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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a4104299",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:36:11.101929Z",
"iopub.status.busy": "2025-03-25T08:36:11.101699Z",
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"shell.execute_reply": "2025-03-25T08:36:11.266645Z"
}
},
"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 = \"Cystic_Fibrosis\"\n",
"cohort = \"GSE67698\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Cystic_Fibrosis\"\n",
"in_cohort_dir = \"../../input/GEO/Cystic_Fibrosis/GSE67698\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Cystic_Fibrosis/GSE67698.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Cystic_Fibrosis/gene_data/GSE67698.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE67698.csv\"\n",
"json_path = \"../../output/preprocess/Cystic_Fibrosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "b6f70ad0",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8ad100c8",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:36:11.268275Z",
"iopub.status.busy": "2025-03-25T08:36:11.268127Z",
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"shell.execute_reply": "2025-03-25T08:36:11.458203Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Transcriptional profiling of CFBE41o-ΔF508 cells and CFBE41o−CFTR cells.\"\n",
"!Series_summary\t\"Cystic fibrosis bronchial epithelial (CFBE41o-ΔF508) cells subjected to 23 bio-active small molecules including vehicle controls, at low temperature and untreated cells. Untreated Cystic fibrosis bronchial epithelial cells (CFBE41o−CFTR) are also included.\"\n",
"!Series_overall_design\t\"Two-colors Dye-swap, two or three biological replicates with two technical replicates each and DMSO (vehicle control) were included in each hybridization batch.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell line: polarized CFBE41o-cell line'], 1: ['transduction: TranzVector lentivectors containing deltaF508 CFTR (CFBE41o-deltaF508CFTR)', 'transduction: TranzVector lentivectors containing wildtype CFTR (CFBE41o-CFTR)']}\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": "9daf3bea",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d5e192f3",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:36:11.460517Z",
"iopub.status.busy": "2025-03-25T08:36:11.460407Z",
"iopub.status.idle": "2025-03-25T08:36:11.488249Z",
"shell.execute_reply": "2025-03-25T08:36:11.487750Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{'GSM1946634': [1.0], 'GSM1946635': [1.0], 'GSM1946636': [1.0], 'GSM1946637': [1.0], 'GSM1946638': [1.0], 'GSM1946639': [1.0], 'GSM1946640': [1.0], 'GSM1946641': [1.0], 'GSM1946642': [1.0], 'GSM1946643': [1.0], 'GSM1946644': [1.0], 'GSM1946645': [1.0], 'GSM1946646': [1.0], 'GSM1946647': [1.0], 'GSM1946648': [1.0], 'GSM1946649': [1.0], 'GSM1946650': [1.0], 'GSM1946651': [1.0], 'GSM1946652': [1.0], 'GSM1946653': [1.0], 'GSM1946654': [1.0], 'GSM1946655': [1.0], 'GSM1946656': [1.0], 'GSM1946657': [1.0], 'GSM1946658': [1.0], 'GSM1946659': [1.0], 'GSM1946660': [1.0], 'GSM1946661': [1.0], 'GSM1946662': [1.0], 'GSM1946663': [1.0], 'GSM1946664': [1.0], 'GSM1946665': [1.0], 'GSM1946666': [1.0], 'GSM1946667': [1.0], 'GSM1946668': [1.0], 'GSM1946669': [1.0], 'GSM1946670': [1.0], 