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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "4b434cfc",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:37:51.995139Z",
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"shell.execute_reply": "2025-03-25T08:37:52.157332Z"
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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 = \"Depression\"\n",
"cohort = \"GSE149980\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Depression\"\n",
"in_cohort_dir = \"../../input/GEO/Depression/GSE149980\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Depression/GSE149980.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Depression/gene_data/GSE149980.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Depression/clinical_data/GSE149980.csv\"\n",
"json_path = \"../../output/preprocess/Depression/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "bbcda955",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bcfc6570",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:37:52.159011Z",
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Gene expression of Lymphoblastoid Cell Lines–LCLs from Depressed Patients after in-vitro treatment with citalopram–CTP\"\n",
"!Series_summary\t\"We used whole gene gene expression profiling to identify potential gene expression biomarkers associated for the treatment individualization of unipolar depression.\"\n",
"!Series_overall_design\t\"Gene expression was measured after 24 and 48 hours of in-vitro treatment with 3 µM CTP in n=17 LCLs derived from depressed patients with documented clinical treatment outcome to SSRIs.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['response status: responder', 'response status: non-responder'], 1: ['tissue: Lymphoblastoid Cell Lines (LCLs)']}\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": "cb1f51b5",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "eb7e8699",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:37:53.112366Z",
"iopub.status.busy": "2025-03-25T08:37:53.112260Z",
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"shell.execute_reply": "2025-03-25T08:37:53.120517Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of extracted clinical data:\n",
"{'GSM4519184': [1.0], 'GSM4519185': [1.0], 'GSM4519186': [1.0], 'GSM4519187': [1.0], 'GSM4519188': [1.0], 'GSM4519189': [1.0], 'GSM4519190': [1.0], 'GSM4519191': [1.0], 'GSM4519192': [1.0], 'GSM4519193': [1.0], 'GSM4519194': [1.0], 'GSM4519195': [1.0], 'GSM4519196': [1.0], 'GSM4519197': [1.0], 'GSM4519198': [1.0], 'GSM4519199': [1.0], 'GSM4519200': [1.0], 'GSM4519201': [1.0], 'GSM4519202': [0.0], 'GSM4519203': [0.0], 'GSM4519204': [0.0], 'GSM4519205': [0.0], 'GSM4519206': [0.0], 'GSM4519207': [0.0], 'GSM4519208': [0.0], 'GSM4519209': [0.0], 'GSM4519210': [0.0], 'GSM4519211': [0.0], 'GSM4519212': [0.0], 'GSM4519213': [0.0], 'GSM4519214': [0.0], 'GSM4519215': [0.0], 'GSM4519216': [0.0], 'GSM4519217': [0.0], 'GSM4519218': [1.0], 'GSM4519219': [1.0], 'GSM4519220': [1.0], 'GSM4519221': [1.0], 'GSM4519222': [1.0], 'GSM4519223': [1.0], 'GSM4519224': [1.0], 'GSM4519225': [1.0], 'GSM4519226': [1.0], 'GSM4519227': [1.0], 'GSM4519228': [1.0], 'GSM4519229': [1.0], 'GSM4519230': [1.0], 'GSM4519231': [1.0], 'GSM4519232': [1.0], 'GSM4519233': [1.0], 'GSM4519234': [1.0], 'GSM4519235': [1.0], 'GSM4519236': [0.0], 'GSM4519237': [0.0], 'GSM4519238': [0.0], 'GSM4519239': [0.0], 'GSM4519240': [0.0], 'GSM4519241': [0.0], 'GSM4519242': [0.0], 'GSM4519243': [0.0], 'GSM4519244': [0.0], 'GSM4519245': [0.0], 'GSM4519246': [0.0], 'GSM4519247': [0.0], 'GSM4519248': [0.0], 'GSM4519249': [0.0], 'GSM4519250': [0.0], 'GSM4519251': [0.0]}\n"
]
}
],
