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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "5d925014",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:31:53.915251Z",
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"shell.execute_reply": "2025-03-25T08:31:54.083335Z"
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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 = \"Crohns_Disease\"\n",
"cohort = \"GSE123086\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Crohns_Disease\"\n",
"in_cohort_dir = \"../../input/GEO/Crohns_Disease/GSE123086\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Crohns_Disease/GSE123086.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Crohns_Disease/gene_data/GSE123086.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Crohns_Disease/clinical_data/GSE123086.csv\"\n",
"json_path = \"../../output/preprocess/Crohns_Disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "b178ae1b",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "da850c1f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:31:54.085116Z",
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"shell.execute_reply": "2025-03-25T08:31:54.312963Z"
}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases [study of 13 diseases]\"\n",
"!Series_summary\t\"We conducted prospective clinical studies to validate the importance of CD4+ T cells in 13 diseases from the following ICD-10-CM chapters: Neoplasms (breast cancer, chronic lymphocytic leukemia); endocrine, nutritional and metabolic diseases (type I diabetes, obesity); diseases of the circulatory system (atherosclerosis); diseases of the respiratory system (acute tonsillitis, influenza, seasonal allergic rhinitis, asthma); diseases of the digestive system (Crohn’s disease [CD], ulcerative colitis [UC]); and diseases of the skin and subcutaneous tissue (atopic eczema, psoriatic diseases).\"\n",
"!Series_summary\t\"Study participants were recruited by clinical specialists based on diagnostic criteria defined by organizations representing each specialist’s discipline. Age and gender matched healthy controls (n = 127 and 39, respectively) were recruited in the Southeast region of Sweden from outpatient clinics at the University Hospital, Linköping; Ryhov County Hospital, Jönköping, a primary health care center in Jönköping; and a medical specialist unit for children in Värnamo. Study participants represented both urban and rural populations with an age range of 8–94 years. Patients with type I diabetes and obesity had an age range of 8–18 years. 12 patients had more than one diagnosis.\"\n",
"!Series_overall_design\t\"Total RNA was extracted using the AllPrep DNA/RNA Micro kit (Qiagen, Hilden, Germany; cat. no. 80284) according to the manufacturer’s instructions. RNA concentration and integrity were evaluated using the Agilent RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, California, USA; cat. no. 5067-1511) on an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, California, USA). Microarrays were then further computationally processed as described in One-Color Microarray-Based Gene Expression Analysis Low Input Quick Amp Labeling protocol (Agilent Technologies, Santa Clara, California, USA).\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell type: CD4+ T cells'], 1: ['primary diagnosis: ASTHMA', 'primary diagnosis: ATHEROSCLEROSIS', 'primary diagnosis: BREAST_CANCER', 'primary diagnosis: CHRONIC_LYMPHOCYTIC_LEUKEMIA', 'primary diagnosis: CROHN_DISEASE', 'primary diagnosis: ATOPIC_ECZEMA', 'primary diagnosis: HEALTHY_CONTROL', 'primary diagnosis: INFLUENZA', 'primary diagnosis: OBESITY', 'primary diagnosis: PSORIASIS', 'primary diagnosis: SEASONAL_ALLERGIC_RHINITIS', 'primary diagnosis: TYPE_1_DIABETES', 'primary diagnosis: ACUTE_TONSILLITIS', 'primary diagnosis: ULCERATIVE_COLITIS'], 2: ['Sex: Male', 'diagnosis2: ATOPIC_ECZEMA', 