'GSM1946671': [1.0], 'GSM1946672': [1.0], 'GSM1946673': [1.0], 'GSM1946674': [1.0], 'GSM1946675': [1.0], 'GSM1946676': [1.0], 'GSM1946677': [1.0], 'GSM1946678': [1.0], 'GSM1946679': [1.0], 'GSM1946680': [1.0], 'GSM1946681': [1.0], 'GSM1946682': [1.0], 'GSM1946683': [1.0], 'GSM1946684': [1.0], 'GSM1946685': [1.0], 'GSM1946686': [0.0], 'GSM1946687': [0.0], 'GSM1946688': [0.0], 'GSM1946689': [0.0], 'GSM1946690': [1.0], 'GSM1946691': [1.0], 'GSM1946692': [1.0], 'GSM1946693': [1.0], 'GSM1946694': [1.0], 'GSM1946695': [1.0], 'GSM1946696': [1.0], 'GSM1946697': [1.0], 'GSM1946698': [1.0], 'GSM1946699': [1.0], 'GSM1946700': [1.0], 'GSM1946701': [1.0], 'GSM1946702': [1.0], 'GSM1946703': [1.0], 'GSM1946704': [1.0], 'GSM1946705': [1.0], 'GSM1946706': [1.0], 'GSM1946707': [1.0], 'GSM1946708': [1.0], 'GSM1946709': [1.0], 'GSM1946710': [1.0], 'GSM1946711': [1.0], 'GSM1946712': [1.0], 'GSM1946713': [1.0], 'GSM1946714': [1.0], 'GSM1946715': [1.0], 'GSM1946716': [1.0], 'GSM1946717': [1.0], 'GSM1946718': [1.0], 'GSM1946719': [1.0], 'GSM1946720': [1.0], 'GSM1946721': [1.0], 'GSM1946722': [1.0], 'GSM1946723': [1.0], 'GSM1946724': [1.0], 'GSM1946725': [1.0], 'GSM1946726': [1.0], 'GSM1946727': [1.0], 'GSM1946728': [1.0], 'GSM1946729': [1.0], 'GSM1946730': [1.0], 'GSM1946731': [1.0], 'GSM1946732': [1.0], 'GSM1946733': [1.0], 'GSM1946734': [1.0], 'GSM1946735': [1.0], 'GSM1946736': [1.0], 'GSM1946737': [1.0], 'GSM1946738': [1.0], 'GSM1946739': [1.0], 'GSM1946740': [1.0], 'GSM1946741': [1.0], 'GSM1946742': [1.0], 'GSM1946743': [1.0], 'GSM1946744': [1.0], 'GSM1946745': [1.0], 'GSM1946746': [1.0], 'GSM1946747': [1.0], 'GSM1946748': [1.0], 'GSM1946749': [1.0], 'GSM1946750': [1.0], 'GSM1946751': [1.0], 'GSM1946752': [1.0], 'GSM1946753': [1.0], 'GSM1946754': [1.0], 'GSM1946755': [1.0]}\n",
"Clinical data saved to ../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE67698.csv\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"import json\n",
"from typing import Callable, Optional, Dict, Any\n",
"\n",
"# 1. Assess gene expression data availability\n",
"# Based on the background information, this dataset contains transcriptional profiling data\n",
"# which indicates gene expression data is available\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Identify rows for trait, age, and gender\n",
"# From the sample characteristics, we can see information about cell line and transduction type\n",
"# The transduction type (row 1) indicates whether cells have deltaF508 CFTR mutation or wildtype CFTR\n",
"# This can be used as our trait (presence of cystic fibrosis mutation)\n",
"trait_row = 1\n",
"# Age and gender information are not available in this dataset\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# 2.2 Define conversion functions\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert CF status based on transduction information\n",
" deltaF508 CFTR = 1 (CF mutation present)\n",
" wildtype CFTR = 0 (No CF mutation)\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if isinstance(value, str):\n",
" if \"deltaF508\" in value:\n",
" return 1 # CF mutation\n",
" elif \"wildtype CFTR\" in value:\n",
" return 0 # No CF mutation\n",
" \n",
" return None\n",
"\n",
"def convert_age(value):\n",
