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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "1abb29e6",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:30:53.306713Z",
"iopub.status.busy": "2025-03-25T06:30:53.306494Z",
"iopub.status.idle": "2025-03-25T06:30:53.470255Z",
"shell.execute_reply": "2025-03-25T06:30:53.469862Z"
}
},
"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 = \"Anorexia_Nervosa\"\n",
"cohort = \"GSE60190\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Anorexia_Nervosa\"\n",
"in_cohort_dir = \"../../input/GEO/Anorexia_Nervosa/GSE60190\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Anorexia_Nervosa/GSE60190.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Anorexia_Nervosa/gene_data/GSE60190.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Anorexia_Nervosa/clinical_data/GSE60190.csv\"\n",
"json_path = \"../../output/preprocess/Anorexia_Nervosa/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "d6a8532d",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "934aaedd",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:30:53.471751Z",
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"shell.execute_reply": "2025-03-25T06:30:53.828937Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Genetic Neuropathology of Obsessive Psychiatric Syndromes\"\n",
"!Series_summary\t\"Anorexia nervosa (AN), bulimia nervosa (BN), and obsessive-compulsive disorder (OCD) are complex psychiatric disorders with shared obsessive features, thought to arise from the interaction of multiple genes of small effect with environmental factors. Potential candidate genes for AN, BN, and OCD have been identified through clinical association and neuroimaging studies; however, recent genome-wide association studies of eating disorders (ED) so far have failed to report significant findings. Additionally, few if any studies have interrogated postmortem brain tissue for evidence of eQTLs associated with candidate genes, which has particular promise as an approach to elucidating molecular mechanisms of association. We therefore selected single nucleotide polymorphisms (SNPs) based on candidate gene studies for AN, BN, and OCD from the literature, and examined the association of these SNPs with gene expression across the lifespan in prefrontal cortex of a non-psychiatric control cohort (N=268). Several risk-predisposing SNPs were significantly associated with gene expression among control subjects. We then measured gene expression in the prefrontal cortex of cases previously diagnosed with obsessive psychiatric disorders, e.g., eating disorders (ED; N=15), and obsessive-compulsive disorder/obsessive-compulsive personality disorder or tics (OCD/OCPD/Tic; N=16), and non-psychiatric controls (N=102) and identified 6 and 286 genes that were differentially expressed between ED compared to controls and OCD cases compared to controls, respectively (FDR < 5%). However, none of the clinical risk SNPs were among the eQTLs and none were significantly associated with gene expression within the broad obsessive cohort, suggesting larger sample sizes or other brain regions may be required to identify candidate molecular mechanisms of clinical association in postmortem brain datasets.\"\n",