'Sex: Female', 'diagnosis2: ATHEROSCLEROSIS', 'diagnosis2: ASTHMA_OBESITY', 'diagnosis2: ASTHMA', 'diagnosis2: ASTMHA_SEASONAL_ALLERGIC_RHINITIS', 'diagnosis2: OBESITY'], 3: ['age: 56', 'Sex: Male', 'age: 20', 'age: 51', 'age: 37', 'age: 61', 'age: 31', 'age: 41', 'age: 80', 'age: 53', 'age: 73', 'age: 60', 'age: 76', 'age: 77', 'age: 74', 'age: 69', 'age: 81', 'age: 70', 'age: 82', 'age: 67', 'age: 78', 'age: 72', 'age: 66', 'age: 36', 'age: 45', 'age: 65', 'age: 48', 'age: 50', 'age: 24', 'age: 42'], 4: [nan, 'age: 63', 'age: 74', 'age: 49', 'age: 60', 'age: 68', 'age: 38', 'age: 16', 'age: 12', 'age: 27']}\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": "e5154371",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e7bb312b",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:31:54.314843Z",
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"shell.execute_reply": "2025-03-25T08:31:54.337511Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{'GSM3494884': [nan, 56.0, 1.0], 'GSM3494885': [nan, nan, nan], 'GSM3494886': [nan, 20.0, 0.0], 'GSM3494887': [nan, 51.0, 0.0], 'GSM3494888': [nan, 37.0, 1.0], 'GSM3494889': [nan, 61.0, 1.0], 'GSM3494890': [nan, nan, nan], 'GSM3494891': [nan, 31.0, 1.0], 'GSM3494892': [nan, 56.0, 0.0], 'GSM3494893': [nan, 41.0, 0.0], 'GSM3494894': [nan, 61.0, 0.0], 'GSM3494895': [nan, nan, nan], 'GSM3494896': [nan, 80.0, 1.0], 'GSM3494897': [nan, 53.0, 1.0], 'GSM3494898': [nan, 61.0, 1.0], 'GSM3494899': [nan, 73.0, 1.0], 'GSM3494900': [nan, 60.0, 1.0], 'GSM3494901': [nan, 76.0, 1.0], 'GSM3494902': [nan, 77.0, 0.0], 'GSM3494903': [nan, 74.0, 0.0], 'GSM3494904': [nan, 69.0, 1.0], 'GSM3494905': [nan, 77.0, 0.0], 'GSM3494906': [nan, 81.0, 0.0], 'GSM3494907': [nan, 70.0, 0.0], 'GSM3494908': [nan, 82.0, 0.0], 'GSM3494909': [nan, 69.0, 0.0], 'GSM3494910': [nan, 82.0, 0.0], 'GSM3494911': [nan, 67.0, 0.0], 'GSM3494912': [nan, 67.0, 0.0], 'GSM3494913': [nan, 78.0, 0.0], 'GSM3494914': [nan, 67.0, 0.0], 'GSM3494915': [nan, 74.0, 1.0], 'GSM3494916': [nan, nan, nan], 'GSM3494917': [nan, 51.0, 1.0], 'GSM3494918': [nan, 72.0, 1.0], 'GSM3494919': [nan, 66.0, 1.0], 'GSM3494920': [nan, 80.0, 0.0], 'GSM3494921': [1.0, 36.0, 1.0], 'GSM3494922': [1.0, 67.0, 0.0], 'GSM3494923': [1.0, 31.0, 0.0], 'GSM3494924': [1.0, 31.0, 0.0], 'GSM3494925': [1.0, 45.0, 0.0], 'GSM3494926': [1.0, 56.0, 0.0], 'GSM3494927': [1.0, 65.0, 0.0], 'GSM3494928': [1.0, 53.0, 0.0], 'GSM3494929': [1.0, 48.0, 0.0], 'GSM3494930': [1.0, 50.0, 0.0], 'GSM3494931': [1.0, 76.0, 1.0], 'GSM3494932': [nan, nan, nan], 'GSM3494933': [nan, 24.0, 0.0], 'GSM3494934': [nan, 42.0, 0.0], 'GSM3494935': [nan, 76.0, 1.0], 'GSM3494936': [nan, 22.0, 1.0], 'GSM3494937': [nan, nan, nan], 'GSM3494938': [nan, 23.0, 0.0], 'GSM3494939': [0.0, 34.0, 1.0], 'GSM3494940': [0.0, 43.0, 1.0], 'GSM3494941': [0.0, 47.0, 1.0], 'GSM3494942': [0.0, 24.0, 0.0], 'GSM3494943': [0.0, 55.0, 1.0], 'GSM3494944': [0.0, 48.0, 1.0], 'GSM3494945': [0.0, 58.0, 1.0], 'GSM3494946': [0.0, 30.0, 0.0], 'GSM3494947': [0.0, 28.0, 1.0], 'GSM3494948': [0.0, 41.0, 0.0], 'GSM3494949': [0.0, 63.0, 1.0], 'GSM3494950': [0.0, 55.0, 0.0], 'GSM3494951': [0.0, 55.0, 0.0], 'GSM3494952': [0.0, 67.0, 1.0], 'GSM3494953': [0.0, 47.0, 0.0], 'GSM3494954': [0.0, 46.0, 0.0], 'GSM3494955': [0.0, 49.0, 1.0], 'GSM3494956': [0.0, 23.0, 1.0], 'GSM3494957': [0.0, 68.0, 1.0], 'GSM3494958': [0.0, 39.0, 1.0], 'GSM3494959': [0.0, 24.0, 1.0], 'GSM3494960': [0.0, 36.0, 0.0], 'GSM3494961': [0.0, 58.0, 0.0], 'GSM3494962': [0.0, 38.0, 0.0], 'GSM3494963': [0.0, 27.0, 0.0], 'GSM3494964': [0.0, 67.0, 0.0], 'GSM3494965': [0.0, 61.0, 1.0], 'GSM3494966': [0.0, 69.0, 1.0], 'GSM3494967': [0.0, 63.0, 1.0], 'GSM3494968': [0.0, 60.0, 0.0], 'GSM3494969': [0.0, 17.0, 1.0], 'GSM3494970': [0.0, 10.0, 0.0], 'GSM3494971': [0.0, 9.0, 1.0], 'GSM3494972': [0.0, 13.0, 0.0], 'GSM3494973': [0.0, 10.0, 1.0], 'GSM3494974': [0.0, 13.0, 0.0], 'GSM3494975': [0.0, 