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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a9077b62",
"metadata": {},
"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 = \"Celiac_Disease\"\n",
"cohort = \"GSE112102\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Celiac_Disease\"\n",
"in_cohort_dir = \"../../input/GEO/Celiac_Disease/GSE112102\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Celiac_Disease/GSE112102.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Celiac_Disease/gene_data/GSE112102.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Celiac_Disease/clinical_data/GSE112102.csv\"\n",
"json_path = \"../../output/preprocess/Celiac_Disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "ea494e7c",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f33c9d95",
"metadata": {},
"outputs": [],
"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": "fc79e0b1",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c871202",
"metadata": {},
"outputs": [],
"source": [
"# 1. Analyze gene expression data availability\n",
"is_gene_available = True # Based on the Series_summary, this dataset contains gene expression data\n",
"\n",
"# 2. Analyze variable availability and conversion functions\n",
"# 2.1 Identify rows for trait, age, and gender\n",
"trait_row = 1 # The trait information is in row 1 (group: CeD, control, FDR)\n",
"age_row = 2 # Age information is in row 2\n",
"gender_row = 4 # Gender information is in row 4\n",
"\n",
"# 2.2 Define conversion functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait values to binary (1 for CeD, 0 for control, None for FDR)\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" if value.lower() == 'ced':\n",
" return 1 # Celiac Disease\n",
" elif value.lower() == 'control':\n",
" return 0 # Control\n",
" else:\n",
" return None # FDR (First Degree Relatives) are neither cases nor controls\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age values to continuous numeric values\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender values to binary (0 for Female, 1 for Male)\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" if value.lower() == 'female':\n",
" return 0\n",
" elif value.lower() == 'male':\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save metadata for initial filtering\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. Extract clinical features if trait data is available\n",
"if trait_row is not None:\n",
" # Get the clinical data by filtering and processing\n",
" 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 clinical data\n",
" print(\"Preview of extracted clinical data:\")\n",
" print(preview_df(clinical_df))\n",
" \n",
" # Save clinical data to CSV file\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" 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": "a9bf6594",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0f0e6d5e",
"metadata": {},
"outputs": [],
"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": "2f7c9c58",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e69d07b",
"metadata": {},
"outputs": [],
"source": [
"# These identifiers begin with \"ILMN_\" which indicates they are Illumina probe IDs,\n",
"# specifically from an Illumina BeadArray microarray platform.\n",
"# These are not human gene symbols but rather probe identifiers that need to be\n",
"# mapped to gene symbols for biological interpretation.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "a615f808",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9bb14b3",
"metadata": {},
"outputs": [],
"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": "c119e2c7",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58e2c871",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify the columns for mapping\n",
"# From the gene annotation preview, we can see that 'ID' contains the probe identifiers (ILMN_*) \n",
"# and 'Symbol' contains the gene symbols\n",
"prob_col = 'ID' # Column with probe IDs matching the gene expression data index\n",
"gene_col = 'Symbol' # Column with gene symbols\n",
"\n",
"# 2. Create the gene mapping dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
"print(\"Gene mapping preview:\")\n",
"print(preview_df(gene_mapping))\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level data to gene-level data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(f\"Gene expression data after mapping - shape: {gene_data.shape}\")\n",
"print(\"First 5 genes in the mapped data:\")\n",
"print(gene_data.index[:5])\n",
"\n",
"# Save the gene expression data to CSV\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": "1cea372c",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "052096f6",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
"print(\"First 10 normalized gene symbols:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Save the normalized gene 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\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Load the clinical data that was saved in Step 2\n",
"clinical_data = pd.read_csv(out_clinical_data_file)\n",
"\n",
"# Convert to proper format for linking\n",
"clinical_df = pd.DataFrame()\n",
"sample_ids = clinical_data.columns\n",
"\n",
"# Create proper clinical dataframe with samples as columns and features as rows\n",
"clinical_df[trait] = clinical_data.iloc[0]\n",
"clinical_df['Age'] = clinical_data.iloc[1]\n",
"clinical_df['Gender'] = clinical_data.iloc[2]\n",
"clinical_df.index = [trait, 'Age', 'Gender']\n",
"clinical_df.columns = sample_ids\n",
"\n",
"print(f\"Clinical data shape: {clinical_df.shape}\")\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(clinical_df))\n",
"\n",
"# 3. Link clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
"print(linked_data.iloc[:5, :5])\n",
"\n",
"# Transpose linked data to have samples as rows and features as columns\n",
"linked_data = linked_data.T\n",
"\n",
"print(\"After transposing:\")\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(\"Actual column names in linked_data:\", linked_data.columns.tolist()[:10]) # Show first 10 columns\n",
"\n",
"# 4. 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",
"# 5. Check for bias in features\n",
"is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 6. 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 Celiac Disease patients and controls.\"\n",
")\n",
"\n",
"# 7. 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.\")\n"
]
},
{
"cell_type": "markdown",
"id": "d7d49f29",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a897ffd",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
"print(\"First 10 normalized gene symbols:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Save the normalized gene 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\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Load the clinical data from Step 2\n",
"clinical_data_path = out_clinical_data_file\n",
"clinical_data = pd.read_csv(clinical_data_path)\n",
"print(f\"Loaded clinical data from {clinical_data_path}\")\n",
"\n",
"# Create a proper clinical DataFrame with rows as features and columns as samples\n",
"# First, get column names from the CSV (these are the sample IDs)\n",
"sample_ids = clinical_data.columns.tolist()\n",
"# Extract and prepare the clinical features\n",
"trait_values = clinical_data.iloc[0].values\n",
"age_values = clinical_data.iloc[1].values\n",
"gender_values = clinical_data.iloc[2].values\n",
"\n",
"# Create DataFrame with correct format for linking\n",
"clinical_df = pd.DataFrame({\n",
" trait: trait_values,\n",
" 'Age': age_values,\n",
" 'Gender': gender_values\n",
"}, index=sample_ids).T\n",
"\n",
"print(f\"Clinical data shape: {clinical_df.shape}\")\n",
"print(\"Clinical data preview:\")\n",
"print(clinical_df.iloc[:, :5]) # Show first 5 columns\n",
"\n",
"# 3. Link clinical and genetic data\n",
"linked_data = pd.concat([clinical_df, gene_data], axis=0)\n",
"print(f\"Linked data shape after concatenation: {linked_data.shape}\")\n",
"\n",
"# Transpose to have samples as rows and features as columns\n",
"linked_data = linked_data.T\n",
"print(f\"Linked data shape after transpose: {linked_data.shape}\")\n",
"print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
"print(linked_data.iloc[:5, :5])\n",
"\n",
"# 4. 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",
"# 5. Check for bias in features\n",
"is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 6. 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 Celiac Disease patients and controls. FDR samples were excluded from the trait analysis.\"\n",
")\n",
"\n",
"# 7. 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.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|