"!Series_overall_design\t\"Gene expression data from the dorsolateral prefrontal cortex (DLPFC) from postmortem tissue on 133 subjects - 15 eating disorder (ED) patients, 16 obessive compulsive disorder (OCD) patients, and 102 non-psychiatric controls - run on the Illumina HumanHT-12 v3 microarray\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['rin: 7.4', 'rin: 8.6', 'rin: 7.8', 'rin: 8.2', 'rin: 8.5', 'rin: 8.3', 'rin: 8.1', 'rin: 8.8', 'rin: 8.7', 'rin: 7.5', 'rin: 9', 'rin: 7.1', 'rin: 7.2', 'rin: 7.7', 'rin: 8.9', 'rin: 6.7', 'rin: 6', 'rin: 8.4', 'rin: 7.3', 'rin: 8', 'rin: 9.1', 'rin: 7.9', 'rin: 9.7', 'rin: 9.2', 'rin: 6.5', 'rin: 7', 'rin: 7.6', 'rin: 6.6', 'rin: 5.4', 'rin: 5.6'], 1: ['ocd: ED', 'ocd: Control', 'ocd: OCD'], 2: ['rinmatched: 1', 'rinmatched: 0'], 3: ['dx: Bipolar', 'dx: Control', 'dx: MDD', 'dx: Tics', 'dx: OCD', 'dx: ED'], 4: ['ph: 6.18', 'ph: 6.59', 'ph: 6.37', 'ph: 6.6', 'ph: 6.38', 'ph: 6.02', 'ph: 6.87', 'ph: 6.95', 'ph: 6.82', 'ph: 6.27', 'ph: 6.53', 'ph: 6.55', 'ph: 6', 'ph: 6.13', 'ph: 6.08', 'ph: 6.29', 'ph: 6.98', 'ph: 5.91', 'ph: 6.06', 'ph: 6.9', 'ph: 6.83', 'ph: 6.36', 'ph: 6.84', 'ph: 6.74', 'ph: 6.28', 'ph: 6.49', 'ph: 6.7', 'ph: 6.63', 'ph: 6.48', 'ph: 6.62'], 5: ['age: 50.421917', 'age: 27.49863', 'age: 30.627397', 'age: 61.167123', 'age: 32.69589', 'age: 39.213698', 'age: 58.605479', 'age: 49.2', 'age: 41.041095', 'age: 51.750684', 'age: 50.89863', 'age: 26.745205', 'age: 29.104109', 'age: 39.301369', 'age: 48.978082', 'age: 57.884931', 'age: 28.364383', 'age: 24.041095', 'age: 19.268493', 'age: 27.230136', 'age: 46.605479', 'age: 23.443835', 'age: 51.038356', 'age: 39.663013', 'age: 46.109589', 'age: 77.989041', 'age: 46.967123', 'age: 63.241095', 'age: 62.306849', 'age: 83.641095'], 6: ['pmi: 27', 'pmi: 19.5', 'pmi: 71.5', 'pmi: 22.5', 'pmi: 64', 'pmi: 28', 'pmi: 18', 'pmi: 29', 'pmi: 49', 'pmi: 13', 'pmi: 26.5', 'pmi: 16.5', 'pmi: 35', 'pmi: 19', 'pmi: 20.5', 'pmi: 9.5', 'pmi: 65.5', 'pmi: 68', 'pmi: 17.5', 'pmi: 44', 'pmi: 34', 'pmi: 21.5', 'pmi: 67.5', 'pmi: 26', 'pmi: 46.5', 'pmi: 33.5', 'pmi: 24.5', 'pmi: 30.5', 'pmi: 29.5', 'pmi: 51.5'], 7: ['Sex: F', 'Sex: M'], 8: ['race: CAUC'], 9: ['batch1: 16', 'batch1: 18', 'batch1: 19', 'batch1: 20', 'batch1: 21', 'batch1: 9', 'batch1: 10', 'batch1: 12', 'batch1: 14', 'batch1: 23', 'batch1: 24', 'batch1: 25', 'batch1: 26', 'batch1: 27', 'batch1: 29', 'batch1: 33', 'batch1: 32', 'batch1: 31', 'batch1: 36', 'batch1: 37', 'batch1: 38', 'batch1: 39', 'batch1: 40', 'batch1: 41', 'batch1: 42', 'batch1: 44', 'batch1: 45', 'batch1: 48', 'batch1: 53', 'batch1: 59']}\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": "9c7086cc",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c3c94d3c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:30:53.830965Z",
"iopub.status.busy": "2025-03-25T06:30:53.830854Z",
"iopub.status.idle": "2025-03-25T06:30:53.859085Z",
"shell.execute_reply": "2025-03-25T06:30:53.858635Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of clinical features:\n",