15.0, 1.0], 'GSM3494976': [0.0, 12.0, 1.0], 'GSM3494977': [0.0, 13.0, 1.0], 'GSM3494978': [nan, 81.0, 0.0], 'GSM3494979': [nan, 94.0, 0.0], 'GSM3494980': [nan, 51.0, 1.0], 'GSM3494981': [nan, 40.0, 1.0], 'GSM3494982': [nan, nan, nan], 'GSM3494983': [nan, 97.0, 1.0], 'GSM3494984': [nan, 23.0, 1.0], 'GSM3494985': [nan, 93.0, 0.0], 'GSM3494986': [nan, 58.0, 1.0], 'GSM3494987': [nan, 28.0, 0.0], 'GSM3494988': [nan, 54.0, 1.0], 'GSM3494989': [nan, 15.0, 1.0], 'GSM3494990': [nan, 8.0, 1.0], 'GSM3494991': [nan, 11.0, 1.0], 'GSM3494992': [nan, 12.0, 1.0], 'GSM3494993': [nan, 8.0, 0.0], 'GSM3494994': [nan, 14.0, 1.0], 'GSM3494995': [nan, 8.0, 0.0], 'GSM3494996': [nan, 10.0, 1.0], 'GSM3494997': [nan, 14.0, 1.0], 'GSM3494998': [nan, 13.0, 1.0], 'GSM3494999': [nan, 40.0, 0.0], 'GSM3495000': [nan, 52.0, 0.0], 'GSM3495001': [nan, 42.0, 0.0], 'GSM3495002': [nan, 29.0, 0.0], 'GSM3495003': [nan, 43.0, 0.0], 'GSM3495004': [nan, 41.0, 0.0], 'GSM3495005': [nan, 54.0, 1.0], 'GSM3495006': [nan, 42.0, 1.0], 'GSM3495007': [nan, 49.0, 1.0], 'GSM3495008': [nan, 45.0, 0.0], 'GSM3495009': [nan, 56.0, 1.0], 'GSM3495010': [nan, 64.0, 0.0], 'GSM3495011': [nan, 71.0, 0.0], 'GSM3495012': [nan, 48.0, 0.0], 'GSM3495013': [nan, 20.0, 1.0], 'GSM3495014': [nan, 53.0, 0.0], 'GSM3495015': [nan, 32.0, 0.0], 'GSM3495016': [nan, 26.0, 0.0], 'GSM3495017': [nan, 28.0, 0.0], 'GSM3495018': [nan, 47.0, 0.0], 'GSM3495019': [nan, 24.0, 0.0], 'GSM3495020': [nan, 48.0, 0.0], 'GSM3495021': [nan, nan, nan], 'GSM3495022': [nan, 19.0, 0.0], 'GSM3495023': [nan, 41.0, 0.0], 'GSM3495024': [nan, 38.0, 0.0], 'GSM3495025': [nan, nan, nan], 'GSM3495026': [nan, 15.0, 0.0], 'GSM3495027': [nan, 12.0, 1.0], 'GSM3495028': [nan, 13.0, 0.0], 'GSM3495029': [nan, nan, nan], 'GSM3495030': [nan, 11.0, 1.0], 'GSM3495031': [nan, nan, nan], 'GSM3495032': [nan, 16.0, 1.0], 'GSM3495033': [nan, 11.0, 1.0], 'GSM3495034': [nan, nan, nan], 'GSM3495035': [nan, 35.0, 0.0], 'GSM3495036': [nan, 26.0, 0.0], 'GSM3495037': [nan, 39.0, 0.0], 'GSM3495038': [nan, 46.0, 0.0], 'GSM3495039': [nan, 42.0, 0.0], 'GSM3495040': [nan, 20.0, 1.0], 'GSM3495041': [nan, 69.0, 1.0], 'GSM3495042': [nan, 69.0, 0.0], 'GSM3495043': [nan, 47.0, 1.0], 'GSM3495044': [nan, 47.0, 1.0], 'GSM3495045': [nan, 56.0, 0.0], 'GSM3495046': [nan, 54.0, 0.0], 'GSM3495047': [nan, 53.0, 0.0], 'GSM3495048': [nan, 50.0, 0.0], 'GSM3495049': [nan, 22.0, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE123086.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the Series_overall_design, this dataset contains microarray data from CD4+ T cells\n",
"# which would provide gene expression data, not just miRNA or methylation\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# For trait - Crohn's disease appears in row 1 under \"primary diagnosis\"\n",
"trait_row = 1\n",
"\n",
"# For gender - appears in rows 2 and 3, but row 2 seems to be more complete\n",
"gender_row = 2 \n",
"\n",
"# For age - appears in rows 3 and 4\n",
"age_row = 3\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" if not isinstance(value, str):\n",
" return None\n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Matching trait (Crohn's Disease)\n",
" if \"CROHN\" in value.upper():\n",
" return 1\n",
" # Healthy controls should be 0\n",
" elif \"HEALTHY\" in value.upper() or \"CONTROL\" in value.upper():\n",
" return 0\n",
" # Other diseases are not relevant for our binary classification\n",
" else:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" if not isinstance(value, str):\n",
" return None\n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Female is 0, Male is 1\n",
" if value.upper() == \"FEMALE\":\n",
" return 0\n",
" elif value.upper() == \"MALE\":\n",
" return 1\n",
" # If it's a diagnosis2 field, return None as it's not gender data\n",
" elif \"DIAGNOSIS2\" in value.upper():\n",
" return None\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" if not isinstance(value, str):\n",
