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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "825ca413",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:27:37.662522Z",
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"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 = \"Amyotrophic_Lateral_Sclerosis\"\n",
"cohort = \"GSE118336\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Amyotrophic_Lateral_Sclerosis\"\n",
"in_cohort_dir = \"../../input/GEO/Amyotrophic_Lateral_Sclerosis/GSE118336\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/GSE118336.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE118336.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE118336.csv\"\n",
"json_path = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "49708c1f",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "33698469",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:27:37.830260Z",
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}
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"outputs": [
{
"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": [
"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": "f7100a7c",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4e3ca3b6",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:27:38.059732Z",
"iopub.status.busy": "2025-03-25T06:27:38.059619Z",
"iopub.status.idle": "2025-03-25T06:27:38.067723Z",
"shell.execute_reply": "2025-03-25T06:27:38.067425Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{0: [0.0], 1: [1.0], 2: [1.0]}\n",
"Clinical data saved to: ../../output/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE118336.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import json\n",
"from typing import Dict, Any, Optional, Callable\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# The dataset seems to be HTA2.0 (human transcriptome array) analysis which implies gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability Analysis\n",
"\n",
"# For trait (ALS status)\n",
"# The genotype key (index 1) contains FUS mutation information which is relevant for ALS\n",
"trait_row = 1 # 'genotype' contains information about FUS mutation (WT vs H517D)\n",
"\n",
"# For age - Not available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# For gender - Not available in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert the FUS genotype to binary trait value (0 for control, 1 for disease)\"\"\"\n",
" if 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",
" # FUSWT/WT is control (0), any H517D mutation indicates disease (1)\n",
" if 'FUSWT/WT' in value:\n",
" return 0\n",
" elif 'H517D' in value: # Either heterozygous or homozygous H517D mutation\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to numeric value\"\"\"\n",
" # Age data not available\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary (0 female, 1 male)\"\"\"\n",
" # Gender data 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",
"# Save initial validation results\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",
" # Create a DataFrame from the sample characteristics dictionary\n",
" # The dictionary format is {row_index: [values_for_samples]}\n",
" # We need to transform this into a proper DataFrame\n",
" \n",
" # Sample characteristics from previous step\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 sample characteristics to a DataFrame format\n",
" # First, determine the number of samples from the row with most entries\n",
" num_samples = max(len(values) for values in sample_chars.values())\n",
" \n",
" # Create a DataFrame with rows for each characteristic and columns for each sample\n",
" clinical_data = pd.DataFrame(index=sample_chars.keys(), columns=range(num_samples))\n",
" \n",
" # Fill in the values where available, leaving NaN for missing values\n",
" for row_idx, values in sample_chars.items():\n",
" for col_idx, value in enumerate(values):\n",
" if col_idx < num_samples:\n",
" clinical_data.iloc[row_idx, col_idx] = value\n",
" \n",
" # Extract clinical features using the library function\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 data\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save the clinical data\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to: {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "58e631f8",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "fcf1b5dd",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:27:38.068846Z",
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"shell.execute_reply": "2025-03-25T06:27:38.410633Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\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",
"\n",
"Gene data dimensions: 70523 genes × 60 samples\n"
]
}
],
"source": [
"# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract the gene expression data from the matrix file\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers)\n",
"print(\"\\nFirst 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n",
"\n",
"# 4. Print the dimensions of the gene expression data\n",
"print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
"is_gene_available = True\n"
]
},
{
"cell_type": "markdown",
"id": "5b2e3171",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "467f411d",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:27:38.412435Z",
"iopub.status.busy": "2025-03-25T06:27:38.412309Z",
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"shell.execute_reply": "2025-03-25T06:27:38.413906Z"
}
},
"outputs": [],
"source": [
"# Review the gene identifiers\n",
"# These identifiers appear to be probe IDs (ending with '_st') from an Affymetrix microarray\n",
"# They are not standard human gene symbols and will need to be mapped to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "fbf376be",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "cf295029",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:27:38.415318Z",
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"shell.execute_reply": "2025-03-25T06:27:46.635175Z"
}
},
"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. First get the file paths using geo_get_relevant_filepaths function\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. 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",
"# 3. 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": "49735edc",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First few probe IDs in gene_data:\n",
"Index(['2824546_st', '2824549_st', '2824551_st', '2824554_st', '2827992_st'], dtype='object', name='ID')\n",
"\n",
"Annotation column names:\n",
"Index(['ID', 'probeset_id', 'seqname', 'strand', 'start', 'stop',\n",
" 'total_probes', 'gene_assignment', 'mrna_assignment', 'swissprot',\n",
" 'unigene', 'category', 'locus type', 'notes', 'SPOT_ID'],\n",
" dtype='object')\n",
"\n",
"Sample gene annotation with ID columns:\n",
" ID gene_assignment\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Mapping dataframe preview:\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",