"{'GSM1467273': [0.0, 50.421917, 0.0], 'GSM1467274': [0.0, 27.49863, 1.0], 'GSM1467275': [0.0, 30.627397, 1.0], 'GSM1467276': [0.0, 61.167123, 1.0], 'GSM1467277': [0.0, 32.69589, 1.0], 'GSM1467278': [0.0, 39.213698, 0.0], 'GSM1467279': [0.0, 58.605479, 1.0], 'GSM1467280': [0.0, 49.2, 1.0], 'GSM1467281': [0.0, 41.041095, 1.0], 'GSM1467282': [0.0, 51.750684, 1.0], 'GSM1467283': [0.0, 50.89863, 1.0], 'GSM1467284': [0.0, 26.745205, 1.0], 'GSM1467285': [0.0, 29.104109, 1.0], 'GSM1467286': [0.0, 39.301369, 0.0], 'GSM1467287': [0.0, 48.978082, 1.0], 'GSM1467288': [0.0, 57.884931, 1.0], 'GSM1467289': [0.0, 28.364383, 1.0], 'GSM1467290': [0.0, 24.041095, 1.0], 'GSM1467291': [0.0, 19.268493, 0.0], 'GSM1467292': [0.0, 27.230136, 0.0], 'GSM1467293': [0.0, 46.605479, 1.0], 'GSM1467294': [0.0, 23.443835, 0.0], 'GSM1467295': [0.0, 51.038356, 1.0], 'GSM1467296': [0.0, 39.663013, 1.0], 'GSM1467297': [0.0, 46.109589, 1.0], 'GSM1467298': [0.0, 77.989041, 1.0], 'GSM1467299': [0.0, 46.967123, 1.0], 'GSM1467300': [0.0, 63.241095, 1.0], 'GSM1467301': [0.0, 62.306849, 1.0], 'GSM1467302': [0.0, 83.641095, 0.0], 'GSM1467303': [0.0, 42.838356, 1.0], 'GSM1467304': [0.0, 51.386301, 1.0], 'GSM1467305': [0.0, 66.715068, 0.0], 'GSM1467306': [0.0, 51.939726, 0.0], 'GSM1467307': [0.0, 34.339726, 1.0], 'GSM1467308': [0.0, 50.109589, 1.0], 'GSM1467309': [0.0, 18.758904, 0.0], 'GSM1467310': [0.0, 16.649315, 0.0], 'GSM1467311': [0.0, 16.353424, 1.0], 'GSM1467312': [0.0, 42.065753, 1.0], 'GSM1467313': [0.0, 16.726027, 0.0], 'GSM1467314': [0.0, 34.465753, 1.0], 'GSM1467315': [0.0, 34.254794, 1.0], 'GSM1467316': [0.0, 47.484931, 1.0], 'GSM1467317': [0.0, 43.756164, 1.0], 'GSM1467318': [0.0, 49.210958, 1.0], 'GSM1467319': [0.0, 57.482191, 0.0], 'GSM1467320': [0.0, 46.561643, 1.0], 'GSM1467321': [0.0, 49.561643, 1.0], 'GSM1467322': [0.0, 28.589041, 1.0], 'GSM1467323': [0.0, 38.410958, 0.0], 'GSM1467324': [0.0, 30.032876, 1.0], 'GSM1467325': [0.0, 56.09041, 1.0], 'GSM1467326': [0.0, 46.915068, 1.0], 'GSM1467327': [0.0, 49.021917, 0.0], 'GSM1467328': [0.0, 71.109589, 0.0], 'GSM1467329': [0.0, 17.235616, 0.0], 'GSM1467330': [0.0, 16.583561, 1.0], 'GSM1467331': [0.0, 16.934246, 1.0], 'GSM1467332': [0.0, 16.8, 1.0], 'GSM1467333': [0.0, 18.117808, 1.0], 'GSM1467334': [0.0, 18.660273, 1.0], 'GSM1467335': [0.0, 16.69589, 0.0], 'GSM1467336': [0.0, 75.572602, 0.0], 'GSM1467337': [0.0, 59.260273, 0.0], 'GSM1467338': [0.0, 55.545205, 1.0], 'GSM1467339': [0.0, 41.778082, 1.0], 'GSM1467340': [0.0, 57.454794, 1.0], 'GSM1467341': [0.0, 45.284931, 1.0], 'GSM1467342': [0.0, 56.304109, 0.0], 'GSM1467343': [0.0, 39.654794, 0.0], 'GSM1467344': [0.0, 55.945205, 1.0], 'GSM1467345': [0.0, 38.232876, 1.0], 'GSM1467346': [0.0, 58.109589, 1.0], 'GSM1467347': [0.0, 40.021917, 1.0], 'GSM1467348': [0.0, 50.504109, 1.0], 'GSM1467349': [0.0, 36.550684, 1.0], 'GSM1467350': [0.0, 45.117808, 1.0], 'GSM1467351': [0.0, 83.545205, 1.0], 'GSM1467352': [0.0, 18.786301, 1.0], 'GSM1467353': [0.0, 48.567123, 0.0], 'GSM1467354': [0.0, 38.331506, 0.0], 'GSM1467355': [0.0, 48.101369, 1.0], 'GSM1467356': [0.0, 18.39452, 1.0], 'GSM1467357': [0.0, 60.843835, 1.0], 'GSM1467358': [0.0, 61.372602, 1.0], 'GSM1467359': [0.0, 52.038356, 1.0], 'GSM1467360': [0.0, 59.254794, 1.0], 'GSM1467361': [1.0, 41.567123, 0.0], 'GSM1467362': [0.0, 50.358904, 1.0], 'GSM1467363': [0.0, 31.558904, 1.0], 'GSM1467364': [0.0, 45.701369, 0.0], 'GSM1467365': [0.0, 44.731506, 1.0], 'GSM1467366': [0.0, 34.39726, 0.0], 'GSM1467367': [1.0, 31.613698, 0.0], 'GSM1467368': [0.0, 54.846575, 1.0], 'GSM1467369': [0.0, 84.057534, 0.0], 'GSM1467370': [0.0, 