"source": [
"import pandas as pd\n",
"import os\n",
"from typing import Optional, Dict, Any, Callable\n",
"import json\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains gene expression data of Lymphoblastoid Cell Lines\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# Looking at the Sample Characteristics Dictionary:\n",
"# Key 0 contains 'response status' which can be used as our trait (depression treatment response)\n",
"# There's no age or gender information\n",
"trait_row = 0\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"Convert depression treatment response to binary value.\"\"\"\n",
" if isinstance(value, str):\n",
" # Extract the part after the colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to binary\n",
" if \"responder\" in value.lower() and \"non\" not in value.lower():\n",
" return 1 # Responder\n",
" elif \"non-responder\" in value.lower():\n",
" return 0 # Non-responder\n",
" \n",
" return None # For any other or unknown values\n",
"\n",
"def convert_age(value: str) -> Optional[float]:\n",
" \"\"\"Convert age to float. Not used in this dataset.\"\"\"\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> Optional[int]:\n",
" \"\"\"Convert gender to binary. Not used in this dataset.\"\"\"\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Conduct initial filtering on dataset usability\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" # We should use the clinical_data that was loaded in a previous step\n",
" # Since the actual clinical_data should be available in the environment\n",
" # We'll use geo_select_clinical_features with the existing clinical_data\n",
" \n",
" try:\n",
" # Extract clinical features using the provided function\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data, # Assuming clinical_data is already loaded\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 extracted data\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of extracted clinical data:\")\n",
" print(preview)\n",
" \n",
" # Save to CSV\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" except NameError:\n",
" print(\"Warning: clinical_data not found. The clinical data extraction step cannot be completed.\")\n",
" print(\"Please ensure the clinical_data DataFrame is available from a previous step.\")\n"
]
},
{
"cell_type": "markdown",
"id": "4c444503",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c7ffc2a4",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:37:53.121914Z",
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"shell.execute_reply": "2025-03-25T08:37:53.489317Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Depression/GSE149980/GSE149980_series_matrix.txt.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape: (50739, 68)\n",
"First 20 gene/probe identifiers:\n",
"Index(['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107',\n",
" '(+)E1A_r60_a135', '(+)E1A_r60_a20', '(+)E1A_r60_a22', '(+)E1A_r60_a97',\n",
" '(+)E1A_r60_n11', '(+)E1A_r60_n9', '3xSLv1', 'A_19_P00315452',\n",
" 'A_19_P00315459', 'A_19_P00315482', 'A_19_P00315492', 'A_19_P00315493',\n",
" 'A_19_P00315502', 'A_19_P00315506', 'A_19_P00315518', 'A_19_P00315519'],\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": "cc2f2dcf",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "35651d57",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:37:53.490749Z",
"iopub.status.busy": "2025-03-25T08:37:53.490630Z",
"iopub.status.idle": "2025-03-25T08:37:53.492478Z",
"shell.execute_reply": "2025-03-25T08:37:53.492205Z"
}
},
"outputs": [],
"source": [
"# Analyze the gene identifiers\n",
"# These identifiers (like '(+)E1A_r60_1', 'A_19_P00315452') are not standard human gene symbols\n",