" # Not applicable for this dataset\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # Not applicable for this dataset\n",
" return None\n",
"\n",
"# 3. Save metadata - perform initial filtering\n",
"# Check if trait data is available (trait_row is not None)\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Initial validation to check if dataset passes basic requirements\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. Extract clinical features if trait data is available\n",
"if trait_row is not None:\n",
" # Make sure the output directory exists\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Access the clinical data that was loaded in a previous step\n",
" # This is typically a DataFrame with samples as rows and characteristic indices as columns\n",
" # We'll assume clinical_data exists from previous steps\n",
" \n",
" # Extract clinical features\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data, # Use existing clinical_data from previous step\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 selected clinical features\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical data to CSV\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "68cf1d95",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "077fc49c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:36:11.489917Z",
"iopub.status.busy": "2025-03-25T08:36:11.489809Z",
"iopub.status.idle": "2025-03-25T08:36:11.889760Z",
"shell.execute_reply": "2025-03-25T08:36:11.889120Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found data marker at line 69\n",
"Header line: \"ID_REF\"\t\"GSM1946634\"\t\"GSM1946635\"\t\"GSM1946636\"\t\"GSM1946637\"\t\"GSM1946638\"\t\"GSM1946639\"\t\"GSM1946640\"\t\"GSM1946641\"\t\"GSM1946642\"\t\"GSM1946643\"\t\"GSM1946644\"\t\"GSM1946645\"\t\"GSM1946646\"\t\"GSM1946647\"\t\"GSM1946648\"\t\"GSM1946649\"\t\"GSM1946650\"\t\"GSM1946651\"\t\"GSM1946652\"\t\"GSM1946653\"\t\"GSM1946654\"\t\"GSM1946655\"\t\"GSM1946656\"\t\"GSM1946657\"\t\"GSM1946658\"\t\"GSM1946659\"\t\"GSM1946660\"\t\"GSM1946661\"\t\"GSM1946662\"\t\"GSM1946663\"\t\"GSM1946664\"\t\"GSM1946665\"\t\"GSM1946666\"\t\"GSM1946667\"\t\"GSM1946668\"\t\"GSM1946669\"\t\"GSM1946670\"\t\"GSM1946671\"\t\"GSM1946672\"\t\"GSM1946673\"\t\"GSM1946674\"\t\"GSM1946675\"\t\"GSM1946676\"\t\"GSM1946677\"\t\"GSM1946678\"\t\"GSM1946679\"\t\"GSM1946680\"\t\"GSM1946681\"\t\"GSM1946682\"\t\"GSM1946683\"\t\"GSM1946684\"\t\"GSM1946685\"\t\"GSM1946686\"\t\"GSM1946687\"\t\"GSM1946688\"\t\"GSM1946689\"\t\"GSM1946690\"\t\"GSM1946691\"\t\"GSM1946692\"\t\"GSM1946693\"\t\"GSM1946694\"\t\"GSM1946695\"\t\"GSM1946696\"\t\"GSM1946697\"\t\"GSM1946698\"\t\"GSM1946699\"\t\"GSM1946700\"\t\"GSM1946701\"\t\"GSM1946702\"\t\"GSM1946703\"\t\"GSM1946704\"\t\"GSM1946705\"\t\"GSM1946706\"\t\"GSM1946707\"\t\"GSM1946708\"\t\"GSM1946709\"\t\"GSM1946710\"\t\"GSM1946711\"\t\"GSM1946712\"\t\"GSM1946713\"\t\"GSM1946714\"\t\"GSM1946715\"\t\"GSM1946716\"\t\"GSM1946717\"\t\"GSM1946718\"\t\"GSM1946719\"\t\"GSM1946720\"\t\"GSM1946721\"\t\"GSM1946722\"\t\"GSM1946723\"\t\"GSM1946724\"\t\"GSM1946725\"\t\"GSM1946726\"\t\"GSM1946727\"\t\"GSM1946728\"\t\"GSM1946729\"\t\"GSM1946730\"\t\"GSM1946731\"\t\"GSM1946732\"\t\"GSM1946733\"\t\"GSM1946734\"\t\"GSM1946735\"\t\"GSM1946736\"\t\"GSM1946737\"\t\"GSM1946738\"\t\"GSM1946739\"\t\"GSM1946740\"\t\"GSM1946741\"\t\"GSM1946742\"\t\"GSM1946743\"\t\"GSM1946744\"\t\"GSM1946745\"\t\"GSM1946746\"\t\"GSM1946747\"\t\"GSM1946748\"\t\"GSM1946749\"\t\"GSM1946750\"\t\"GSM1946751\"\t\"GSM1946752\"\t\"GSM1946753\"\t\"GSM1946754\"\t\"GSM1946755\"\n",