66.79452, 0.0], 'GSM1467371': [0.0, 53.323287, 1.0], 'GSM1467372': [0.0, 30.043835, 0.0], 'GSM1467373': [0.0, 55.435616, 1.0], 'GSM1467374': [0.0, 45.676712, 1.0], 'GSM1467375': [0.0, 54.334246, 1.0], 'GSM1467376': [0.0, 63.558904, 1.0], 'GSM1467377': [0.0, 45.224657, 0.0], 'GSM1467378': [0.0, 23.69589, 1.0], 'GSM1467379': [0.0, 67.865753, 1.0], 'GSM1467380': [0.0, 16.753424, 1.0], 'GSM1467381': [0.0, 18.424657, 1.0], 'GSM1467382': [0.0, 17.09041, 0.0], 'GSM1467383': [0.0, 16.183561, 1.0], 'GSM1467384': [0.0, 33.260273, 1.0], 'GSM1467385': [0.0, 54.424657, 1.0], 'GSM1467386': [0.0, 45.378082, 1.0], 'GSM1467387': [0.0, 52.523287, 1.0], 'GSM1467388': [0.0, 35.273972, 1.0], 'GSM1467389': [0.0, 22.630136, 1.0], 'GSM1467390': [0.0, 20.863013, 1.0], 'GSM1467391': [0.0, 26.531506, 0.0], 'GSM1467392': [0.0, 24.627397, 1.0], 'GSM1467393': [0.0, 53.978082, 1.0], 'GSM1467394': [0.0, 34.961643, 1.0], 'GSM1467395': [0.0, 18.731506, 1.0], 'GSM1467396': [1.0, 30.726027, 0.0], 'GSM1467397': [0.0, 63.471232, 1.0], 'GSM1467398': [0.0, 54.808219, 1.0], 'GSM1467399': [0.0, 57.512328, 1.0], 'GSM1467400': [0.0, 57.610958, 1.0], 'GSM1467401': [0.0, 44.958904, 1.0], 'GSM1467402': [0.0, 35.684931, 0.0], 'GSM1467403': [0.0, 63.0, 1.0], 'GSM1467404': [0.0, 38.780821, 1.0], 'GSM1467405': [0.0, 45.978082, 1.0]}\n",
"Clinical features saved to ../../output/preprocess/Anorexia_Nervosa/clinical_data/GSE60190.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains gene expression data from DLPFC tissue\n",
"# run on the Illumina HumanHT-12 v3 microarray, which is gene expression data.\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Trait Data (Anorexia Nervosa)\n",
"# Looking at the sample characteristics, we can see that row 3 contains diagnoses information\n",
"# and row 1 contains OCD status which includes \"ED\" (eating disorder)\n",
"# Since our trait is Anorexia_Nervosa, and the dataset mentions ED (eating disorders)\n",
"# we'll use row 3 (dx field) which has more specific diagnostic categories\n",
"trait_row = 3\n",
"\n",
"# 2.2 Age Data\n",
"# Row 5 contains age information\n",
"age_row = 5\n",
"\n",
"# 2.3 Gender Data\n",
"# Row 7 contains Sex information\n",
"gender_row = 7\n",
"\n",
"# 3. Define conversion functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value to binary format (0=Control, 1=Anorexia_Nervosa)\"\"\"\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",
" # Check if the value indicates Anorexia Nervosa\n",
" # From the sample characteristics, 'ED' refers to eating disorder patients\n",
" if value == 'ED':\n",
" return 1\n",
" elif value == 'Control':\n",
" return 0\n",
" else:\n",
" # Other diagnoses are not our target trait\n",
" return 0\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous format\"\"\"\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 format (0=Female, 1=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()\n",
" \n",
" if value == 'F':\n",
" return 0\n",
" elif value == 'M':\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Initial filtering on the usability of the dataset\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",
" # Extract clinical features\n",
" clinical_features_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the clinical features dataframe\n",
