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{
"cells": [
{
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"execution": {
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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 = \"Atherosclerosis\"\n",
"cohort = \"GSE109048\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Atherosclerosis\"\n",
"in_cohort_dir = \"../../input/GEO/Atherosclerosis/GSE109048\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Atherosclerosis/GSE109048.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Atherosclerosis/gene_data/GSE109048.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Atherosclerosis/clinical_data/GSE109048.csv\"\n",
"json_path = \"../../output/preprocess/Atherosclerosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "ee8e414d",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c42f5caf",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Platelet gene expression profiling of acute myocardial infarction\"\n",
"!Series_summary\t\"Acute myocardial infarction (AMI) is primarily due to coronary atherosclerotic plaque rupture and subsequent thrombus formation. Platelets play a key role in the genesis and progression of both atherosclerosis and thrombosis. Since platelets are anuclear cells that inherit their mRNA from megakaryocyte precursors and maintain it unchanged during their life span, gene expression (GE) profiling at the time of an AMI provides information concerning the platelet GE preceding the coronary event. In ST-segment elevation myocardial infarction (STEMI), a gene-by-gene analysis of the platelet GE identified five differentially expressed genes (DEGs): FKBP5, S100P, SAMSN1, CLEC4E and S100A12. The logistic regression model used to combine the GE in a STEMI vs healthy donors score showed an AUC of 0.95. The same five DEGs were externally validated using platelet GE data from patients with coronary atherosclerosis but without thrombosis. Early signals of an imminent AMI are likely to be found by platelet GE profiling before the infarction occurs.\"\n",
"!Series_overall_design\t\"Platelet gene expression profiling in ST-acute myocardial infarction (STEMI) patients, Healthy Donor (HD), coronary artery diseases (SCAD) patients\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: Platelets'], 1: ['diagnosis: sCAD', 'diagnosis: healthy', 'diagnosis: STEMI']}\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": "69e2619a",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e7201640",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical features preview: {'GSM2928447': [1.0], 'GSM2928448': [1.0], 'GSM2928449': [1.0], 'GSM2928450': [1.0], 'GSM2928451': [1.0], 'GSM2928452': [1.0], 'GSM2928453': [1.0], 'GSM2928454': [1.0], 'GSM2928455': [1.0], 'GSM2928456': [1.0], 'GSM2928457': [1.0], 'GSM2928458': [1.0], 'GSM2928459': [1.0], 'GSM2928460': [1.0], 'GSM2928461': [1.0], 'GSM2928462': [1.0], 'GSM2928463': [1.0], 'GSM2928464': [1.0], 'GSM2928465': [1.0], 'GSM2928466': [0.0], 'GSM2928467': [0.0], 'GSM2928468': [0.0], 'GSM2928469': [0.0], 'GSM2928470': [0.0], 'GSM2928471': [0.0], 'GSM2928472': [0.0], 'GSM2928473': [0.0], 'GSM2928474': [0.0], 'GSM2928475': [0.0], 'GSM2928476': [0.0], 'GSM2928477': [0.0], 'GSM2928478': [0.0], 'GSM2928479': [0.0], 'GSM2928480': [0.0], 'GSM2928481': [0.0], 'GSM2928482': [0.0], 'GSM2928483': [0.0], 'GSM2928484': [0.0], 'GSM2928485': [1.0], 'GSM2928486': [1.0], 'GSM2928487': [1.0], 'GSM2928488': [1.0], 'GSM2928489': [1.0], 'GSM2928490': [1.0], 'GSM2928491': [1.0], 'GSM2928492': [1.0], 'GSM2928493': [1.0], 'GSM2928494': [1.0], 'GSM2928495': [1.0], 'GSM2928496': [1.0], 'GSM2928497': [1.0], 'GSM2928498': [1.0], 'GSM2928499': [1.0], 'GSM2928500': [1.0], 'GSM2928501': [1.0], 'GSM2928502': [1.0], 'GSM2928503': [1.0]}\n",
"Clinical features saved to: ../../output/preprocess/Atherosclerosis/clinical_data/GSE109048.csv\n"
]
}
],
"source": [
"# First, let's determine if the dataset contains gene expression data\n",
"is_gene_available = True # Based on the background information, this dataset contains platelet gene expression data\n",
"\n",
"# Analyze the sample characteristics dictionary to find the trait, age, and gender information\n",
"\n",
"# For trait (Atherosclerosis)\n",
"# From the dictionary, row 1 contains 'diagnosis' which includes info about coronary artery disease\n",
"# sCAD = stable Coronary Artery Disease, which is a form of atherosclerosis\n",
"trait_row = 1 # The diagnosis information is in row 1\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert diagnosis value to binary trait value for Atherosclerosis\"\"\"\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",
" # Convert to binary (1 = has atherosclerosis, 0 = does not have atherosclerosis)\n",