"First data line: \"A_23_P100001\"\t-2.8787\t-2.5598\t-2.5796\t-2.5329\t-0.2065\t-0.6002\t-2.8878\t-3.1946\t-0.0517\t0.0390\t-3.1205\t-2.7660\t-3.3119\t-2.3934\t-3.4219\t-2.5577\t-2.7781\t-2.8961\t-2.4142\t-2.5723\t-1.8035\t-1.9974\t-2.6340\t-2.1938\t-3.3447\t-3.7593\t-2.3820\t-3.2490\t-2.7328\t-2.7992\t-3.5729\t-4.0990\t-2.2118\t-2.2726\t-3.3225\t-3.5793\t-2.3955\t-3.0237\t-3.0172\t-3.3335\t-2.6537\t-2.7303\t-1.9556\t-2.0283\t-1.8409\t-1.7097\t-1.6155\t-1.7088\t-2.6423\t-3.2713\t-2.5211\t-2.0271\t1.1176\t0.4275\t0.2648\t0.2237\t-2.9698\t-2.6803\t-1.9177\t-2.3395\t-1.7564\t-1.4396\t-2.5793\t-2.2490\t-2.6141\t-2.8285\t-2.5020\t-2.3917\t-2.5550\t-2.2365\t-3.1108\t-3.0147\t-3.2419\t-3.3621\t-2.8760\t-3.0787\t-2.2880\t-2.3686\t-2.6394\t-2.7403\t-2.1537\t-2.5304\t-2.6259\t-2.2200\t-2.3386\t-2.8193\t-2.9143\t-3.0036\t-2.8826\t-3.8299\t-2.4721\t-2.7392\t-3.1828\t-3.2706\t-2.6757\t-2.6363\t-2.8124\t-3.0037\t-2.8452\t-3.2319\t-3.1110\t-2.8970\t-2.4607\t-2.5720\t-3.5185\t-4.4907\t-2.5415\t-2.7348\t-2.8109\t-2.9797\t-3.2431\t-4.0171\t-2.6663\t-2.2648\t-1.4216\t-2.3937\t-2.6968\t-2.3184\t-1.9126\t-2.0352\t-3.3332\t-4.1652\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056',\n",
" 'A_23_P100074', 'A_23_P100092', 'A_23_P100103', 'A_23_P100111',\n",
" 'A_23_P100127', 'A_23_P100133', 'A_23_P100141', 'A_23_P100156',\n",
" 'A_23_P100177', 'A_23_P100189', 'A_23_P100196', 'A_23_P100203',\n",
" 'A_23_P100220', 'A_23_P100240', 'A_23_P10025', 'A_23_P100263'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Get the file paths for the SOFT file and matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. First, let's examine the structure of the matrix file to understand its format\n",
"import gzip\n",
"\n",
"# Peek at the first few lines of the file to understand its structure\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" # Read first 100 lines to find the header structure\n",
" for i, line in enumerate(file):\n",
" if '!series_matrix_table_begin' in line:\n",
" print(f\"Found data marker at line {i}\")\n",
" # Read the next line which should be the header\n",
" header_line = next(file)\n",
" print(f\"Header line: {header_line.strip()}\")\n",
" # And the first data line\n",
" first_data_line = next(file)\n",
" print(f\"First data line: {first_data_line.strip()}\")\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Matrix table marker not found in first 100 lines\")\n",
" break\n",
"\n",
"# 3. Now try to get the genetic data with better error handling\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(gene_data.index[:20])\n",
"except KeyError as e:\n",
" print(f\"KeyError: {e}\")\n",
" \n",
" # Alternative approach: manually extract the data\n",
" print(\"\\nTrying alternative approach to read the gene data:\")\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Find the start of the data\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" \n",
" # Read the headers and data\n",
" import pandas as pd\n",
" df = pd.read_csv(file, sep='\\t', index_col=0)\n",
" print(f\"Column names: {df.columns[:5]}\")\n",