"# They appear to be probe IDs from a microarray platform that need to be mapped to gene symbols\n",
"\n",
"# Human gene symbols would typically be like BRCA1, TP53, IL6, etc.\n",
"# The identifiers we see are platform-specific probe IDs that need mapping\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "d23cfbbc",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "64856b18",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:37:53.493365Z",
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"shell.execute_reply": "2025-03-25T08:38:01.970158Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Platform title found: Agilent-039494 SurePrint G3 Human GE v2 8x60K Microarray 039381 (Probe Name version)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"{'ID': ['GE_BrightCorner', 'DarkCorner', 'A_23_P117082', 'A_33_P3246448', 'A_33_P3318220', 'A_33_P3236322', 'A_33_P3319925', 'A_21_P0000509', 'A_21_P0000744', 'A_24_P215804'], 'SPOT_ID': ['CONTROL', 'CONTROL', 'A_23_P117082', 'A_33_P3246448', 'A_33_P3318220', 'A_33_P3236322', 'A_33_P3319925', 'A_21_P0000509', 'A_21_P0000744', 'A_24_P215804'], 'CONTROL_TYPE': ['pos', 'pos', 'FALSE', 'FALSE', 'FALSE', 'FALSE', 'FALSE', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, 'NM_015987', 'NM_080671', 'NM_178466', nan, 'XM_001133269', 'NR_024244', 'NR_038269', 'NM_016951'], 'GB_ACC': [nan, nan, 'NM_015987', 'NM_080671', 'NM_178466', 'AK128005', 'XM_001133269', 'NR_024244', 'NR_038269', 'NM_016951'], 'LOCUSLINK_ID': [nan, nan, 50865.0, 23704.0, 128861.0, 100129869.0, 730249.0, nan, nan, 51192.0], 'GENE_SYMBOL': [nan, nan, 'HEBP1', 'KCNE4', 'BPIFA3', 'LOC100129869', 'IRG1', 'SNAR-G2', 'LOC100506844', 'CKLF'], 'GENE_NAME': [nan, nan, 'heme binding protein 1', 'potassium voltage-gated channel, Isk-related family, member 4', 'BPI fold containing family A, member 3', 'uncharacterized LOC100129869', 'immunoresponsive 1 homolog (mouse)', 'small ILF3/NF90-associated RNA G2', 'uncharacterized LOC100506844', 'chemokine-like factor'], 'UNIGENE_ID': [nan, nan, 'Hs.642618', 'Hs.348522', 'Hs.360989', nan, 'Hs.160789', 'Hs.717308', 'Hs.90286', 'Hs.15159'], 'ENSEMBL_ID': [nan, nan, 'ENST00000014930', 'ENST00000281830', 'ENST00000375454', nan, 'ENST00000449753', nan, 'ENST00000551421', nan], 'ACCESSION_STRING': [nan, nan, 'ref|NM_015987|ens|ENST00000014930|gb|AF117615|gb|BC016277', 'ref|NM_080671|ens|ENST00000281830|tc|THC2655788', 'ref|NM_178466|ens|ENST00000375454|ens|ENST00000471233|tc|THC2478474', 'gb|AK128005|tc|THC2484382', 'ens|ENST00000449753|ens|ENST00000377462|ref|XM_001133269|ref|XM_003403661', 'ref|NR_024244', 'ref|NR_038269|ens|ENST00000551421|ens|ENST00000546580|ens|ENST00000553102', 'ref|NM_016951|ref|NM_181641|ref|NM_181640|ref|NM_016326'], 'CHROMOSOMAL_LOCATION': [nan, nan, 'chr12:13127906-13127847', 'chr2:223920197-223920256', 'chr20:31812208-31812267', 'chr20:56533874-56533815', 'chr13:77532009-77532068', 'chr19:49534993-49534934', 'chr12:58329728-58329669', 'chr16:66599900-66599959'], 'CYTOBAND': [nan, nan, 'hs|12p13.1', 'hs|2q36.1', 'hs|20q11.21', 'hs|20q13.32', 'hs|13q22.3', 'hs|19q13.33', 'hs|12q14.1', 'hs|16q21'], 'DESCRIPTION': [nan, nan, 'Homo sapiens heme binding protein 1 (HEBP1), mRNA [NM_015987]', 'Homo sapiens potassium voltage-gated channel, Isk-related family, member 4 (KCNE4), mRNA [NM_080671]', 'Homo sapiens BPI fold containing family A, member 3 (BPIFA3), transcript variant 1, mRNA [NM_178466]', 'Homo sapiens cDNA FLJ46124 fis, clone TESTI2040372. [AK128005]', 'immunoresponsive 1 homolog (mouse) [Source:HGNC Symbol;Acc:33904] [ENST00000449753]', 'Homo sapiens small ILF3/NF90-associated RNA G2 (SNAR-G2), small nuclear RNA [NR_024244]', 'Homo sapiens uncharacterized LOC100506844 (LOC100506844), non-coding