" preview = preview_df(clinical_features_df)\n",
" print(\"Preview of clinical features:\")\n",
" print(preview)\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save clinical features as CSV\n",
" clinical_features_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "fa934a62",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1f830a02",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:30:54.492840Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Anorexia_Nervosa/GSE60190/GSE60190_series_matrix.txt.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape: (48679, 133)\n",
"First 20 gene/probe identifiers:\n",
"Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n",
" 'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n",
" 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n",
" 'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n",
" 'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\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": "4c7ef891",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2ef8ad48",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:30:54.494837Z",
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"shell.execute_reply": "2025-03-25T06:30:54.496692Z"
}
},
"outputs": [],
"source": [
"# Based on the gene identifiers observed in the gene expression data, \n",
"# these are Illumina probe IDs (starting with ILMN_) rather than standard human gene symbols.\n",
"# Illumina probe IDs need to be mapped to gene symbols for proper analysis.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "13ae29a8",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "602100c6",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:30:54.498341Z",
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"shell.execute_reply": "2025-03-25T06:31:11.923469Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Platform title found: Illumina HumanHT-12 V3.0 expression beadchip\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"{'ID': ['ILMN_1725881', 'ILMN_1910180', 'ILMN_1804174', 'ILMN_1796063', 'ILMN_1811966', 'ILMN_1668162', 'ILMN_1715600', 'ILMN_1912287', 'ILMN_1793729', 'ILMN_1889125'], 'nuID': ['rp13_p1x6D80lNLk3c', 'NEX0oqCV8.er4HVfU4', 'KyqQynMZxJcruyylEU', 'xXl7eXuF7sbPEp.KFI', '9ckqJrioiaej9_ajeQ', 'rtCnUep15THUpc_0e4', 'QtVBXBWhekTEIT0kjo', 'EuUnlPkeXRP9fyO.iQ', '0flyIEROp.olYSF6n4', 'fegCQD_j_69DUU38dI'], 'Species': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Source': ['RefSeq', 'Unigene', 'RefSeq', 'RefSeq', 'RefSeq', 'RefSeq', 'RefSeq', 'Unigene', 'RefSeq', 'Unigene'], 'Search_Key': ['ILMN_44919', 'ILMN_127219', 'ILMN_139282', 'ILMN_5006', 'ILMN_38756', 'ILMN_7652', 'ILMN_35097', 'ILMN_77451', 'ILMN_18382', 'ILMN_108888'], 'Transcript': ['ILMN_44919', 'ILMN_127219', 'ILMN_139282', 'ILMN_5006', 'ILMN_38756', 'ILMN_7652', 'ILMN_35097', 'ILMN_77451', 'ILMN_18382', 'ILMN_108888'], 'ILMN_Gene': ['LOC23117', 'HS.575038', 'FCGR2B', 'TRIM44', 'LOC653895', 'DGAT2L3', 'LOC387701', 'HS.133181', 'C15ORF39', 'HS.545755'], 'Source_Reference_ID': ['XM_933824.1', 'Hs.575038', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1', 'NM_001013579.1', 'XM_373469.3', 'Hs.133181', 'NM_015492.4', 'Hs.545755'], 'RefSeq_ID': ['XM_933824.1', nan, 'XM_938851.1', 'NM_017583.3', 'XM_936379.1', 'NM_001013579.1', 