" print(f\"First 20 row IDs: {df.index[:20]}\")\n",
" gene_data = df\n"
]
},
{
"cell_type": "markdown",
"id": "f57e69d0",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6063f53f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:36:11.891524Z",
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"shell.execute_reply": "2025-03-25T08:36:11.893373Z"
}
},
"outputs": [],
"source": [
"# Based on the gene identifiers, these appear to be Agilent microarray probe IDs (starting with A_23_P),\n",
"# not standard human gene symbols. These will need to be mapped to official gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "ad5198d7",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a382c4f3",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:36:11.895466Z",
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"shell.execute_reply": "2025-03-25T08:36:20.831540Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'SPOT_ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'CONTROL_TYPE': ['FALSE', 'FALSE', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GB_ACC': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GENE': [400451.0, 10239.0, 9899.0, 348093.0, 57099.0], 'GENE_SYMBOL': ['FAM174B', 'AP3S2', 'SV2B', 'RBPMS2', 'AVEN'], 'GENE_NAME': ['family with sequence similarity 174, member B', 'adaptor-related protein complex 3, sigma 2 subunit', 'synaptic vesicle glycoprotein 2B', 'RNA binding protein with multiple splicing 2', 'apoptosis, caspase activation inhibitor'], 'UNIGENE_ID': ['Hs.27373', 'Hs.632161', 'Hs.21754', 'Hs.436518', 'Hs.555966'], 'ENSEMBL_ID': ['ENST00000557398', nan, 'ENST00000557410', 'ENST00000300069', 'ENST00000306730'], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': ['ref|NM_207446|ens|ENST00000557398|ens|ENST00000553393|ens|ENST00000327355', 'ref|NM_005829|ref|NM_001199058|ref|NR_023361|ref|NR_037582', 'ref|NM_014848|ref|NM_001167580|ens|ENST00000557410|ens|ENST00000330276', 'ref|NM_194272|ens|ENST00000300069|gb|AK127873|gb|AK124123', 'ref|NM_020371|ens|ENST00000306730|gb|AF283508|gb|BC010488'], 'CHROMOSOMAL_LOCATION': ['chr15:93160848-93160789', 'chr15:90378743-90378684', 'chr15:91838329-91838388', 'chr15:65032375-65032316', 'chr15:34158739-34158680'], 'CYTOBAND': ['hs|15q26.1', 'hs|15q26.1', 'hs|15q26.1', 'hs|15q22.31', 'hs|15q14'], 'DESCRIPTION': ['Homo sapiens family with sequence similarity 174, member B (FAM174B), mRNA [NM_207446]', 'Homo sapiens adaptor-related protein complex 3, sigma 2 subunit (AP3S2), transcript variant 1, mRNA [NM_005829]', 'Homo sapiens synaptic vesicle glycoprotein 2B (SV2B), transcript variant 1, mRNA [NM_014848]', 'Homo sapiens RNA binding protein with multiple splicing 2 (RBPMS2), mRNA [NM_194272]', 'Homo sapiens apoptosis, caspase activation inhibitor (AVEN), mRNA [NM_020371]'], 'GO_ID': ['GO:0016020(membrane)|GO:0016021(integral to membrane)', 'GO:0005794(Golgi apparatus)|GO:0006886(intracellular protein transport)|GO:0008565(protein transporter activity)|GO:0016020(membrane)|GO:0016192(vesicle-mediated transport)|GO:0030117(membrane coat)|GO:0030659(cytoplasmic vesicle membrane)|GO:0031410(cytoplasmic vesicle)', 'GO:0001669(acrosomal vesicle)|GO:0006836(neurotransmitter transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0022857(transmembrane transporter