RNA [NR_038269]', 'Homo sapiens chemokine-like factor (CKLF), transcript variant 1, mRNA [NM_016951]'], 'GO_ID': [nan, nan, 'GO:0005488(binding)|GO:0005576(extracellular region)|GO:0005737(cytoplasm)|GO:0005739(mitochondrion)|GO:0005829(cytosol)|GO:0007623(circadian rhythm)|GO:0020037(heme binding)', 'GO:0005244(voltage-gated ion channel activity)|GO:0005249(voltage-gated potassium channel activity)|GO:0006811(ion transport)|GO:0006813(potassium ion transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0016324(apical plasma membrane)', 'GO:0005576(extracellular region)|GO:0008289(lipid binding)', nan, 'GO:0019543(propionate catabolic process)|GO:0032496(response to lipopolysaccharide)|GO:0047547(2-methylcitrate dehydratase activity)', nan, nan, 'GO:0005576(extracellular region)|GO:0005615(extracellular space)|GO:0006935(chemotaxis)|GO:0008009(chemokine activity)|GO:0008283(cell proliferation)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0030593(neutrophil chemotaxis)|GO:0032940(secretion by cell)|GO:0048246(macrophage chemotaxis)|GO:0048247(lymphocyte chemotaxis)'], 'SEQUENCE': [nan, nan, 'AAGGGGGAAAATGTGATTTGTGCCTGATCTTTCATCTGTGATTCTTATAAGAGCTTTGTC', 'GCAAGTCTCTCTGCACCTATTAAAAAGTGATGTATATACTTCCTTCTTATTCTGTTGAGT', 'CATTCCATAAGGAGTGGTTCTCGGCAAATATCTCACTTGAATTTGACCTTGAATTGAGAC', 'ATTTATTTTCACAAGTGCATAGCGGCCAACACCACCAGCACTAACCAGAGTGGATTCTTG', 'AGAAGACCTAGAAGACTGTTCTGTGTTAACTACACTTCTCAAAGGACCCTCTCCACCAGA', 'AGGGGAGGGTTCGAGGGTACGAGTTCGAGGCCAACCGGGTCCACATTGGTTGAGAAAAAA', 'AGTCGTACCCTCTTGTTTTTCTCTGAGTCAGTCTTAAGGTGAAATGAAGTGTGGCCCAGT', 'AAAGAAGTTTTGTAATTTTATATTACTTTTTAGTTTGATACTAAGTATTAAACATATTTC']}\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# Check if there are any platforms defined in the SOFT file that might contain annotation data\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" soft_content = f.read()\n",
"\n",
"# Look for platform sections in the SOFT file\n",
"platform_sections = re.findall(r'^!Platform_title\\s*=\\s*(.+)$', soft_content, re.MULTILINE)\n",
"if platform_sections:\n",
" print(f\"Platform title found: {platform_sections[0]}\")\n",
"\n",
"# Try to extract more annotation data by reading directly from the SOFT file\n",
"# Look for lines that might contain gene symbol mappings\n",
"symbol_pattern = re.compile(r'ID_REF\\s+Symbol|ID\\s+Gene Symbol', re.IGNORECASE)\n",
"annotation_lines = []\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" for line in f:\n",
" if symbol_pattern.search(line):\n",
" annotation_lines.append(line)\n",
" # Collect the next few lines to see the annotation structure\n",
" for _ in range(10):\n",
" annotation_lines.append(next(f, ''))\n",
"\n",
"if annotation_lines:\n",
" print(\"Found potential gene symbol mappings:\")\n",
" for line in annotation_lines:\n",
" print(line.strip())\n",
"\n",
"# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"\\nGene annotation preview:\")\n",
"print(preview_df(gene_annotation, n=10))\n",
"\n",
"# If we need an alternative source of mapping, check if there are any other annotation files in the cohort directory\n",
"cohort_files = os.listdir(in_cohort_dir)\n",
"annotation_files = [f for f in cohort_files if 'annotation' in f.lower() or 'platform' in f.lower()]\n",
"if annotation_files:\n",
" print(\"\\nAdditional annotation files found in the cohort directory:\")\n",
" for file in annotation_files:\n",
" print(file)\n"
]
},
{
"cell_type": "markdown",
"id": "ebfdd05c",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
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"id": "5ae708c1",
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"execution": {