'XM_373469.3', nan, 'NM_015492.4', nan], 'Unigene_ID': [nan, 'Hs.575038', nan, nan, nan, nan, nan, 'Hs.133181', nan, 'Hs.545755'], 'Entrez_Gene_ID': [23117.0, nan, 2213.0, 54765.0, 653895.0, 158833.0, 387701.0, nan, 56905.0, nan], 'GI': [89040007.0, 10437021.0, 88952550.0, 29029528.0, 89033487.0, 61888901.0, 89031576.0, 27826545.0, 153251858.0, 1999235.0], 'Accession': ['XM_933824.1', 'AK024680', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1', 'NM_001013579.1', 'XM_373469.3', 'BX093329', 'NM_015492.4', 'AA346998'], 'Symbol': ['LOC23117', nan, 'FCGR2B', 'TRIM44', 'LOC653895', 'DGAT2L3', 'LOC387701', nan, 'C15orf39', nan], 'Protein_Product': ['XP_938917.1', nan, 'XP_943944.1', 'NP_060053.2', 'XP_941472.1', 'NP_001013597.1', 'XP_373469.1', nan, 'NP_056307.2', nan], 'Array_Address_Id': [1710221.0, 5900364.0, 2480717.0, 1300239.0, 4480719.0, 6020725.0, 3870215.0, 2710020.0, 870110.0, 290020.0], 'Probe_Type': ['I', 'S', 'I', 'S', 'S', 'S', 'A', 'S', 'S', 'S'], 'Probe_Start': [122.0, 1409.0, 1643.0, 2901.0, 25.0, 782.0, 301.0, 324.0, 3585.0, 139.0], 'SEQUENCE': ['GGCTCCTCTTTGGGCTCCTACTGGAATTTATCAGCCATCAGTGCATCTCT', 'ACACCTTCAGGAGGGAAGCCCTTATTTCTGGGTTGAACTCCCCTTCCATG', 'TAGGGGCAATAGGCTATACGCTACAGCCTAGGTGTGTAGTAGGCCACACC', 'CCTGCCTGTCTGCCTGTGACCTGTGTACGTATTACAGGCTTTAGGACCAG', 'CTAGCAGGGAGCGGTGAGGGAGAGCGGCTGGATTTCTTGCGGGATCTGCA', 'GTCAAGGCTCCACTGGGCTCCTGCCATACTCCAGGCCTATTGTCACTGTG', 'GTCCCCAACCCTAACCCGGACCTGGCACATACAAGACATTCAGCAGATGG', 'GTGCCAGCTGCCATTGCACTGCCTCACATTTTCCTTTAGATGTTTGAGCA', 'CTTGCCTAGAGAACACACATGGGCTTTGGAGCCCGACAGACCTGGGCTTG', 'CTGGAAAAGCAAAATTTGGATTTGTGGTTCAATCCACCATCTTTACTCAG'], 'Chromosome': ['16', nan, nan, '11', nan, 'X', '10', nan, '15', nan], 'Probe_Chr_Orientation': ['-', nan, nan, '+', nan, '+', '+', nan, '+', nan], 'Probe_Coordinates': ['21766363-21766363:21769901-21769949', nan, nan, '35786070-35786119', nan, '63280932-63280981', '92811754-92811767:92811768-92811803', nan, '73290721-73290770', nan], 'Cytoband': ['16p12.2a', nan, '1q23.3b', '11p13a', '10q11.23b', 'Xq13.1b', nan, nan, '15q24.2a', nan], 'Definition': ['PREDICTED: Homo sapiens KIAA0220-like protein, transcript variant 11 (LOC23117), mRNA.', 'Homo sapiens cDNA: FLJ21027 fis, clone CAE07110', 'PREDICTED: Homo sapiens Fc fragment of IgG, low affinity IIb, receptor (CD32) (FCGR2B), mRNA.', 'Homo sapiens tripartite motif-containing 44 (TRIM44), mRNA.', 'PREDICTED: Homo sapiens similar to protein geranylgeranyltransferase type I, beta subunit (LOC653895), mRNA.', 'Homo sapiens diacylglycerol O-acyltransferase 2-like 3 (DGAT2L3), mRNA.', 'PREDICTED: Homo sapiens hypothetical LOC387701 (LOC387701), mRNA.', 'BX093329 Soares_parathyroid_tumor_NbHPA Homo sapiens cDNA clone IMAGp998A124183 ; IMAGE:1648403, mRNA sequence', 'Homo sapiens chromosome 15 open reading frame 39 (C15orf39), mRNA.', 'EST53225 Fetal heart II Homo sapiens cDNA 3 end, mRNA sequence'], 'Ontology_Component': [nan, nan, nan, 'intracellular [goid 5622] [evidence IEA]', nan, 'membrane [goid 16020] [evidence IEA]; integral to membrane [goid 16021] [evidence IEA]; endoplasmic reticulum [goid 5783] [evidence IEA]', nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan, 'lipid biosynthesis [goid 8610] [evidence IEA]; lipid metabolism [goid 6629] [evidence IEA]', nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, 