activity)|GO:0030054(cell junction)|GO:0030672(synaptic vesicle membrane)|GO:0031410(cytoplasmic vesicle)|GO:0045202(synapse)', 'GO:0000166(nucleotide binding)|GO:0003676(nucleic acid binding)', 'GO:0005515(protein binding)|GO:0005622(intracellular)|GO:0005624(membrane fraction)|GO:0006915(apoptosis)|GO:0006916(anti-apoptosis)|GO:0012505(endomembrane system)|GO:0016020(membrane)'], 'SEQUENCE': ['ATCTCATGGAAAAGCTGGATTCCTCTGCCTTACGCAGAAACACCCGGGCTCCATCTGCCA', 'TCAAGTATTGGCCTGACATAGAGTCCTTAAGACAAGCAAAGACAAGCAAGGCAAGCACGT', 'ATGTCGGCTGTGGAGGGTTAAAGGGATGAGGCTTTCCTTTGTTTAGCAAATCTGTTCACA', 'CCCTGTCAGATAAGTTTAATGTTTAGTTTGAGGCATGAAGAAGAAAAGGGTTTCCATTCT', 'GACCAGCCAGTTTACAAGCATGTCTCAAGCTAGTGTGTTCCATTATGCTCACAGCAGTAA']}\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",
"# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "eab7c3d6",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "50cce18a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:36:20.834296Z",
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"shell.execute_reply": "2025-03-25T08:36:22.306745Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data (after mapping):\n",
"Shape: (18488, 122)\n",
"First 5 gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2LD1', 'A2M']\n",
"First 5 sample columns: ['GSM1946634', 'GSM1946635', 'GSM1946636', 'GSM1946637', 'GSM1946638']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Cystic_Fibrosis/gene_data/GSE67698.csv\n"
]
}
],
"source": [
"# 1. Identify which columns to use for mapping\n",
"# From the preview, we can see that the 'ID' column contains the probe IDs (A_23_P...)\n",
"# and 'GENE_SYMBOL' contains the gene symbols we want to map to\n",
"probe_col = 'ID' # Column with probe identifiers\n",
"gene_col = 'GENE_SYMBOL' # Column with gene symbols\n",
"\n",
"# 2. Get gene mapping dataframe by extracting the probe and gene symbol columns\n",
"mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
"\n",
"# 3. Convert probe-level measurements to gene expression data\n",
"# This will handle the many-to-many mapping between probes and genes\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Print preview of the mapped gene expression data\n",
"print(\"Gene expression data (after mapping):\")\n",
"print(f\"Shape: {gene_data.shape}\")\n",
"print(f\"First 5 gene symbols: {gene_data.index[:5].tolist()}\")\n",
"print(f\"First 5 sample columns: {gene_data.columns[:5].tolist()}\")\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": "c3edc0fc",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "0e4f0bc2",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:36:22.308624Z",
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"shell.execute_reply": "2025-03-25T08:36:28.665735Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data shape: (18247, 122)\n",
"First few genes with their expression values after normalization:\n",
" GSM1946634 GSM1946635 GSM1946636 GSM1946637 GSM1946638 \\\n",
"Gene \n",
"A1BG 0.7790 0.3736 -0.7298 -0.5658 0.7993 \n",
"A1BG-AS1 1.4335 1.1881 0.9390 0.7998 1.9718 \n",
"A1CF -8.6711 -8.6980 -8.1731 -9.8930 -6.6285 \n",
"A2M -5.8238 -5.7392 -5.8264 -7.6944 -5.6462 \n",
"A2ML1 -0.9369 -0.8575 -0.6521 -0.4720 2.5128 \n",
"\n",
" GSM1946639 GSM1946640 GSM1946641 GSM1946642 GSM1946643 ... \\\n",
"Gene ... \n",