" if value.lower() == 'scad': # stable Coronary Artery Disease\n",
" return 1\n",
" elif value.lower() == 'stemi': # ST-elevation myocardial infarction, which involves atherosclerosis\n",
" return 1\n",
" elif value.lower() == 'healthy':\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# For age and gender\n",
"# The sample characteristics dictionary doesn't contain explicit information about age or gender\n",
"age_row = None # Age information is not available\n",
"gender_row = None # Gender information is not available\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous\"\"\"\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",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\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().lower()\n",
" \n",
" if value in ['male', 'm', '1', 'man']:\n",
" return 1\n",
" elif value in ['female', 'f', '0', 'woman']:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# Check if trait data is available\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save 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",
"# Extract clinical features if trait_row is not None\n",
"if trait_row is not None:\n",
" # We need clinical_data for this step\n",
" # For the purpose of this task, let's assume clinical_data is available from earlier steps\n",
" try:\n",
" clinical_features = 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 clinical features\n",
" preview = preview_df(clinical_features)\n",
" print(f\"Clinical features preview: {preview}\")\n",
" \n",
" # Save the clinical features to a CSV file\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to: {out_clinical_data_file}\")\n",
" except NameError:\n",
" print(\"Cannot extract clinical features: clinical_data not found\")\n"
]
},
{
"cell_type": "markdown",
"id": "06385561",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d6ed0e2f",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Atherosclerosis/GSE109048/GSE109048_series_matrix.txt.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape: (70523, 57)\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. Get the SOFT and matrix file paths again \n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"Matrix file found: {matrix_file}\")\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" \n",
" # 3. Print the first 20 row IDs (gene or probe identifiers)\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"
]
},
{
"cell_type": "markdown",
"id": "92113893",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cb66250a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:42:11.763195Z",
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"outputs": [],
"source": [
"# Examining the gene identifiers in the first 20 rows shows that they are in the format \"XXXXXXX_st\"\n",
"# These are not standard human gene symbols, but rather probe IDs from an Affymetrix microarray\n",
"# These identifiers need to be mapped to standard human gene symbols for analysis\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "0ab3fed1",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "426049e2",
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"name": "stdout",
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"text": [
"\n",
"Gene annotation preview:\n",
"Columns in gene annotation: ['ID', 'probeset_id', 'seqname', 'strand', 'start', 'stop', 'total_probes', 'gene_assignment', 'mrna_assignment', 'swissprot', 'unigene', 'category', 'locus type', 'notes', 'SPOT_ID']\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",
"\n",
"Examining potential gene mapping columns:\n",
"\n",
"Sample values from 'gene_assignment' column:\n",
"['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 // --- // ---']\n",
"\n",
"Sample values from 'mrna_assignment' column:\n",
"['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']\n",
"\n",
"Sample values from 'swissprot' column:\n",
"['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']\n",
"\n",
"Sample values from 'unigene' column:\n",
"['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 // ---']\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
"print(\"\\nGene annotation preview:\")\n",
"print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
"print(preview_df(gene_annotation, n=5))\n",
"\n",
"# Look more closely at columns that might contain gene information\n",
"print(\"\\nExamining potential gene mapping columns:\")\n",
"potential_gene_columns = ['gene_assignment', 'mrna_assignment', 'swissprot', 'unigene']\n",
"for col in potential_gene_columns:\n",
" if col in gene_annotation.columns:\n",
" print(f\"\\nSample values from '{col}' column:\")\n",
" print(gene_annotation[col].head(3).tolist())\n"
]
},
{
"cell_type": "markdown",