'zinc ion binding [goid 8270] [evidence IEA]; metal ion binding [goid 46872] [evidence IEA]', nan, 'acyltransferase activity [goid 8415] [evidence IEA]; transferase activity [goid 16740] [evidence IEA]', nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, 'MGC3490; MC7; HSA249128; DIPB', nan, 'AWAT1; DGA2', nan, nan, 'DKFZP434H132; FLJ46337; MGC117209', nan], 'Obsolete_Probe_Id': [nan, nan, nan, 'MGC3490; MC7; HSA249128; DIPB', nan, 'AWAT1; DGA2', nan, nan, 'DKFZP434H132; FLJ46337; MGC117209', nan], 'GB_ACC': ['XM_933824.1', 'AK024680', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1', 'NM_001013579.1', 'XM_373469.3', 'BX093329', 'NM_015492.4', 'AA346998']}\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": "65852f68",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7763dede",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:31:14.080964Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping dataframe shape: (36157, 2)\n",
"First 10 rows of gene mapping:\n",
" ID Gene\n",
"0 ILMN_1725881 LOC23117\n",
"2 ILMN_1804174 FCGR2B\n",
"3 ILMN_1796063 TRIM44\n",
"4 ILMN_1811966 LOC653895\n",
"5 ILMN_1668162 DGAT2L3\n",
"6 ILMN_1715600 LOC387701\n",
"8 ILMN_1793729 C15orf39\n",
"10 ILMN_2296644 PCDHGA9\n",
"11 ILMN_1711283 PCDHGA9\n",
"12 ILMN_1682799 STAMBPL1\n",
"Gene expression data shape after mapping: (19091, 133)\n",
"First 10 gene symbols after mapping:\n",
"Index(['A1BG', 'A1CF', 'A26A1', 'A26B1', 'A26C1B', 'A26C3', 'A2BP1', 'A2M',\n",
" 'A2ML1', 'A3GALT2'],\n",
" dtype='object', name='Gene')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data shape after normalizing symbols: (18298, 133)\n",
"First 10 normalized gene symbols:\n",
"Index(['A1BG', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1',\n",
" 'AAAS', 'AACS'],\n",
" dtype='object', name='Gene')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Anorexia_Nervosa/gene_data/GSE60190.csv\n"
]
}
],
"source": [
"# 1. Observe the gene identifiers and determine the appropriate columns\n",
"# From the preview, we can see the gene expression data uses 'ILMN_XXXXX' identifiers (Illumina probe IDs)\n",
"# In the annotation dataframe, the 'ID' column contains these same Illumina probe IDs\n",
"# The 'Symbol' column contains the gene symbols we need to map to\n",
"\n",
"# 2. Get a gene mapping dataframe by extracting the relevant columns\n",
"# Use the get_gene_mapping function to create a mapping dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
"print(\"First 10 rows of gene mapping:\")\n",
"print(gene_mapping.head(10))\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"# This handles probes mapping to multiple genes and genes with multiple probes\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
"print(\"First 10 gene symbols after mapping:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Normalize gene symbols to ensure consistency across datasets\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene expression data shape after normalizing symbols: {gene_data.shape}\")\n",
"print(\"First 10 normalized gene symbols:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Save the gene expression data\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": "85e8e14e",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6310ce66",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:31:19.170355Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data already normalized and saved to ../../output/preprocess/Anorexia_Nervosa/gene_data/GSE60190.csv\n",