"A1BG -0.4768 -1.2291 -1.4813 0.8968 0.5161 ... \n",
"A1BG-AS1 1.2580 0.9186 0.8031 1.7652 1.5084 ... \n",
"A1CF -3.9316 -10.1926 -10.9090 -7.0169 -7.6412 ... \n",
"A2M -5.8414 -7.3668 -9.7768 -6.5750 -6.7933 ... \n",
"A2ML1 1.7026 -1.2058 -1.0400 3.4189 3.6057 ... \n",
"\n",
" GSM1946746 GSM1946747 GSM1946748 GSM1946749 GSM1946750 \\\n",
"Gene \n",
"A1BG 0.9995 1.1157 -0.9717 -2.3244 -1.4169 \n",
"A1BG-AS1 1.7237 1.3286 1.3644 0.5460 1.1454 \n",
"A1CF -8.8023 -8.5343 -8.8704 -7.6491 -9.6272 \n",
"A2M -6.0530 -6.1823 -6.1065 -7.3115 -7.3060 \n",
"A2ML1 -1.1442 -0.9833 0.5798 0.0510 -0.9032 \n",
"\n",
" GSM1946751 GSM1946752 GSM1946753 GSM1946754 GSM1946755 \n",
"Gene \n",
"A1BG -1.3968 1.0533 0.8462 -2.0529 -3.0817 \n",
"A1BG-AS1 0.9398 1.6896 1.3775 0.5250 0.1934 \n",
"A1CF -10.4649 -6.7870 -8.6401 -8.9628 -11.2042 \n",
"A2M -8.2849 -5.4245 -6.3632 -5.4934 -8.9906 \n",
"A2ML1 -1.1459 1.3318 1.9127 -1.6521 -0.8781 \n",
"\n",
"[5 rows x 122 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Cystic_Fibrosis/gene_data/GSE67698.csv\n",
"Clinical data preview:\n",
"{'GSM1946634': [1.0], 'GSM1946635': [1.0], 'GSM1946636': [1.0], 'GSM1946637': [1.0], 'GSM1946638': [1.0], 'GSM1946639': [1.0], 'GSM1946640': [1.0], 'GSM1946641': [1.0], 'GSM1946642': [1.0], 'GSM1946643': [1.0], 'GSM1946644': [1.0], 'GSM1946645': [1.0], 'GSM1946646': [1.0], 'GSM1946647': [1.0], 'GSM1946648': [1.0], 'GSM1946649': [1.0], 'GSM1946650': [1.0], 'GSM1946651': [1.0], 'GSM1946652': [1.0], 'GSM1946653': [1.0], 'GSM1946654': [1.0], 'GSM1946655': [1.0], 'GSM1946656': [1.0], 'GSM1946657': [1.0], 'GSM1946658': [1.0], 'GSM1946659': [1.0], 'GSM1946660': [1.0], 'GSM1946661': [1.0], 'GSM1946662': [1.0], 'GSM1946663': [1.0], 'GSM1946664': [1.0], 'GSM1946665': [1.0], 'GSM1946666': [1.0], 'GSM1946667': [1.0], 'GSM1946668': [1.0], 'GSM1946669': [1.0], 'GSM1946670': [1.0], 'GSM1946671': [1.0], 'GSM1946672': [1.0], 'GSM1946673': [1.0], 'GSM1946674': [1.0], 'GSM1946675': [1.0], 'GSM1946676': [1.0], 'GSM1946677': [1.0], 'GSM1946678': [1.0], 'GSM1946679': [1.0], 'GSM1946680': [1.0], 'GSM1946681': [1.0], 'GSM1946682': [1.0], 'GSM1946683': [1.0], 'GSM1946684': [1.0], 'GSM1946685': [1.0], 'GSM1946686': [0.0], 'GSM1946687': [0.0], 'GSM1946688': [0.0], 'GSM1946689': [0.0], 'GSM1946690': [1.0], 'GSM1946691': [1.0], 'GSM1946692': [1.0], 'GSM1946693': [1.0], 'GSM1946694': [1.0], 'GSM1946695': [1.0], 'GSM1946696': [1.0], 'GSM1946697': [1.0], 'GSM1946698': [1.0], 'GSM1946699': [1.0], 'GSM1946700': [1.0], 'GSM1946701': [1.0], 'GSM1946702': [1.0], 'GSM1946703': [1.0], 'GSM1946704': [1.0], 'GSM1946705': [1.0], 'GSM1946706': [1.0], 'GSM1946707': [1.0], 'GSM1946708': [1.0], 'GSM1946709': [1.0], 'GSM1946710': [1.0], 'GSM1946711': [1.0], 'GSM1946712': [1.0], 'GSM1946713': [1.0], 'GSM1946714': [1.0], 'GSM1946715': [1.0], 'GSM1946716': [1.0], 'GSM1946717': [1.0], 'GSM1946718': [1.0], 'GSM1946719': [1.0], 'GSM1946720': [1.0], 'GSM1946721': [1.0], 'GSM1946722': [1.0], 'GSM1946723': [1.0], 'GSM1946724': [1.0], 'GSM1946725': [1.0], 'GSM1946726': [1.0], 'GSM1946727': [1.0], 'GSM1946728': [1.0], 'GSM1946729': [1.0], 'GSM1946730': [1.0], 'GSM1946731': [1.0], 'GSM1946732': [1.0], 'GSM1946733': [1.0], 'GSM1946734': [1.0], 'GSM1946735': [1.0], 'GSM1946736': [1.0], 'GSM1946737': [1.0], 'GSM1946738': [1.0], 'GSM1946739': [1.0], 'GSM1946740': [1.0], 'GSM1946741': [1.0], 'GSM1946742': [1.0], 'GSM1946743': [1.0], 'GSM1946744': [1.0], 'GSM1946745': [1.0], 'GSM1946746': [1.0], 'GSM1946747': [1.0], 'GSM1946748': [1.0], 'GSM1946749': [1.0], 'GSM1946750': [1.0], 'GSM1946751': [1.0], 'GSM1946752': [1.0], 'GSM1946753': [1.0], 'GSM1946754': [1.0], 'GSM1946755': [1.0]}\n",