" return None\n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Try to convert to float for continuous age\n",
" try:\n",
" return float(value)\n",
" except:\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 using validate_and_save_cohort_info\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 not None, we proceed with clinical feature extraction\n",
"if trait_row is not None:\n",
" # Extract clinical features using the geo_select_clinical_features function\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the extracted features\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "d1038144",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "608edba3",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:31:54.339030Z",
"iopub.status.busy": "2025-03-25T08:31:54.338825Z",
"iopub.status.idle": "2025-03-25T08:31:54.748049Z",
"shell.execute_reply": "2025-03-25T08:31:54.747652Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"First 20 gene/probe identifiers:\n",
"Index(['1', '2', '3', '9', '10', '12', '13', '14', '15', '16', '18', '19',\n",
" '20', '21', '22', '23', '24', '25', '26', '27'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene data dimensions: 22881 genes × 166 samples\n"
]
}
],
"source": [
"# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract the gene expression data from the matrix file\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers)\n",
"print(\"\\nFirst 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n",
"\n",
"# 4. Print the dimensions of the gene expression data\n",
"print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
"is_gene_available = True\n"
]
},
{
"cell_type": "markdown",
"id": "68513421",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b17de00e",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# Examining the gene identifiers\n",
"# The identifiers appear to be numerical values (1, 2, 3, etc.)\n",
"# These are not standard human gene symbols, which are typically alphanumeric \n",
"# (like BRCA1, TP53, etc.)\n",
"# These appear to be probe IDs or some other form of identifiers that would\n",
"# need to be mapped to standard gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "8d4e6390",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5b49e7b5",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:31:58.331624Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation dataframe column names:\n",
"Index(['ID', 'ENTREZ_GENE_ID', 'SPOT_ID'], dtype='object')\n",
"\n",
"Preview of gene annotation data:\n",
"{'ID': ['1', '2', '3'], 'ENTREZ_GENE_ID': ['1', '2', '3'], 'SPOT_ID': [1.0, 2.0, 3.0]}\n"
]
}
],
"source": [
"# 1. First get the file paths using geo_get_relevant_filepaths function\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract gene annotation data from the SOFT file\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 3. Preview the gene annotation dataframe\n",
"print(\"Gene annotation dataframe column names:\")\n",
"print(gene_annotation.columns)\n",
"\n",
"# Preview the first few rows to understand the data structure\n",
"print(\"\\nPreview of gene annotation data:\")\n",
"annotation_preview = preview_df(gene_annotation, n=3)\n",
"print(annotation_preview)\n",
"\n",
"# Maintain gene availability status as True based on previous steps\n",
"is_gene_available = True\n"
]
},
{
"cell_type": "markdown",
"id": "88933adc",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "79475a3d",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:32:05.569287Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation first few rows:\n",
" ID ENTREZ_GENE_ID SPOT_ID\n",
"0 1 1 1.0\n",
"1 2 2 2.0\n",
"2 3 3 3.0\n",
"3 9 9 9.0\n",
"4 10 10 10.0\n",
"\n",
"Sample values in ENTREZ_GENE_ID column:\n",
"0 1\n",
"1 2\n",
"2 3\n",
"3 9\n",
"4 10\n",
"5 12\n",
"6 13\n",
"7 14\n",
"8 15\n",
"9 16\n",
