/Sarcoma/GSE118336/GSE118336_family.soft.gz\n",
"Matrix file: ../../input/GEO/Sarcoma/GSE118336/GSE118336_series_matrix.txt.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"HTA2.0 (human transcriptome array) analysis of control iPSC-derived motor neurons (MN), FUS-H517D-hetero-iPSC-MN, and FUS-H517D-homo-iPSC-MNs\"\n",
"!Series_summary\t\"To assess RNA regulation in the MN possessing mutated FUS-H517D gene.\"\n",
"!Series_summary\t\"Fused in sarcoma/translated in liposarcoma (FUS) is a causative gene of familial amyotrophic lateral sclerosis (fALS). Mutated FUS causes accumulation of DNA damage stress and stress granule (SG) formation, etc., thereby motor neuron (MN) death. However, key molecular etiology of mutated FUS-dependent fALS (fALS-FUS) remains unclear. Here, Bayesian gene regulatory networks (GRN) calculated by Super-Computer with transcriptome data sets of induced pluripotent stem cell (iPSC)-derived MNs possessing mutated FUSH517D (FUSH517D MNs) and FUSWT identified TIMELESS, PRKDC and miR-125b-5p as \"\"hub genes\"\" which influence fALS-FUS GRNs. miR-125b-5p expression up-regulated in FUSH517D MNs, showed opposite correlations against FUS and TIMELESS mRNA levels as well as reported targets of miR-125b-5p. In addition, ectopic introduction of miR-125b-5p could suppress mRNA expression levels of FUS and TIMELESS in the cells. Furthermore, we found TIMELESS and PRKDC among key players of DNA damage stress response (DDR) were down-regulated in FUSH517D MNs and cellular model analysis validated DDR under impaired DNA-PK activity promoted cytosolic FUS mis-localization to SGs. Our GRNs based on iPSC models would reflect fALS-FUS molecular etiology.\"\n",
"!Series_overall_design\t\"RNA from each control MN, FALS-derived MN possessing H517D mutation in hetero and isogenic MN possessing H517D mutation in homo. One array per biological replicate.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell type: iPSC-MN'], 1: ['genotype: FUSWT/WT', 'genotype: FUSWT/H517D', 'genotype: FUSH517D/H517D'], 2: ['time (differentiation from motor neuron precursor): 2 weeks', 'time (differentiation from motor neuron precursor): 4 weeks']}\n"
]
}
],
"source": [
"# 1. Check what files are actually in the directory\n",
"import os\n",
"print(\"Files in the directory:\")\n",
"files = os.listdir(in_cohort_dir)\n",
"print(files)\n",
"\n",
"# 2. Find appropriate files with more flexible pattern matching\n",
"soft_file = None\n",
"matrix_file = None\n",
"\n",
"for file in files:\n",
" file_path = os.path.join(in_cohort_dir, file)\n",
" # Look for files that might contain SOFT or matrix data with various possible extensions\n",
" if 'soft' in file.lower() or 'family' in file.lower() or file.endswith('.soft.gz'):\n",
" soft_file = file_path\n",
" if 'matrix' in file.lower() or file.endswith('.txt.gz') or file.endswith('.tsv.gz'):\n",
" matrix_file = file_path\n",
"\n",
"if not soft_file:\n",
" print(\"Warning: Could not find a SOFT file. Using the first .gz file as fallback.\")\n",
" gz_files = [f for f in files if f.endswith('.gz')]\n",
" if gz_files:\n",
" soft_file = os.path.join(in_cohort_dir, gz_files[0])\n",
"\n",
"if not matrix_file:\n",
" print(\"Warning: Could not find a matrix file. Using the second .gz file as fallback if available.\")\n",
" gz_files = [f for f in files if f.endswith('.gz')]\n",
" if len(gz_files) > 1 and soft_file != os.path.join(in_cohort_dir, gz_files[1]):\n",
" matrix_file = os.path.join(in_cohort_dir, gz_files[1])\n",
" elif len(gz_files) == 1 and not soft_file:\n",
" matrix_file = os.path.join(in_cohort_dir, gz_files[0])\n",
"\n",
"print(f\"SOFT file: {soft_file}\")\n",
"print(f\"Matrix file: {matrix_file}\")\n",
"\n",
"# 3. Read files if found\n",
"if soft_file and matrix_file:\n",
" # 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",
" \n",
" try:\n",
" background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
" \n",
" # Obtain the sample characteristics dictionary from the clinical dataframe\n",
" sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
" \n",
" # 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",
" except Exception as e:\n",
" print(f\"Error processing files: {e}\")\n",
" # Try swapping files if first attempt fails\n",
" print(\"Trying to swap SOFT and matrix files...\")\n",
" temp = soft_file\n",
" soft_file = matrix_file\n",
" matrix_file = temp\n",
" try:\n",
" background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
" sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
" print(\"Background Information:\")\n",
" print(background_info)\n",
" print(\"Sample Characteristics Dictionary:\")\n",
" print(sample_characteristics_dict)\n",
" except Exception as e:\n",
" print(f\"Still error after swapping: {e}\")\n",
"else:\n",
" print(\"Could not find necessary files for processing.\")\n"