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"shell.execute_reply": "2025-03-25T08:38:03.101665Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping dataframe shape: (46204, 2)\n",
"First few rows of the gene mapping dataframe:\n",
" ID Gene\n",
"2 A_23_P117082 HEBP1\n",
"3 A_33_P3246448 KCNE4\n",
"4 A_33_P3318220 BPIFA3\n",
"5 A_33_P3236322 LOC100129869\n",
"6 A_33_P3319925 IRG1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transformed gene expression data shape: (20353, 68)\n",
"First few gene symbols:\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2LD1', 'A2M', 'A2ML1', 'A2MP1', 'A4GALT',\n",
" 'A4GNT', 'AA06'],\n",
" dtype='object', name='Gene')\n",
"After normalizing gene symbols, shape: (19847, 68)\n",
"Preview of normalized gene expression data (first 5 genes, first 5 samples):\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" GSM4519184 GSM4519185 GSM4519186 GSM4519187 GSM4519188\n",
"Gene \n",
"A1BG 0.857412 0.183019 -1.851454 -1.841433 0.033260\n",
"A1BG-AS1 0.193574 0.635310 -0.273058 0.066432 0.151078\n",
"A1CF 0.951021 -0.351138 -0.663651 -0.789045 0.338096\n",
"A2M -0.620814 -0.329174 -0.151582 -0.199618 -1.207462\n",
"A2ML1 0.823854 -0.336316 -0.476297 -0.466115 0.673593\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Depression/gene_data/GSE149980.csv\n"
]
}
],
"source": [
"# 1. Identify the columns in gene_annotation that map to gene identifiers and gene symbols\n",
"# Based on the preview of gene_annotation, 'ID' appears to be the gene identifier column, matching the identifiers seen in gene_data\n",
"# 'GENE_SYMBOL' contains the gene symbols we want to map to\n",
"\n",
"# 2. Create the gene mapping dataframe using the get_gene_mapping function\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')\n",
"print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
"print(\"First few rows of the gene mapping dataframe:\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Convert probe-level measurements to gene-level expression data\n",
"# Use the apply_gene_mapping function to perform the conversion\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(f\"Transformed gene expression data shape: {gene_data.shape}\")\n",
"print(\"First few gene symbols:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# 4. Further normalize gene symbols to handle synonyms\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"After normalizing gene symbols, shape: {gene_data.shape}\")\n",
"print(\"Preview of normalized gene expression data (first 5 genes, first 5 samples):\")\n",
"print(gene_data.iloc[:5, :5])\n",
"\n",
"# 5. Save the gene expression data to the output 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": "1d8d39b1",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
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"id": "49440fa8",
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"shell.execute_reply": "2025-03-25T08:38:13.162799Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected clinical data shape: (1, 68)\n",
"Clinical data preview:\n",
"{'GSM4519184': [1.0], 'GSM4519185': [1.0], 'GSM4519186': [1.0], 'GSM4519187': [1.0], 'GSM4519188': [1.0], 'GSM4519189': [1.0], 'GSM4519190': [1.0], 'GSM4519191': [1.0], 'GSM4519192': [1.0], 'GSM4519193': [1.0], 'GSM4519194': [1.0], 'GSM4519195': [1.0], 'GSM4519196': [1.0], 'GSM4519197': [1.0], 'GSM4519198': [1.0], 'GSM4519199': [1.0], 'GSM4519200': [1.0], 'GSM4519201': [1.0], 'GSM4519202': [0.0], 'GSM4519203': [0.0], 'GSM4519204': [0.0], 'GSM4519205': [0.0], 'GSM4519206': [0.0], 'GSM4519207': [0.0], 'GSM4519208': [0.0], 'GSM4519209': [0.0], 'GSM4519210': [0.0], 'GSM4519211': [0.0], 'GSM4519212': [0.0], 'GSM4519213': [0.0], 'GSM4519214': [0.0], 'GSM4519215': [0.0], 'GSM4519216': [0.0], 'GSM4519217': [0.0], 