"Linked data shape (before handling missing values): (122, 18248)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data after handling missing values: (122, 18248)\n",
"For the feature 'Cystic_Fibrosis', the least common label is '0.0' with 4 occurrences. This represents 3.28% of the dataset.\n",
"Data was determined to be unusable and was not saved\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(\"First few genes with their expression values after normalization:\")\n",
"print(normalized_gene_data.head())\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",
"\n",
"# 2. Link the clinical and genetic data\n",
"# Use the clinical data that was already processed and saved in previous steps\n",
"selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(selected_clinical_df))\n",
"\n",
"# Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
"print(f\"Linked data shape (before handling missing values): {linked_data.shape}\")\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Data after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Determine whether the trait and demographic features are biased\n",
"# First check if trait is biased\n",
"trait_type = 'binary' if len(linked_data[trait].unique()) == 2 else 'continuous'\n",
"if trait_type == \"binary\":\n",
" is_trait_biased = judge_binary_variable_biased(linked_data, trait)\n",
"else:\n",
" is_trait_biased = judge_continuous_variable_biased(linked_data, trait)\n",
"\n",
"# Check for and remove biased demographic features\n",
"unbiased_linked_data = linked_data.copy()\n",
"if \"Age\" in unbiased_linked_data.columns:\n",
" age_biased = judge_continuous_variable_biased(unbiased_linked_data, 'Age')\n",
" if age_biased:\n",
" print(f\"The distribution of the feature \\'Age\\' in this dataset is severely biased.\\n\")\n",
" unbiased_linked_data = unbiased_linked_data.drop(columns='Age')\n",
" else:\n",
" print(f\"The distribution of the feature \\'Age\\' in this dataset is fine.\\n\")\n",
"\n",
"if \"Gender\" in unbiased_linked_data.columns:\n",
" gender_biased = judge_binary_variable_biased(unbiased_linked_data, 'Gender')\n",
" if gender_biased:\n",
" print(f\"The distribution of the feature \\'Gender\\' in this dataset is severely biased.\\n\")\n",
" unbiased_linked_data = unbiased_linked_data.drop(columns='Gender')\n",
" else:\n",
" print(f\"The distribution of the feature \\'Gender\\' in this dataset is fine.\\n\")\n",
"\n",
"# 5. Conduct final quality validation 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_trait_biased, \n",
" df=unbiased_linked_data,\n",
" note=f\"Dataset contains gene expression data comparing CFBE41o-ΔF508 (CF) cells with CFBE41o−CFTR (control) cells.\"\n",
")\n",
"\n",
"# 6. If the linked data is usable, save it\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Data was determined to be unusable and was not saved\")"
]
}
],
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|