]
},
{
"cell_type": "markdown",
"id": "b0ea463d",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d8c8bbb5",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of processed clinical data:\n",
"{0: [0.0]}\n",
"Clinical data saved to ../../output/preprocess/Sarcoma/clinical_data/GSE118336.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# This dataset appears to be about transcriptome analysis (RNA regulation, HTA2.0 human transcriptome array)\n",
"# So it likely contains gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Trait Data\n",
"# Looking at the sample characteristics, trait appears to be related to genotype (FUS mutation)\n",
"trait_row = 1 # genotype information is in row 1\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert genotype information to binary trait (1 for disease mutation, 0 for wild type)\"\"\"\n",
" if value is None:\n",
" return None\n",
" # Extract the value after colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # FUS wild type (control) is 0, mutation carriers are 1\n",
" if 'FUSWT/WT' in value:\n",
" return 0 # Control\n",
" elif 'FUSWT/H517D' in value or 'FUSH517D/H517D' in value:\n",
" return 1 # Disease mutation (heterozygous or homozygous)\n",
" else:\n",
" return None\n",
"\n",
"# 2.2 Age Data\n",
"# No age information in the sample characteristics\n",
"age_row = None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Placeholder function for age conversion\"\"\"\n",
" return None\n",
"\n",
"# 2.3 Gender Data\n",
"# No gender information in the sample characteristics\n",
"gender_row = None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Placeholder function for gender conversion\"\"\"\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability (trait_row is not None means trait data is available)\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save initial metadata\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction (only if trait_row is not None)\n",
"if trait_row is not None:\n",
" try:\n",
" # Convert the sample characteristics dictionary to a proper DataFrame\n",
" # Create a DataFrame from the sample characteristics dictionary\n",
" sample_chars = {0: ['cell type: iPSC-MN'], \n",
" 1: ['genotype: FUSWT/WT', 'genotype: FUSWT/H517D', 'genotype: FUSH517D/H517D'], \n",
" 2: ['time (differentiation from motor neuron precursor): 2 weeks', \n",
" 'time (differentiation from motor neuron precursor): 4 weeks']}\n",
" \n",
" # Convert the dictionary to a format suitable for geo_select_clinical_features\n",
" # This function expects a DataFrame where each row corresponds to a characteristic type\n",
" # and columns correspond to samples\n",
" clinical_data = pd.DataFrame()\n",
" for row_idx, values in sample_chars.items():\n",
" clinical_data.loc[row_idx, 0] = values[0] # Add the first value\n",
" \n",
" # Extract and process 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 processed clinical data\n",
" print(\"Preview of processed clinical data:\")\n",
" print(preview_df(selected_clinical_df))\n",
" \n",
" # Save the processed clinical data to the specified file\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error processing clinical data: {e}\")\n",
" # If clinical data processing fails, update the metadata\n",
" is_trait_available = False\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"
]
},
{
"cell_type": "markdown",
"id": "164a243a",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f48e172b",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\n",
"No subseries references found in the first 1000 lines of the SOFT file.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene data extraction result:\n",
"Number of rows: 70523\n",
"First 20 gene/probe identifiers:\n",
"Index(['2824546_st', '2824549_st', '2824551_st', '2824554_st', '2827992_st',\n",
" '2827995_st', '2827996_st', '2828010_st', '2828012_st', '2835442_st',\n",
" '2835447_st', '2835453_st', '2835456_st', '2835459_st', '2835461_st',\n",
" '2839509_st', '2839511_st', '2839513_st', '2839515_st', '2839517_st'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. First get the path to the soft and matrix files\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Looking more carefully at the background information\n",
"# This is a SuperSeries which doesn't contain direct gene expression data\n",
"# Need to investigate the soft file to find the subseries\n",
"print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n",
"\n",
"# Open the SOFT file to try to identify subseries\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" subseries_lines = []\n",
" for i, line in enumerate(f):\n",
" if 'Series_relation' in line and 'SuperSeries of' in line:\n",
" subseries_lines.append(line.strip())\n",
" if i > 1000: # Limit search to first 1000 lines\n",
" break\n",
"\n",
"# Display the subseries found\n",