"Name: ENTREZ_GENE_ID, dtype: object\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Check if gene symbols are available in the SOFT file:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Cleaned gene mapping:\n",
" ID Gene\n",
"0 1 1\n",
"1 2 2\n",
"2 3 3\n",
"3 9 9\n",
"4 10 10\n",
"Mapping shape after cleaning: (3822578, 2)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data after mapping:\n",
"Number of genes: 0\n",
"Number of samples: 166\n",
"No genes were mapped successfully.\n"
]
}
],
"source": [
"# Let's examine the gene_annotation data more carefully to understand the structure\n",
"print(\"Gene annotation first few rows:\")\n",
"print(gene_annotation.head())\n",
"\n",
"# Check what's in the ENTREZ_GENE_ID column - we need actual gene identifiers\n",
"print(\"\\nSample values in ENTREZ_GENE_ID column:\")\n",
"print(gene_annotation['ENTREZ_GENE_ID'].head(10))\n",
"\n",
"# The issue is that we need proper gene symbols, not just Entrez IDs\n",
"# Let's check if we have access to proper gene symbols by fetching the platform annotation\n",
"# from the SOFT file\n",
"\n",
"# Parse the SOFT file to get platform information including gene symbols\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" soft_content = f.read()\n",
"\n",
"# Look for sections containing gene symbol information\n",
"print(\"\\nCheck if gene symbols are available in the SOFT file:\")\n",
"gene_symbol_lines = [line for line in soft_content.split('\\n') if 'gene_symbol' in line.lower()][:5]\n",
"print(gene_symbol_lines)\n",
"\n",
"# If we don't find gene symbols directly, we'll use the Entrez Gene IDs as identifiers\n",
"# since they can be mapped to gene symbols later\n",
"\n",
"# Create a mapping dataframe with ID and Entrez Gene ID\n",
"gene_mapping = gene_annotation[['ID', 'ENTREZ_GENE_ID']].copy()\n",
"gene_mapping.rename(columns={'ENTREZ_GENE_ID': 'Gene'}, inplace=True)\n",
"\n",
"# Filter out any rows with missing values\n",
"gene_mapping = gene_mapping.dropna()\n",
"\n",
"# Print a preview of the cleaned mapping\n",
"print(\"\\nCleaned gene mapping:\")\n",
"print(gene_mapping.head())\n",
"print(f\"Mapping shape after cleaning: {gene_mapping.shape}\")\n",
"\n",
"# Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"# We'll use the standard function but make sure our Gene column has proper values\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print statistics about the resulting gene data\n",
"print(f\"\\nGene expression data after mapping:\")\n",
"print(f\"Number of genes: {gene_data.shape[0]}\")\n",
"print(f\"Number of samples: {gene_data.shape[1]}\")\n",
"\n",
"# Check the first few gene identifiers - they should be Entrez IDs\n",
"if gene_data.shape[0] > 0:\n",
" print(\"First 5 gene identifiers:\")\n",
" print(gene_data.index[:5])\n",
"else:\n",
" print(\"No genes were mapped successfully.\")\n",
"\n",
"# For this dataset, since we don't have access to proper gene symbols, \n",
"# we'll treat the Entrez Gene IDs as our gene identifiers\n",
"# Later normalization can map these to standard gene symbols if needed\n",
"\n",
"# Verify we have non-empty gene expression data\n",
"is_gene_available = gene_data.shape[0] > 0\n"
]
},
{
"cell_type": "markdown",
"id": "fc4c1efa",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2e150f28",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:32:05.571074Z",
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"shell.execute_reply": "2025-03-25T08:32:05.577484Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error: Gene expression matrix is empty after mapping.\n",
"Abnormality detected in the cohort: GSE123086. Preprocessing failed.\n",
"A new JSON file was created at: ../../output/preprocess/Crohns_Disease/cohort_info.json\n",
"Dataset deemed not usable due to lack of gene expression data.\n"
]
}
],
"source": [
"# 1. Check if gene data is available after mapping\n",
"if gene_data.shape[0] == 0:\n",
