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{
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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 = \"Vitamin_D_Levels\"\n",
"cohort = \"GSE35925\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Vitamin_D_Levels\"\n",
"in_cohort_dir = \"../../input/GEO/Vitamin_D_Levels/GSE35925\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Vitamin_D_Levels/GSE35925.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Vitamin_D_Levels/gene_data/GSE35925.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Vitamin_D_Levels/clinical_data/GSE35925.csv\"\n",
"json_path = \"../../output/preprocess/Vitamin_D_Levels/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "4c2f9ca8",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"id": "494de71a",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Files in the cohort directory:\n",
"['GSE35925_family.soft.gz', 'GSE35925_series_matrix.txt.gz']\n",
"Identified SOFT files: ['GSE35925_family.soft.gz']\n",
"Identified matrix files: ['GSE35925_series_matrix.txt.gz']\n",
"\n",
"Background Information:\n",
"!Series_title\t\"Calcitriol supplementation effects on Ki67 expression and transcriptional profile of breast cancer specimens from post-menopausal patients\"\n",
"!Series_summary\t\"Background: Breast cancer patients present lower 1,25(OH)2D3 or 25(OH)D3 serum levels than unaffected women. Although 1,25(OH)2D3 pharmacological concentrations of 1,25(OH)2D3 may exert antiproliferative effects in breast cancer cell lines, much uncertainty remains about the effects of calcitriol supplementation in tumor specimens in vivo. We have evaluated tumor dimension (ultrassonography), proliferative index (Ki67 expression), 25(OH)D3 serum concentration and gene expression profile, before and after a short term calcitriol supplementation (dose to prevent osteoporosis) to post-menopausal patients. Results: Thirty three patients with operable disease had tumor samples evaluated. Most of them (87.5%) presented 25(OH)D3 insufficiency (<30 ng/mL). Median period of calcitriol supplementation was 30 days. Although tumor dimension did not vary, Ki67 immunoexpression decreased after supplementation. Transcriptional analysis of 15 matched pre/post-supplementation samples using U133 Plus 2.0 GeneChip (Affymetrix) revealed 18 genes over-expressed in post-supplementation tumors. As a technical validation procedure, expression of four genes was also determined by RT-qPCR and a direct correlation was observed between both methods (microarray vs PCR). To further explore the effects of near physiological concentrations of calcitriol on breast cancer samples, an ex vivo model of fresh tumor slices was utilized. Tumor samples from another 12 post-menopausal patients were sliced and treated in vitro with slightly high concentrations of calcitriol (0.5nM), that can be attained in vivo, for 24 hours In this model, expression of PBEF1, EGR1, ATF3, FOS and RGS1 was not induced after a short exposure to calcitriol. Conclusions: In our work, most post-menopausal breast cancer patients presented at least 25(OH)D3 insufficiency. In these patients, a short period of calcitriol supplementation may prevent tumor growth and reduce Ki67 expression, probably associated with discrete transcriptional changes. This observation deserves further investigation to better clarify calcitriol effects in tumor behavior under physiological conditions.\"\n",
"!Series_overall_design\t\"Post-menopausal patients with early stage breast cancer, in the absence of distant metastasis, were invited to take part in the study. This protocol was approved by the Institutional Ethics Committee, and a written informed consent was signed by all participants. Patients had blood and tumor samples collected during biopsy, and were prescribed calcitriol supplementation, (Rocaltrol)TM 0.50 ug/day PO, as recommended for osteoporosis prevention. Tumor specimens obtained during biopsy (pre-supplementation) or breast surgery (post-supplementation) were hand dissected and samples with at least 70% tumor cells were further processed. Breast surgery followed in about one month\"\n",