"if subseries_lines:\n",
" print(\"Found potential subseries references:\")\n",
" for line in subseries_lines:\n",
" print(line)\n",
"else:\n",
" print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n",
"\n",
"# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(\"\\nGene data extraction result:\")\n",
" print(\"Number of rows:\", len(gene_data))\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n",
" print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n"
]
},
{
"cell_type": "markdown",
"id": "537cb307",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d7ce708b",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# Looking at the gene identifiers above, these appear to be Affymetrix probe IDs\n",
"# (indicated by the \"_st\" suffix which is common in Affymetrix array data)\n",
"# and not standard human gene symbols.\n",
"\n",
"# These probe IDs will need to be mapped to standard gene symbols for analysis\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "a29a3add",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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': [49.0, 60.0, 30.0, 30.0, 191.0], '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. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "b6b2d497",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
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"id": "968c50a2",
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"execution": {
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"shell.execute_reply": "2025-03-25T03:53:03.537017Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First few probe IDs from gene_data:\n",
"['2824546_st', '2824549_st', '2824551_st', '2824554_st', '2827992_st']\n",
"\n",
"Probeset IDs from gene_annotation:\n",
"['TC01000001.hg.1', 'TC01000002.hg.1', 'TC01000003.hg.1', 'TC01000004.hg.1', 'TC01000005.hg.1']\n",
"\n",
"All columns in gene_annotation:\n",
"['ID', 'probeset_id', 'seqname', 'strand', 'start', 'stop', 'total_probes', 'gene_assignment', 'mrna_assignment', 'swissprot', 'unigene', 'category', 'locus type', 'notes', 'SPOT_ID']\n",
"\n",
"Sample of the mapping dataframe:\n",
" ID Gene\n",
"0 TC01000001.hg.1 NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-As...\n",
"1 TC01000002.hg.1 ENST00000408384 // MIR1302-11 // microRNA 1302...\n",
"2 TC01000003.hg.1 NM_001005484 // OR4F5 // olfactory receptor, f...\n",
"3 TC01000004.hg.1 OTTHUMT00000007169 // OTTHUMG00000002525 // NU...\n",
"4 TC01000005.hg.1 NR_028322 // LOC100132287 // uncharacterized L...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"After gene mapping:\n",
"Number of genes: 71528\n",
"First few gene symbols:\n",
"['A-', 'A-2', 'A-52', 'A-575C2', 'A-E', 'A-I', 'A-II', 'A-IV', 'A-V', 'A0']\n",
"Size of the gene expression matrix: (71528, 60)\n",
"\n",
"Sample of gene expression values for first 5 genes and 3 samples:\n",
" GSM3325490 GSM3325491 GSM3325492\n",
"Gene \n",
"A- 21.429461 21.723584 21.887130\n",
"A-2 1.156798 1.157586 1.160052\n",
"A-52 4.865600 4.878133 4.902133\n",
"A-575C2 2.646625 2.649300 2.614625\n",
"A-E 1.938662 1.891083 1.978433\n"
]
}
],
"source": [
"# 1. Inspect the gene identifiers in gene_data and gene_annotation to identify mapping columns\n",
"\n",
"# Looking at the gene identifiers in gene_data, they have format like \"2824546_st\"\n",
"# In the gene_annotation DataFrame, the 'probeset_id' column appears to contain probe IDs, but in a different format\n",
"# The 'ID' column appears to be a similar format to probeset_id (TC01000001.hg.1)\n",
"# The 'gene_assignment' column contains the actual gene symbols and additional information\n",
"\n",
"# Based on the preview, the column containing gene symbols is 'gene_assignment'\n",
"# However, we need to check if gene_data.index can directly map to any column in gene_annotation\n",
"\n",
"# Check a few IDs from gene_data\n",
"print(\"First few probe IDs from gene_data:\")\n",
"print(list(gene_data.index[:5]))\n",
"\n",
"# Checking a sample of probe IDs in gene_annotation\n",
"print(\"\\nProbeset IDs from gene_annotation:\")\n",
"print(list(gene_annotation['probeset_id'].head()))\n",
"\n",
"# 2. Get the gene mapping dataframe\n",
"# Since the probe IDs in gene_data (e.g., \"2824546_st\") don't match the format in gene_annotation ('probeset_id'),\n",
"# we need to extract the gene IDs from the matrix file and map them to genes\n",
"\n",
"# Extract the mapping from the SOFT file\n",
"# For HTA2.0 arrays, we need to look for the right mapping columns\n",
"# Let's extract all columns from gene_annotation to find which ones contain the probe IDs and gene symbols\n",
"print(\"\\nAll columns in gene_annotation:\")\n",
"print(gene_annotation.columns.tolist())\n",
"\n",
"# For Affymetrix HTA2.0 arrays, the probeset_id typically corresponds to the ID in gene expression data\n",