"id": "e4e302e3",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dd2afb6f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:42:19.998889Z",
"iopub.status.busy": "2025-03-25T06:42:19.998759Z",
"iopub.status.idle": "2025-03-25T06:42:21.950817Z",
"shell.execute_reply": "2025-03-25T06:42:21.950350Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First few gene identifiers in gene_data:\n",
"Index(['2824546_st', '2824549_st', '2824551_st', '2824554_st', '2827992_st'], dtype='object', name='ID')\n",
"\n",
"Checking matching probe IDs in gene_data:\n",
"Sample IDs from gene_data: ['2824546_st', '2824549_st', '2824551_st', '2824554_st', '2827992_st']\n",
"\n",
"Gene mapping sample (first 5 rows):\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",
"Mapped gene expression data shape: (71528, 57)\n",
"First few gene symbols after mapping:\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",
"Gene data shape after normalization: (24018, 57)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to: ../../output/preprocess/Atherosclerosis/gene_data/GSE109048.csv\n"
]
}
],
"source": [
"# Based on the gene identifiers and gene annotation data, we need to map probe IDs to gene symbols\n",
"\n",
"# Examine the gene identifiers in gene_data (probe IDs look like XXXXXXX_st)\n",
"print(\"First few gene identifiers in gene_data:\")\n",
"print(gene_data.index[:5])\n",
"\n",
"# Looking at the gene annotation columns, we need to:\n",
"# 1. Find the column with probe IDs that match gene_data index format\n",
"# 2. Find the column with gene symbols for mapping\n",
"\n",
"# Check which column in gene_annotation contains probe IDs matching gene_data\n",
"# The \"ID\" column in gene_annotation is not in the same format as gene_data.index\n",
"# Need to see what index values actually exist in gene_data to make correct mapping\n",
"\n",
"# First, check if ID values actually exist in the gene_data index\n",
"print(\"\\nChecking matching probe IDs in gene_data:\")\n",
"sample_ids = gene_data.index[:5].tolist()\n",
"print(f\"Sample IDs from gene_data: {sample_ids}\")\n",
"\n",
"# Looking for a better match between gene_data.index and the gene_annotation columns\n",
"# From the output, we need to determine which column contains the probe IDs that match gene_data index\n",
"\n",
"# Since the data appears to be from an Affymetrix HTA 2.0 array (based on the '_st' suffix),\n",
"# we need to find the right probe ID column and gene symbol column\n",
"\n",
"# Infer that 'ID' is the appropriate probe column, and 'gene_assignment' contains gene symbols\n",
"# We'll need to use extract_human_gene_symbols to parse the gene_assignment field\n",
"\n",
"# Get the gene mapping dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')\n",
"print(\"\\nGene mapping sample (first 5 rows):\")\n",
"print(gene_mapping.head())\n",
"\n",
"# Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(\"\\nMapped gene expression data shape:\", gene_data.shape)\n",
"print(\"First few gene symbols after mapping:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Normalize gene symbols to handle synonyms\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(\"\\nGene data shape after normalization:\", gene_data.shape)\n",
"\n",
"# Save the gene expression data to file\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Gene expression data saved to: {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ec9fdbe8",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f3856448",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:42:21.952384Z",
"iopub.status.busy": "2025-03-25T06:42:21.952259Z",
"iopub.status.idle": "2025-03-25T06:42:37.258685Z",
"shell.execute_reply": "2025-03-25T06:42:37.258292Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalizing gene symbols...\n",
"Gene data shape after normalization: (24018, 57)\n",
"First 10 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": [
"Normalized gene data saved to: ../../output/preprocess/Atherosclerosis/gene_data/GSE109048.csv\n",
"\n",
"Preparing clinical data...\n",
"Clinical data preview:\n",
"{'GSM2928447': [1.0], 'GSM2928448': [1.0], 'GSM2928449': [1.0], 'GSM2928450': [1.0], 'GSM2928451': [1.0], 'GSM2928452': [1.0], 'GSM2928453': [1.0], 'GSM2928454': [1.0], 'GSM2928455': [1.0], 'GSM2928456': [1.0], 'GSM2928457': [1.0], 'GSM2928458': [1.0], 'GSM2928459': [1.0], 'GSM2928460': [1.0], 'GSM2928461': [1.0], 'GSM2928462': [1.0], 'GSM2928463': [1.0], 'GSM2928464': [1.0], 'GSM2928465': [1.0], 'GSM2928466': [0.0], 'GSM2928467': [0.0], 'GSM2928468': [0.0], 'GSM2928469': [0.0], 'GSM2928470': [0.0], 'GSM2928471': [0.0], 'GSM2928472': [0.0], 'GSM2928473': [0.0], 'GSM2928474': [0.0], 'GSM2928475': [0.0], 'GSM2928476': [0.0], 'GSM2928477': [0.0], 'GSM2928478': [0.0], 'GSM2928479': [0.0], 'GSM2928480': [0.0], 'GSM2928481': [0.0], 'GSM2928482': [0.0], 'GSM2928483': [0.0], 'GSM2928484': [0.0], 'GSM2928485': [1.0], 'GSM2928486': [1.0], 'GSM2928487': [1.0], 'GSM2928488': [1.0], 'GSM2928489': [1.0], 'GSM2928490': [1.0], 'GSM2928491': [1.0], 'GSM2928492': [1.0], 'GSM2928493': [1.0], 'GSM2928494': [1.0], 'GSM2928495': [1.0], 'GSM2928496': [1.0], 'GSM2928497': [1.0], 'GSM2928498': [1.0], 'GSM2928499': [1.0], 'GSM2928500': [1.0], 'GSM2928501': [1.0], 'GSM2928502': [1.0], 'GSM2928503': [1.0]}\n",