" print(\"Error: Gene expression matrix is empty after mapping.\")\n",
" # Mark the dataset as not usable due to lack of gene expression data\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=False, # No usable gene data\n",
" is_trait_available=True,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"Failed to map probe IDs to gene symbols. The annotation format may not be compatible with the extraction methods.\"\n",
" )\n",
" print(\"Dataset deemed not usable due to lack of gene expression data.\")\n",
"else:\n",
" # Only proceed with normalization if we have gene data\n",
" print(\"Normalizing gene symbols...\")\n",
" gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
" print(f\"Gene data shape after normalization: {gene_data_normalized.shape[0]} genes × {gene_data_normalized.shape[1]} samples\")\n",
"\n",
" # Save the normalized gene data\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" gene_data_normalized.to_csv(out_gene_data_file)\n",
" print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
" \n",
" # Extract clinical features from the original data source\n",
" print(\"Extracting clinical features from the original source...\")\n",
" # Get background information and clinical data again\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",
" # Extract clinical features\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" print(\"Extracted clinical features preview:\")\n",
" print(preview_df(selected_clinical_df))\n",
" print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
" \n",
" # Save the extracted clinical features\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 features saved to {out_clinical_data_file}\")\n",
" \n",
" # Link clinical and genetic data\n",
" print(\"Linking clinical and genetic data...\")\n",
" linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data_normalized)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" \n",
" # Check if the linked data has adequate data\n",
" if linked_data.shape[0] == 0 or linked_data.shape[1] <= 4: # 4 is an arbitrary small number\n",
" print(\"Error: Linked data has insufficient samples or features.\")\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=True,\n",
" df=linked_data,\n",
" note=\"Failed to properly link gene expression data with clinical features.\"\n",
" )\n",
" print(\"Dataset deemed not usable due to linking failure.\")\n",
" else:\n",
" # Handle missing values systematically\n",
" print(\"Handling missing values...\")\n",
" linked_data_clean = handle_missing_values(linked_data, trait_col=trait)\n",
" print(f\"Data shape after handling missing values: {linked_data_clean.shape}\")\n",
" \n",
" # Check if there are still samples after missing value handling\n",
" if linked_data_clean.shape[0] == 0:\n",
" print(\"Error: No samples remain after handling missing values.\")\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=True,\n",
" df=pd.DataFrame(),\n",
" note=\"All samples were removed during missing value handling.\"\n",
" )\n",
" print(\"Dataset deemed not usable as all samples were filtered out.\")\n",
" else:\n",
" # Check if the dataset is biased\n",
" print(\"\\nChecking for bias in feature variables:\")\n",
" is_biased, linked_data_final = judge_and_remove_biased_features(linked_data_clean, trait)\n",
" \n",
" # Conduct final quality validation\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_final,\n",
" note=\"Dataset contains gene expression data for Crohn's Disease patients, examining response to Infliximab treatment.\"\n",
" )\n",
" \n",
" # Save linked data if usable\n",
" if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data_final.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" print(f\"Final dataset shape: {linked_data_final.shape}\")\n",
" else:\n",
" print(\"Dataset deemed not usable for trait association studies, linked data not saved.\")"
]
}
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