"Mapping dataframe shape: (70753, 2)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data after mapping:\n",
"Shape: (71528, 60)\n",
"First few gene symbols:\n",
"Index(['A-', 'A-2', 'A-52', 'A-575C2', 'A-E', 'A-I', 'A-II', 'A-IV', 'A-V',\n",
" 'A0'],\n",
" dtype='object', name='Gene')\n",
"\n",
"After normalizing gene symbols:\n",
"Shape: (24018, 60)\n",
"First few normalized gene symbols:\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2ML1-AS1',\n",
" 'A2ML1-AS2', 'A2MP1', 'A4GALT'],\n",
" dtype='object', name='Gene')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data saved to: ../../output/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE118336.csv\n"
]
}
],
"source": [
"# 1. Analyze gene identifiers to match between gene expression data and annotation data\n",
"# Looking at the gene expression data, we have IDs like 2824546_st\n",
"# Looking at the gene annotation data, we need to identify columns containing similar identifiers\n",
"\n",
"# Check the first few rows of gene_data index to see what the probe IDs look like\n",
"print(\"First few probe IDs in gene_data:\")\n",
"print(gene_data.index[:5])\n",
"\n",
"# Looking at the annotation data, we need to find which column contains the probe IDs that match\n",
"# and which column contains the gene symbols for mapping\n",
"print(\"\\nAnnotation column names:\")\n",
"print(gene_annotation.columns)\n",
"\n",
"# Since the probe IDs in gene_data end with \"_st\" but the annotation shows different format,\n",
"# we need to find which annotation columns might contain the relevant mapping information\n",
"\n",
"# 2. Examine gene annotation data to find proper mapping columns\n",
"# Display sample rows with relevant columns to help identify the mapping\n",
"print(\"\\nSample gene annotation with ID columns:\")\n",
"print(gene_annotation[['ID', 'gene_assignment']].head(3))\n",
"\n",
"# Based on the examination, we can see that 'ID' contains identifiers (which might need transformation)\n",
"# and 'gene_assignment' contains gene symbol information.\n",
"\n",
"# 3. Extract mapping data and apply it\n",
"# First, map probe IDs in gene_data to annotation IDs\n",
"# Since Affymetrix HTA 2.0 arrays have a specific format, \n",
"# we need to ensure our mapping works between the two datasets\n",
"\n",
"# Create a mapping dataframe from probe IDs to gene symbols\n",
"mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')\n",
"\n",
"print(\"\\nMapping dataframe preview:\")\n",
"print(mapping_df.head())\n",
"print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n",
"\n",
"# Apply the mapping to convert probe-level measurements to gene-level expression\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"print(\"\\nGene expression data after mapping:\")\n",
"print(f\"Shape: {gene_data.shape}\")\n",
"print(\"First few gene symbols:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Normalize gene symbols to ensure consistency\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"\n",
"print(\"\\nAfter normalizing gene symbols:\")\n",
"print(f\"Shape: {gene_data.shape}\")\n",
"print(\"First few normalized gene symbols:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Save gene data to CSV\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\"\\nGene expression data saved to: {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "13d6b95e",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape after normalization: (24018, 60)\n",
"First 5 gene symbols after normalization: Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1'], dtype='object', name='Gene')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE118336.csv\n",
"Clinical data loaded from ../../output/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE118336.csv\n",
"Clinical data shape: (1, 3)\n",
"Clinical data preview: {'0': [0.0], '1': [1.0], '2': [1.0]}\n",
"Linked data shape before handling missing values: (63, 24019)\n",
"Data shape after handling missing values: (0, 1)\n",
"Quartiles for 'Amyotrophic_Lateral_Sclerosis':\n",
" 25%: nan\n",
" 50% (Median): nan\n",
" 75%: nan\n",
"Min: nan\n",
"Max: nan\n",
"The distribution of the feature 'Amyotrophic_Lateral_Sclerosis' in this dataset is fine.\n",
"\n",
"Data shape after removing biased features: (0, 1)\n",
"Abnormality detected in the cohort: GSE118336. Preprocessing failed.\n",
"A new JSON file was created at: ../../output/preprocess/Amyotrophic_Lateral_Sclerosis/cohort_info.json\n",
"Dataset deemed not usable for associational studies.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the index of gene expression data\n",
"# (Already done in the previous step, but we're keeping this for clarity)\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
"print(f\"First 5 gene symbols after normalization: {normalized_gene_data.index[:5]}\")\n",
"\n",
"# Save the normalized gene data (already saved in previous step, but we'll keep it for clarity)\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 the previously saved clinical data from Step 2\n",
"# This is more reliable than reprocessing the original data\n",
"try:\n",
" selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
" is_trait_available = True\n",
" print(f\"Clinical data loaded from {out_clinical_data_file}\")\n",
" print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
" print(f\"Clinical data preview: {preview_df(selected_clinical_df, n=3)}\")\n",
"except Exception as e:\n",
" print(f\"Error loading clinical data: {e}\")\n",
" is_trait_available = False\n",
" selected_clinical_df = pd.DataFrame()\n",
"\n",
"# Link clinical and genetic data if trait is available\n",
"if is_trait_available:\n",
" # Link clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
" print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n",
" \n",
" # 3. Handle missing values\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" # 4. Determine if trait and demographic features are biased\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" print(f\"Data shape after removing biased features: {linked_data.shape}\")\n",
"else:\n",
" print(\"Trait data is unavailable in this dataset.\")\n",
" is_biased = True # Dataset can't be used without trait data\n",
" linked_data = pd.DataFrame() # Empty DataFrame\n",
"\n",
"# 5. Validate and save cohort info\n",
"# If linked_data is empty because trait is not available, use sample IDs from gene data for metadata\n",
"sample_df = pd.DataFrame(index=normalized_gene_data.columns) if linked_data.empty else linked_data\n",
" \n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased if is_trait_available else None,\n",
" df=sample_df,\n",
" note=\"Dataset contains iPSC-derived motor neuron gene expression data from FUS-H517D mutation carriers related to ALS.\"\n",
")\n",
"\n",
"# 6. Save linked data if 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 deemed not usable for associational studies.\")"
]
}
],
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