"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['gender: female'], 1: ['age: 54', 'age: 62', 'age: 63', 'age: 49', 'age: 66', 'age: 56', 'age: 52', 'age: 51', 'age: 64'], 2: ['histologic type: metaplastic', 'histologic type: CDI', 'histologic type: CLI', 'histologic type: CDI/CLI', 'histologic type: CDICLI'], 3: ['tissue type: breast cancer']}\n"
]
}
],
"source": [
"# 1. Let's first list the directory contents to understand what files are available\n",
"import os\n",
"\n",
"print(\"Files in the cohort directory:\")\n",
"files = os.listdir(in_cohort_dir)\n",
"print(files)\n",
"\n",
"# Adapt file identification to handle different naming patterns\n",
"soft_files = [f for f in files if 'soft' in f.lower() or '.soft' in f.lower() or '_soft' in f.lower()]\n",
"matrix_files = [f for f in files if 'matrix' in f.lower() or '.matrix' in f.lower() or '_matrix' in f.lower()]\n",
"\n",
"# If no files with these patterns are found, look for alternative file types\n",
"if not soft_files:\n",
" soft_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
"if not matrix_files:\n",
" matrix_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
"\n",
"print(\"Identified SOFT files:\", soft_files)\n",
"print(\"Identified matrix files:\", matrix_files)\n",
"\n",
"# Use the first files found, if any\n",
"if len(soft_files) > 0 and len(matrix_files) > 0:\n",
" soft_file = os.path.join(in_cohort_dir, soft_files[0])\n",
" matrix_file = os.path.join(in_cohort_dir, matrix_files[0])\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(\"\\nBackground Information:\")\n",
" print(background_info)\n",
" print(\"\\nSample Characteristics Dictionary:\")\n",
" print(sample_characteristics_dict)\n",
"else:\n",
" print(\"No appropriate files found in the directory.\")\n"
]
},
{
"cell_type": "markdown",
"id": "d4b1fe18",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
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{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the series title and summary, this is a study of breast cancer specimens with gene\n",
"# expression profiling using microarrays (U133 Plus 2.0 GeneChip), not just miRNA or methylation\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# For trait (Vitamin D Levels):\n",
"# From the summary, we know this study examined 25(OH)D3 serum concentration,\n",
"# but the data is not explicitly available in the sample characteristics dictionary\n",
"trait_row = None # Vitamin D level data is not available in sample characteristics\n",
"\n",
"# For age:\n",
"# Age is available in row 1 of the sample characteristics\n",
"age_row = 1\n",
"\n",
"# For gender:\n",
"# Gender is available in row 0 of the sample characteristics, and all patients are female\n",
"gender_row = 0\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" # Since trait data isn't available, this function won't be used\n",
" # But defining it for completeness\n",
" if not value or ':' not in value:\n",
" return None\n",
" val = value.split(':', 1)[1].strip()\n",
" try:\n",
" return float(val)\n",
" except:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" # Convert age to continuous value\n",
" if not value or ':' not in value:\n",
" return None\n",
" val = value.split(':', 1)[1].strip()\n",
" try:\n",
" return int(val)\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # Convert gender to binary (0 for female, 1 for male)\n",
" if not value or ':' not in value:\n",
" return None\n",
" val = value.split(':', 1)[1].strip().lower()\n",
" if 'female' in val:\n",
" return 0\n",
" elif 'male' in val:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Trait data is not available in the sample characteristics\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"# Since trait_row is None, we should skip this substep\n",
"# No need to call geo_select_clinical_features or save clinical data\n"
]
},
{
"cell_type": "markdown",
"id": "32a2750d",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
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{
"name": "stdout",
"output_type": "stream",
"text": [
"First 20 gene/probe identifiers:\n",
"Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n",
" '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n",
" '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n",
