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{
"cells": [
{
"cell_type": "code",
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"metadata": {
"execution": {
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"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"Age-Related_Macular_Degeneration\"\n",
"cohort = \"GSE67899\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Age-Related_Macular_Degeneration\"\n",
"in_cohort_dir = \"../../input/GEO/Age-Related_Macular_Degeneration/GSE67899\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Age-Related_Macular_Degeneration/GSE67899.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE67899.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE67899.csv\"\n",
"json_path = \"../../output/preprocess/Age-Related_Macular_Degeneration/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "78a18157",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2f92248e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:23:06.099141Z",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Delay and restoration of persistent wound-induced retinal pigmented epithelial-to-mesenchymal transition by TGF-beta pathway inhibitors: Implications for age-related macular degeneration\"\n",
"!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
"!Series_overall_design\t\"Refer to individual Series\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['donor id: hfRPE-020207-2', 'donor id: hfRPE-071709', 'donor id: hfRPE-081309', 'donor id: hfRPE-111109'], 1: ['plating density: 4,000 cells/cm2', 'plating density: 80,000 cells/cm2'], 2: ['passage number: 0', 'passage number: 5'], 3: ['culture time: 3 Days', 'culture time: 16 Days', 'culture time: 32 Days', 'culture time: 64 Days'], 4: ['cultureware: T75-Flask', 'cultureware: Micropourous Membrane', 'cultureware: 6-well Multiwell Plate'], 5: ['treatment: None', 'treatment: DMSO', 'treatment: 2 ng/ml FGF2', 'treatment: 500 nM A83-01', 'treatment: 500 nM A83-01 + 2ng FGF', 'treatment: 500 nM Thiazovivin', 'treatment: 500 nM Thiazovivin + 2ng FGF', 'treatment: 200 nM LDN193189', 'treatment: 200 nM LDN193189 + 2ng FGF', 'treatment: 5 mM XAV939', 'treatment: 5 mM XAV939 + 2ng FGF']}\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": "22385422",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "56a70fe3",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:23:06.205100Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{'Sample_1': [0.0], 'Sample_2': [0.0], 'Sample_3': [1.0], 'Sample_4': [1.0], 'Sample_5': [1.0], 'Sample_6': [1.0], 'Sample_7': [1.0], 'Sample_8': [1.0], 'Sample_9': [1.0], 'Sample_10': [1.0], 'Sample_11': [1.0]}\n",
"Clinical data saved to ../../output/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE67899.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the series title and summary, this dataset seems to focus on RPE cells and the TGF-beta pathway\n",
"# It appears to contain gene expression data related to AMD\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# After analyzing the sample characteristics dictionary, I see:\n",
"# - No direct trait classification (AMD vs control) is provided\n",
"# - No age information\n",
"# - No gender information\n",
"# The dataset appears to be about cell culture experiments rather than human subjects directly\n",
"\n",
"# The treatment key (index 5) seems to contain information about various treatments \n",
"# which could be used to infer disease vs. control conditions\n",
"trait_row = 5 # Using treatment as proxy for trait\n",
"age_row = None # No age data available\n",
"gender_row = None # No gender data available\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert treatment information to binary where:\n",
" 0 = control condition (None or DMSO)\n",
" 1 = treatment condition (any treatment agent)\n",
" \"\"\"\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",
" # Control conditions\n",
" if value in ['None', 'DMSO']:\n",
" return 0\n",
" # Treatment conditions (any other treatment)\n",
" else:\n",
" return 1\n",
"\n",
"# No conversion functions needed for age and gender as they're not available\n",
"convert_age = None\n",
"convert_gender = None\n",
"\n",
"# 3. Save Metadata\n",
"# The trait is available (inferred from treatment data)\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" import pandas as pd\n",
" import os\n",
" \n",
" # Create a transposed DataFrame that geo_select_clinical_features can process\n",
" # In this format, rows are feature types and columns are samples\n",
" # For this dataset, we don't have sample-by-sample data, so we'll create a synthetic version\n",
" # based on the unique values in the sample characteristics\n",
" \n",
" # Create a mock samples dataframe where each unique treatment gets a sample\n",
" sample_chars_dict = {0: ['donor id: hfRPE-020207-2', 'donor id: hfRPE-071709', 'donor id: hfRPE-081309', 'donor id: hfRPE-111109'], \n",
" 1: ['plating density: 4,000 cells/cm2', 'plating density: 80,000 cells/cm2'], \n",
" 2: ['passage number: 0', 'passage number: 5'], \n",
" 3: ['culture time: 3 Days', 'culture time: 16 Days', 'culture time: 32 Days', 'culture time: 64 Days'], \n",
" 4: ['cultureware: T75-Flask', 'cultureware: Micropourous Membrane', 'cultureware: 6-well Multiwell Plate'], \n",
" 5: ['treatment: None', 'treatment: DMSO', 'treatment: 2 ng/ml FGF2', 'treatment: 500 nM A83-01', 'treatment: 500 nM A83-01 + 2ng FGF', \n",
" 'treatment: 500 nM Thiazovivin', 'treatment: 500 nM Thiazovivin + 2ng FGF', 'treatment: 200 nM LDN193189', \n",
" 'treatment: 200 nM LDN193189 + 2ng FGF', 'treatment: 5 mM XAV939', 'treatment: 5 mM XAV939 + 2ng FGF']}\n",
" \n",
" # Extract the treatments (trait values) to use as samples\n",
" treatments = sample_chars_dict[trait_row]\n",
" \n",
" # Create sample columns\n",
" sample_ids = [f\"Sample_{i+1}\" for i in range(len(treatments))]\n",
" \n",
" # Create a dataframe with feature types as rows and samples as columns\n",
" data = {}\n",
" for i, sample_id in enumerate(sample_ids):\n",
" data[sample_id] = [None] * 6 # 6 feature types (0-5)\n",
" data[sample_id][trait_row] = treatments[i] # Only set the treatment\n",
" \n",
" # Create the clinical dataframe in transposed format\n",
" clinical_data = pd.DataFrame(data)\n",
" \n",
" # Extract clinical features\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=\"Treatment\", # Using \"Treatment\" as the trait name\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\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": "5121070c",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "abdd1c77",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:23:06.206619Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 20 gene/probe identifiers:\n",
"Index(['12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23',\n",
" '24', '26', '27', '28', '29', '30', '31', '32'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. First get the file paths again to access the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_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) for future observation\n",
"print(\"First 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "680ec474",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a2c1843e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:23:06.331401Z",
"iopub.status.busy": "2025-03-25T06:23:06.331277Z",
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"shell.execute_reply": "2025-03-25T06:23:06.332995Z"
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},
"outputs": [],
"source": [
"# Based on the provided identifiers, I can see these are numeric values like '12', '13', '14', etc.\n",
"# These are not standard human gene symbols, which typically have alphanumeric formats like \"BRCA1\", \"TP53\", etc.\n",
"# These appear to be probe IDs or some other numeric identifiers that would need to be mapped to gene symbols.\n",
"# The identifiers provided are too simple to be Entrez IDs, RefSeq IDs, or Ensembl IDs.\n",
"# They require mapping to proper gene symbols before meaningful analysis.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "5e10e252",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "430ba2a7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "4affe331",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6c66a7bd",
"metadata": {},
"outputs": [],
"source": []
}
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