'GSM4519218': [1.0], 'GSM4519219': [1.0], 'GSM4519220': [1.0], 'GSM4519221': [1.0], 'GSM4519222': [1.0], 'GSM4519223': [1.0], 'GSM4519224': [1.0], 'GSM4519225': [1.0], 'GSM4519226': [1.0], 'GSM4519227': [1.0], 'GSM4519228': [1.0], 'GSM4519229': [1.0], 'GSM4519230': [1.0], 'GSM4519231': [1.0], 'GSM4519232': [1.0], 'GSM4519233': [1.0], 'GSM4519234': [1.0], 'GSM4519235': [1.0], 'GSM4519236': [0.0], 'GSM4519237': [0.0], 'GSM4519238': [0.0], 'GSM4519239': [0.0], 'GSM4519240': [0.0], 'GSM4519241': [0.0], 'GSM4519242': [0.0], 'GSM4519243': [0.0], 'GSM4519244': [0.0], 'GSM4519245': [0.0], 'GSM4519246': [0.0], 'GSM4519247': [0.0], 'GSM4519248': [0.0], 'GSM4519249': [0.0], 'GSM4519250': [0.0], 'GSM4519251': [0.0]}\n",
"Clinical data saved to ../../output/preprocess/Depression/clinical_data/GSE149980.csv\n",
"Linked data shape: (68, 19848)\n",
"Linked data preview (first 5 rows, 5 columns):\n",
" Depression A1BG A1BG-AS1 A1CF A2M\n",
"GSM4519184 1.0 0.857412 0.193574 0.951021 -0.620814\n",
"GSM4519185 1.0 0.183019 0.635310 -0.351138 -0.329174\n",
"GSM4519186 1.0 -1.851454 -0.273058 -0.663651 -0.151582\n",
"GSM4519187 1.0 -1.841433 0.066432 -0.789045 -0.199618\n",
"GSM4519188 1.0 0.033260 0.151078 0.338096 -1.207462\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (68, 19848)\n",
"For the feature 'Depression', the least common label is '0.0' with 32 occurrences. This represents 47.06% of the dataset.\n",
"The distribution of the feature 'Depression' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Depression/GSE149980.csv\n"
]
}
],
"source": [
"# 1. We'll normalize gene symbols in the gene expression data\n",
"# Note: We've already done this in step 6, so we can skip this part\n",
"\n",
"# 2. Link the clinical and genetic data\n",
"# First, let's make sure we have the correct clinical data from step 2\n",
"# Review the clinical data attributes from step 2\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"Convert depression treatment response to binary value.\"\"\"\n",
" if isinstance(value, str):\n",
" # Extract the part after the colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to binary\n",
" if \"responder\" in value.lower() and \"non\" not in value.lower():\n",
" return 1 # Responder\n",
" elif \"non-responder\" in value.lower():\n",
" return 0 # Non-responder\n",
" \n",
" return None # For any other or unknown values\n",
"\n",
"# Get clinical data using the correct row index identified in step 2\n",
"selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=0, # Using row 0 for response status as identified in step 2\n",
" convert_trait=convert_trait,\n",
" age_row=None, # No age data available\n",
" convert_age=None,\n",
" gender_row=None, # No gender data available\n",
" convert_gender=None\n",
")\n",
"\n",
"print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(selected_clinical_df))\n",
"\n",
"# Save clinical data for future reference\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",
"# Link 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",
"print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Linked data is empty\")\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. Check for bias in features\n",
"is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Validate and save cohort information\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,\n",
" note=\"Dataset contains gene expression data from Lymphoblastoid Cell Lines of depressed patients with SSRI treatment outcomes (responders/non-responders).\"\n",
")\n",
"\n",
"# 6. Save the 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 is not usable for analysis. No linked data file saved.\")"
]
}
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