" '1552263_at', '1552264_a_at', '1552266_at'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene expression data shape: (54675, 30)\n"
]
}
],
"source": [
"# Use the helper function to get the proper file paths\n",
"soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Extract gene expression data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file_path)\n",
" \n",
" # Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
" \n",
" # Print shape to understand the dataset dimensions\n",
" print(f\"\\nGene expression data shape: {gene_data.shape}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "9add6928",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
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"outputs": [],
"source": [
"# Reviewing the gene identifiers shown in previous step\n",
"# These identifiers (e.g., '1007_s_at', '1053_at') are Affymetrix probe IDs from a microarray\n",
"# They are not human gene symbols and need to be mapped to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "339bb8bd",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sample of gene expression data (first 5 rows, first 5 columns):\n",
" GSM877494 GSM877495 GSM877496 GSM877497 GSM877498\n",
"ID \n",
"1007_s_at 11.333510 10.134981 11.504773 11.124785 11.144094\n",
"1053_at 8.367081 6.781699 7.152553 6.712648 6.979207\n",
"117_at 7.119038 6.212178 7.274306 6.750108 7.428198\n",
"121_at 8.201648 7.997442 8.637606 8.335036 8.053557\n",
"1255_g_at 3.864814 3.267283 3.474927 3.288460 2.955061\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Platform information:\n",
"!Series_title = Calcitriol supplementation effects on Ki67 expression and transcriptional profile of breast cancer specimens from post-menopausal patients\n",
"!Platform_title = [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array\n",
"!Platform_description = Affymetrix submissions are typically submitted to GEO using the GEOarchive method described at http://www.ncbi.nlm.nih.gov/projects/geo/info/geo_affy.html\n",
"!Platform_description =\n",
"!Platform_description = June 03, 2009: annotation table updated with netaffx build 28\n",
"!Platform_description = June 06, 2012: annotation table updated with netaffx build 32\n",
"!Platform_description = June 23, 2016: annotation table updated with netaffx build 35\n",
"#Target Description =\n",
"#RefSeq Transcript ID = References to multiple sequences in RefSeq. The field contains the ID and Description for each entry, and there can be multiple entries per ProbeSet.\n",
"#Gene Ontology Biological Process = Gene Ontology Consortium Biological Process derived from LocusLink. Each annotation consists of three parts: \"Accession Number // Description // Evidence\". The description corresponds directly to the GO ID. The evidence can be \"direct\", or \"extended\".\n",
"#Gene Ontology Cellular Component = Gene Ontology Consortium Cellular Component derived from LocusLink. Each annotation consists of three parts: \"Accession Number // Description // Evidence\". The description corresponds directly to the GO ID. The evidence can be \"direct\", or \"extended\".\n",
"#Gene Ontology Molecular Function = Gene Ontology Consortium Molecular Function derived from LocusLink. Each annotation consists of three parts: \"Accession Number // Description // Evidence\". The description corresponds directly to the GO ID. The evidence can be \"direct\", or \"extended\".\n",
"ID\tGB_ACC\tSPOT_ID\tSpecies Scientific Name\tAnnotation Date\tSequence Type\tSequence Source\tTarget Description\tRepresentative Public ID\tGene Title\tGene Symbol\tENTREZ_GENE_ID\tRefSeq Transcript ID\tGene Ontology Biological Process\tGene Ontology Cellular Component\tGene Ontology Molecular Function\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation columns:\n",
"['ID', 'GB_ACC', 'SPOT_ID', 'Species Scientific Name', 'Annotation Date', 'Sequence Type', 'Sequence Source', 'Target Description', 'Representative Public ID', 'Gene Title', 'Gene Symbol', 'ENTREZ_GENE_ID', 'RefSeq Transcript ID', 'Gene Ontology Biological Process', 'Gene Ontology Cellular Component', 'Gene Ontology Molecular Function']\n",
"\n",
"Gene annotation preview:\n",
"{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\n",
"\n",
"Matching rows in annotation for sample IDs: 310\n",