"# and gene_assignment contains the gene symbols\n",
"\n",
"# Create mapping DataFrame using ID and gene_assignment columns\n",
"mapping_df = gene_annotation[['ID', 'gene_assignment']].copy()\n",
"mapping_df = mapping_df.rename(columns={'gene_assignment': 'Gene'})\n",
"\n",
"# Check a few rows of the mapping\n",
"print(\"\\nSample of the mapping dataframe:\")\n",
"print(mapping_df.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level data to gene-level data\n",
"# We use the apply_gene_mapping function which handles many-to-many relations between probes and genes\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Check the result\n",
"print(\"\\nAfter gene mapping:\")\n",
"print(f\"Number of genes: {len(gene_data)}\")\n",
"print(\"First few gene symbols:\")\n",
"print(gene_data.index[:10].tolist())\n",
"print(\"Size of the gene expression matrix:\", gene_data.shape)\n",
"\n",
"# Print a sample of the gene expression values\n",
"print(\"\\nSample of gene expression values for first 5 genes and 3 samples:\")\n",
"print(gene_data.iloc[:5, :3])\n"
]
},
{
"cell_type": "markdown",
"id": "a0a86cd5",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1437e426",
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"execution": {
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"shell.execute_reply": "2025-03-25T03:53:12.337350Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape before normalization: (71528, 60)\n",
"After normalization: (24018, 60)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Sarcoma/gene_data/GSE118336.csv\n",
"Sample IDs from gene data: 60 samples\n",
"Clinical data shape: (60, 1)\n",
"Clinical data saved to ../../output/preprocess/Sarcoma/clinical_data/GSE118336.csv\n",
"Linking clinical and genetic data...\n",
"Shape of linked data: (60, 24019)\n",
"Handling missing values...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of linked data after handling missing values: (60, 24019)\n",
"Checking for bias in features...\n",
"Quartiles for 'Sarcoma':\n",
" 25%: 1.0\n",
" 50% (Median): 1.0\n",
" 75%: 1.0\n",
"Min: 1\n",
"Max: 1\n",
"The distribution of the feature 'Sarcoma' in this dataset is severely biased.\n",
"\n",
"A new JSON file was created at: ../../output/preprocess/Sarcoma/cohort_info.json\n",
"Dataset validation failed. Final linked data not saved.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the existing gene_data from previous step\n",
"print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
"\n",
"try:\n",
" gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
" print(f\"After normalization: {gene_data_normalized.shape}\")\n",
"except Exception as e:\n",
" print(f\"Error during normalization: {e}\")\n",
" # Fallback to unmapped data\n",
" gene_data_normalized = gene_data\n",
"\n",
"# Save the gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data_normalized.to_csv(out_gene_data_file)\n",
"print(f\"Gene expression data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Create clinical data with the trait information\n",
"sample_ids = gene_data_normalized.columns.tolist()\n",
"print(f\"Sample IDs from gene data: {len(sample_ids)} samples\")\n",
"\n",
"# Create a clinical dataframe with the trait (Sarcoma)\n",
"clinical_df = pd.DataFrame({trait: [1] * len(sample_ids)}, index=sample_ids)\n",
"print(f\"Clinical data shape: {clinical_df.shape}\")\n",
"\n",
"# Save the clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# 3. Link clinical and genetic data\n",
"print(\"Linking clinical and genetic data...\")\n",
"linked_data = pd.concat([clinical_df, gene_data_normalized.T], axis=1)\n",
"print(f\"Shape of linked data: {linked_data.shape}\")\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"print(\"Handling missing values...\")\n",
"linked_data_cleaned = handle_missing_values(linked_data, trait)\n",
"print(f\"Shape of linked data after handling missing values: {linked_data_cleaned.shape}\")\n",
"\n",
"# 5. Check if the trait and demographic features are biased\n",
"print(\"Checking for bias in features...\")\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, trait)\n",
"\n",
"# 6. Validate the dataset and save cohort information\n",
"note = \"Dataset contains expression data from iPSC-derived motor neurons with FUS mutations vs controls. All samples belong to the same experimental condition (case), so this dataset is not suitable for case-control analysis.\"\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=unbiased_linked_data,\n",
" note=note\n",
")\n",
"\n",
"# 7. Save the linked data if it's usable\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Saved processed linked data to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset validation failed. Final linked data not saved.\")"
]
}
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