"Clinical data saved to: ../../output/preprocess/Atherosclerosis/clinical_data/GSE109048.csv\n",
"\n",
"Linking clinical and genetic data...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape: (57, 24019)\n",
"Linked data preview (first 5 rows, 5 columns):\n",
" Atherosclerosis A1BG A1BG-AS1 A1CF A2M\n",
"GSM2928447 1.0 6.471813 2.250198 0.743049 4.202583\n",
"GSM2928448 1.0 6.336136 2.166351 0.774551 4.305025\n",
"GSM2928449 1.0 6.359611 2.162430 0.809359 4.136589\n",
"GSM2928450 1.0 6.689616 2.110528 0.881079 4.223449\n",
"GSM2928451 1.0 6.672700 2.214330 0.805369 4.152748\n",
"\n",
"Handling missing values...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (57, 24019)\n",
"\n",
"Checking for bias in dataset features...\n",
"For the feature 'Atherosclerosis', the least common label is '0.0' with 19 occurrences. This represents 33.33% of the dataset.\n",
"The distribution of the feature 'Atherosclerosis' in this dataset is fine.\n",
"\n",
"A new JSON file was created at: ../../output/preprocess/Atherosclerosis/cohort_info.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Atherosclerosis/GSE109048.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols using NCBI database\n",
"print(\"Normalizing gene symbols...\")\n",
"gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
"print(\"First 10 normalized gene symbols:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Save the normalized gene data\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to: {out_gene_data_file}\")\n",
"\n",
"# 2. Extract and prepare clinical data from the matrix file\n",
"print(\"\\nPreparing clinical data...\")\n",
"\n",
"# Get the clinical data rows\n",
"_, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
"clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
"_, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
"\n",
"# Define convert_trait function to ensure it's available in this step\n",
"def convert_trait(value):\n",
" \"\"\"Convert diagnosis value to binary trait value for Atherosclerosis\"\"\"\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",
" # Convert to binary (1 = has atherosclerosis, 0 = does not have atherosclerosis)\n",
" if value.lower() == 'scad': # stable Coronary Artery Disease\n",
" return 1\n",
" elif value.lower() == 'stemi': # ST-elevation myocardial infarction, which involves atherosclerosis\n",
" return 1\n",
" elif value.lower() == 'healthy':\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# Process clinical data using the correct parameters from Step 2\n",
"selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=1, # Correct trait_row from Step 2\n",
" convert_trait=convert_trait,\n",
" age_row=None,\n",
" convert_age=None,\n",
" gender_row=None,\n",
" convert_gender=None\n",
")\n",
"\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(selected_clinical_df))\n",
"\n",
"# Save the clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_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(\"\\nLinking clinical and genetic data...\")\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
"if linked_data.shape[0] > 0 and linked_data.shape[1] > 5:\n",
" print(linked_data.iloc[:5, :5])\n",
"else:\n",
" print(linked_data)\n",
"\n",
"# 4. Handle missing values\n",
"print(\"\\nHandling missing values...\")\n",
"linked_data_clean = handle_missing_values(linked_data, trait)\n",
"print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n",
"\n",
"# 5. Check for bias in the dataset\n",
"print(\"\\nChecking for bias in dataset features...\")\n",
"is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n",
"\n",
"# 6. Conduct final quality validation\n",
"note = \"This GSE109048 dataset contains platelet gene expression data from ST-segment elevation myocardial infarction (STEMI) patients, stable Coronary Artery Disease (sCAD) patients, and healthy donors, relevant to atherosclerosis.\"\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_biased,\n",
" df=linked_data_clean,\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",
" linked_data_clean.to_csv(out_data_file, index=True)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset deemed not usable for associative studies. Linked data not saved.\")"
]
}
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