"\n",
"Potential gene symbol columns: ['Species Scientific Name', 'Gene Title', 'Gene Symbol', 'ENTREZ_GENE_ID', 'Gene Ontology Biological Process', 'Gene Ontology Cellular Component', 'Gene Ontology Molecular Function']\n",
"\n",
"Is this dataset likely to contain gene expression data? True\n"
]
}
],
"source": [
"# 1. This part examines the data more thoroughly to determine what type of data it contains\n",
"try:\n",
" # First, let's check a few rows of the gene_data we extracted in Step 3\n",
" print(\"Sample of gene expression data (first 5 rows, first 5 columns):\")\n",
" print(gene_data.iloc[:5, :5])\n",
" \n",
" # Analyze the SOFT file to identify the data type and mapping information\n",
" platform_info = []\n",
" with gzip.open(soft_file_path, 'rt', encoding='latin-1') as f:\n",
" for line in f:\n",
" if line.startswith(\"!Platform_title\") or line.startswith(\"!Series_title\") or \"description\" in line.lower():\n",
" platform_info.append(line.strip())\n",
" \n",
" print(\"\\nPlatform information:\")\n",
" for line in platform_info:\n",
" print(line)\n",
" \n",
" # Extract the gene annotation using the library function\n",
" gene_annotation = get_gene_annotation(soft_file_path)\n",
" \n",
" # Display column names of the annotation dataframe\n",
" print(\"\\nGene annotation columns:\")\n",
" print(gene_annotation.columns.tolist())\n",
" \n",
" # Preview the annotation dataframe\n",
" print(\"\\nGene annotation preview:\")\n",
" annotation_preview = preview_df(gene_annotation)\n",
" print(annotation_preview)\n",
" \n",
" # Check if ID column exists in the gene_annotation dataframe\n",
" if 'ID' in gene_annotation.columns:\n",
" # Check if any of the IDs in gene_annotation match those in gene_data\n",
" sample_ids = list(gene_data.index[:10])\n",
" matching_rows = gene_annotation[gene_annotation['ID'].isin(sample_ids)]\n",
" print(f\"\\nMatching rows in annotation for sample IDs: {len(matching_rows)}\")\n",
" \n",
" # Look for gene symbol column\n",
" gene_symbol_candidates = [col for col in gene_annotation.columns if 'gene' in col.lower() or 'symbol' in col.lower() or 'name' in col.lower()]\n",
" print(f\"\\nPotential gene symbol columns: {gene_symbol_candidates}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error analyzing gene annotation data: {e}\")\n",
" gene_annotation = pd.DataFrame()\n",
"\n",
"# Based on our analysis, determine if this is really gene expression data\n",
"# Check the platform description and match with the data we've extracted\n",
"is_gene_expression = False\n",
"for info in platform_info:\n",
" if 'expression' in info.lower() or 'transcript' in info.lower() or 'mrna' in info.lower():\n",
" is_gene_expression = True\n",
" break\n",
"\n",
"print(f\"\\nIs this dataset likely to contain gene expression data? {is_gene_expression}\")\n",
"\n",
"# If this isn't gene expression data, we need to update our metadata\n",
"if not is_gene_expression:\n",
" print(\"\\nNOTE: Based on our analysis, this dataset doesn't appear to contain gene expression data.\")\n",
" print(\"It appears to be a different type of data (possibly SNP array or other genomic data).\")\n",
" # Update is_gene_available for metadata\n",
" is_gene_available = False\n",
" \n",
" # Save the updated 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"
]
},
{
"cell_type": "markdown",
"id": "17519416",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping dataframe:\n",
"Shape: (45782, 2)\n",
" ID Gene\n",
"0 1007_s_at DDR1 /// MIR4640\n",
"1 1053_at RFC2\n",
"2 117_at HSPA6\n",
"3 121_at PAX8\n",
"4 1255_g_at GUCA1A\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression dataframe after mapping:\n",
"Shape: (21278, 30)\n",
" GSM877494 GSM877495 GSM877496 GSM877497 GSM877498 GSM877499 \\\n",
"Gene \n",
"A1BG 5.616316 6.773878 5.610153 6.248181 5.881286 6.651108 \n",
"A1BG-AS1 5.794817 5.586119 6.158518 5.386011 6.405554 6.576005 \n",
"A1CF 7.951007 8.912474 8.437105 8.502681 7.911599 7.672422 \n",
"A2M 15.064945 15.318153 15.979660 16.447088 16.520041 16.582738 \n",
"A2M-AS1 5.150456 5.886847 5.095151 5.191165 7.678134 7.326865 \n",
"\n",
" GSM877500 GSM877501 GSM877502 GSM877503 ... GSM877514 \\\n",
"Gene ... \n",
"A1BG 5.631434 5.051273 5.880273 6.100511 ... 5.424866 \n",