"Loaded clinical data shape: (3, 133)\n",
" GSM1467273 GSM1467274 GSM1467275 GSM1467276 GSM1467277 \\\n",
"Anorexia_Nervosa 0.000000 0.00000 0.000000 0.000000 0.00000 \n",
"Age 50.421917 27.49863 30.627397 61.167123 32.69589 \n",
"Gender 0.000000 1.00000 1.000000 1.000000 1.00000 \n",
"\n",
" GSM1467278 GSM1467279 GSM1467280 GSM1467281 GSM1467282 \\\n",
"Anorexia_Nervosa 0.000000 0.000000 0.0 0.000000 0.000000 \n",
"Age 39.213698 58.605479 49.2 41.041095 51.750684 \n",
"Gender 0.000000 1.000000 1.0 1.000000 1.000000 \n",
"\n",
" ... GSM1467396 GSM1467397 GSM1467398 GSM1467399 \\\n",
"Anorexia_Nervosa ... 1.000000 0.000000 0.000000 0.000000 \n",
"Age ... 30.726027 63.471232 54.808219 57.512328 \n",
"Gender ... 0.000000 1.000000 1.000000 1.000000 \n",
"\n",
" GSM1467400 GSM1467401 GSM1467402 GSM1467403 GSM1467404 \\\n",
"Anorexia_Nervosa 0.000000 0.000000 0.000000 0.0 0.000000 \n",
"Age 57.610958 44.958904 35.684931 63.0 38.780821 \n",
"Gender 1.000000 1.000000 0.000000 1.0 1.000000 \n",
"\n",
" GSM1467405 \n",
"Anorexia_Nervosa 0.000000 \n",
"Age 45.978082 \n",
"Gender 1.000000 \n",
"\n",
"[3 rows x 133 columns]\n",
"Linked data shape: (133, 18301)\n",
"Linked data preview (first 5 rows, 5 columns):\n",
" Anorexia_Nervosa Age Gender A1BG A1CF\n",
"GSM1467273 0.0 50.421917 0.0 14.962093 22.217464\n",
"GSM1467274 0.0 27.498630 1.0 14.918877 22.188462\n",
"GSM1467275 0.0 30.627397 1.0 14.925598 22.199076\n",
"GSM1467276 0.0 61.167123 1.0 14.907169 22.233160\n",
"GSM1467277 0.0 32.695890 1.0 14.868744 22.283053\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (133, 18301)\n",
"For the feature 'Anorexia_Nervosa', the least common label is '1.0' with 3 occurrences. This represents 2.26% of the dataset.\n",
"The distribution of the feature 'Anorexia_Nervosa' in this dataset is severely biased.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 30.043835\n",
" 50% (Median): 45.224657\n",
" 75%: 54.424657\n",
"Min: 16.183561\n",
"Max: 84.057534\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '0.0' with 36 occurrences. This represents 27.07% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"A new JSON file was created at: ../../output/preprocess/Anorexia_Nervosa/cohort_info.json\n",
"Dataset is not usable for analysis. No linked data file saved.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data - we already did this in step 6\n",
"# No need to do it again\n",
"print(f\"Gene data already normalized and saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Load clinical data that was already saved in Step 2\n",
"clinical_data_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
"print(f\"Loaded clinical data shape: {clinical_data_df.shape}\")\n",
"print(clinical_data_df.head())\n",
"\n",
"# 2. Link clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_data_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 dorsolateral prefrontal cortex of postmortem tissue with Eating Disorders.\"\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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