"A1BG-AS1 6.704271 6.987863 5.783158 6.023409 ... 5.434135 \n",
"A1CF 8.708905 7.364438 8.764073 8.270111 ... 8.128462 \n",
"A2M 16.101992 15.756496 17.071811 17.016488 ... 14.467333 \n",
"A2M-AS1 5.533050 6.608568 5.800557 6.052964 ... 5.428202 \n",
"\n",
" GSM877515 GSM877516 GSM877517 GSM877518 GSM877519 GSM877520 \\\n",
"Gene \n",
"A1BG 5.712718 6.557843 6.955593 6.449011 6.272387 6.110234 \n",
"A1BG-AS1 5.268080 5.745164 5.744610 5.424327 5.881286 5.776714 \n",
"A1CF 8.147963 8.522083 8.552667 8.105057 8.478136 8.309216 \n",
"A2M 14.083032 16.451196 16.545189 15.388393 16.665775 15.511591 \n",
"A2M-AS1 5.352689 6.302366 6.565838 5.885645 5.747984 5.236020 \n",
"\n",
" GSM877521 GSM877522 GSM877523 \n",
"Gene \n",
"A1BG 5.999768 5.885590 5.607684 \n",
"A1BG-AS1 6.024566 5.967344 5.774737 \n",
"A1CF 8.330425 8.306175 7.747662 \n",
"A2M 16.037330 15.681003 15.944138 \n",
"A2M-AS1 6.043255 5.346307 5.383081 \n",
"\n",
"[5 rows x 30 columns]\n",
"\n",
"Number of genes after mapping: 21278\n"
]
}
],
"source": [
"# 1. Identify the correct columns for mapping\n",
"# From the previous inspection, we can see that:\n",
"# - Gene identifiers in gene_data are probe IDs like '1007_s_at', which match the 'ID' column in gene_annotation\n",
"# - Gene symbols are stored in the 'Gene Symbol' column of gene_annotation\n",
"\n",
"# 2. Extract gene mapping dataframe with the probe ID and gene symbol columns\n",
"prob_col = 'ID'\n",
"gene_col = 'Gene Symbol'\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"\n",
"print(\"Gene mapping dataframe:\")\n",
"print(f\"Shape: {mapping_df.shape}\")\n",
"print(mapping_df.head())\n",
"\n",
"# 3. Convert probe-level measurements to gene-level expression data\n",
"# This will divide probe values across multiple genes and sum all contributions for each gene\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"print(\"\\nGene expression dataframe after mapping:\")\n",
"print(f\"Shape: {gene_data.shape}\")\n",
"print(gene_data.head())\n",
"\n",
"# Check how many genes we have after mapping\n",
"print(f\"\\nNumber of genes after mapping: {len(gene_data)}\")\n"
]
},
{
"cell_type": "markdown",
"id": "f1997610",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape after normalization: (19845, 30)\n",
"First few gene symbols after normalization: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Vitamin_D_Levels/gene_data/GSE35925.csv\n",
"Data quality check result: Not usable\n",
"Cannot save linked data due to missing trait information.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the obtained gene expression data\n",
"try:\n",
" # Now let's normalize the gene data using the provided function\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 few gene symbols after normalization: {list(normalized_gene_data.index[:10])}\")\n",
" \n",
" # Save the normalized gene data\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" normalized_gene_data.to_csv(out_gene_data_file)\n",
" print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"except Exception as e:\n",
" print(f\"Error in gene normalization: {e}\")\n",
" # If normalization fails, use the original gene data\n",
" normalized_gene_data = gene_data\n",
" print(\"Using original gene data without normalization\")\n",
"\n",
"# Since trait_row is None (as determined in Step 2), we know that trait data is not available\n",
"# We should record this in the metadata without final validation\n",
"\n",
"# Create a placeholder dataframe with samples as rows for metadata needs\n",
"sample_df = pd.DataFrame(\n",
" {trait: [None] * len(normalized_gene_data.columns)}, \n",
" index=normalized_gene_data.columns\n",
")\n",
"\n",
"# Save metadata recording that gene data is available but trait data is not\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=False, # Set to False since we can't do final validation without trait data\n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=False\n",
")\n",
"\n",
"# Since trait data is not available, we can't create usable linked data\n",
"print(f\"Data quality check result: {'Usable' if is_usable else 'Not usable'}\")\n",
"print(\"Cannot save linked data due to missing trait information.\")"
]
}
],
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|